A diversity biological collection recognition system

Abstract

The application provides a diversity biological collection and identification system, a high-definition camera and a microphone are mounted on a UAV platform, a plurality of groups of biological species and living habits are included in a database, the biological species and the living habits are in one-to-one correspondence, the living habits include sound characteristics, living traces, habitats and the like; a habit analysis module is used for obtaining animal traces in an image to be analyzed in an image analysis technology, a habit influence value is obtained by calculating the living trace reliability of all marked animal traces; a sound recognition module is used for obtaining animal calls or activity sounds in sound data as audio segments to be analyzed, a sound influence value is obtained by calculating the sound similarity of the marked audio to be analyzed; a comprehensive evaluation module is used for obtaining the number of biological samples to be analyzed, the habit influence value and the sound influence value, and obtaining the expected number of biological samples in a specified area by using a statistical algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of biometric collection, and more particularly to a diversity biometric collection and recognition system. Background Art

[0002] The purpose of collecting biodiversity is to fully understand the basic conditions of biodiversity such as distribution, quantity, and species. It is of great significance to maintaining the balance of the ecosystem, ensuring food security and human survival. For areas that are inconvenient for people to enter, such as forests and wild areas, collection is mainly carried out through drones. In the process of biological quantity statistics, the main basis is currently the number of organisms photographed by drones. For organisms that are not photographed, the number cannot be counted. During the biological collection process, the statistical deviation of the number of organisms is large. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a diverse biometric collection and identification system to overcome the above-mentioned shortcomings in the existing technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A diverse biological collection and identification system includes an unmanned aerial vehicle (UAV) platform equipped with a high-definition camera and a microphone, and an identification system comprising a database, a data collection module, an image analysis module, a habit analysis module, a data storage module, a sound recognition module, and a comprehensive evaluation module.

[0006] The database includes several groups of biological species and living habits, wherein the biological species correspond to the living habits one by one, and the living habits include sound characteristics, living traces, habitats, etc.;

[0007] The data acquisition module is used to control the UAV to collect image data and sound data in a specified area according to a preset flight strategy;

[0008] The image analysis module is used to acquire image data in real time as an image to be analyzed, determine the organisms to be analyzed, and obtain the number of organisms to be analyzed in the image to be analyzed by image recognition technology;

[0009] The habit analysis module is used to obtain animal traces in the image to be analyzed in the image analysis technology, and compare all animal traces with the life traces of the creature to be analyzed, respectively, to obtain the reliability of the life traces, and preset a reliability threshold. If the life trace reliability is greater than the reliability threshold, the animal trace is marked, and the habit influence value is calculated based on the life trace reliability of all marked animal traces;

[0010] The sound recognition module is used to obtain animal calls or activity sounds in the sound data as audio segments to be analyzed, compare all the audio segments to be analyzed with the sound characteristics of the creature to be analyzed, and obtain sound similarity. A similarity threshold is preset. If the sound similarity is greater than the similarity threshold, the audio to be analyzed is marked, and the sound influence value is calculated based on the sound similarity of the marked audio to be analyzed;

[0011] The comprehensive evaluation module is used to obtain the number of organisms to be analyzed, the influence value of habits and the influence value of sound, and obtain the estimated number of organisms to be analyzed in the specified area through statistical algorithms;

[0012] The data storage module is used to obtain and store the types and quantities of organisms to be analyzed, and to construct an organism distribution model according to the distribution of the organisms to be analyzed.

[0013] Preferably, the living traces include activity paths, nest locations, foraging areas, etc.

[0014] Preferably, the habit analysis module is provided with a scratch analysis unit, which is used to obtain scratch image information in the image to be analyzed and compare the scratch image information with the life traces in the database.

[0015] Preferably, a nest recognition unit is provided in the habit analysis module, and a target detection model is preset in the nest recognition unit. The target detection module is used to analyze and identify the image to be analyzed, obtain the target point, and obtain the nest credibility, and a credibility threshold is preset. If the nest credibility is greater than the credibility threshold, the habit impact value is calculated based on the living habit reliability and the nest credibility.

[0016] Preferably, the habit analysis module is further provided with an identification calibration unit. When the nest credibility is less than the credibility threshold, the identification calibration unit is used to obtain the nest credibility and compare it with the minimum threshold in the identification calibration unit. If the nest credibility is greater than the minimum threshold, the target point is obtained and an execution command is generated. The drone is provided with a blowing component and also includes an execution module. The execution module is used to obtain the execution command and target point in the identification calibration unit, and control the drone to move to the target point, and drive the blowing component to move, so as to move the obstruction on the surface of the nest and control the nest identification unit to re-identify.

[0017] Preferably, the sound recognition module is further provided with a sound simulation unit, in which the audio information of the organism to be analyzed is preset. When the current volume in the sound data is too low, the sound simulation unit can be used to randomly play the audio information of the organism to be analyzed.

[0018] Preferably, the sound recognition module is also preset with an environment judgment unit, which is used to collect environmental characteristics, including sampling time, ambient temperature and ambient humidity, and obtain the habitat of the organism to be analyzed, obtain standard characteristics based on the habitat, and the standard characteristics reflect the habitat suitable for the life of the organism to be analyzed, compare the environmental characteristics with the standard characteristics, and generate a judgment value, and obtain the sound impact value through the judgment value and sound similarity.

[0019] Preferably, a flight control module is also included, which is used to obtain the living habits of the organisms to be analyzed and dynamically adjust the flight path of the drone based on the image data collected in real time.

[0020] Preferably, the determination includes obtaining a moving object in the image to be analyzed as a feature object to be analyzed, obtaining a type of the feature object to be analyzed based on image recognition technology, and comparing the type of the feature object to be analyzed with the type of the organism to be analyzed in the data storage module; if the type of the feature object to be analyzed is the same as the type of the organism to be analyzed in the data storage module, re-determining the organism to be analyzed; if the type of the feature object to be analyzed is the same as the type of the organism to be analyzed in the data storage module, marking the feature object as the organism to be analyzed.

[0021] The beneficial effects of the present invention are as follows: using a drone as a carrier and formulating a targeted flight strategy according to the living habits of the organisms to be analyzed, so as to fully detect the common activity range of the organisms to be analyzed; using a habit analysis module, the animal traces in the image to be analyzed are compared with the living traces of the organisms in the database, and the reliability of the living traces is evaluated, so as to calculate the habit impact value. Through habit analysis, a more comprehensive understanding of the living habits and activity range of the organisms to be analyzed can be obtained; and through a sound recognition module, the animal calls or activity sounds in the sound data are analyzed and compared with the sound characteristics of the organisms in the database, so as to obtain the sound impact value; by obtaining the number of organisms to be analyzed captured by the image, the sound impact value and the habit impact value, a comprehensive analysis is performed to obtain the expected number of organisms to be analyzed, thereby improving the accuracy of the number during the biological collection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the overall structural diagram of the present invention. DETAILED DESCRIPTION

[0023] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments derived by persons of ordinary skill in the art without inventive effort are within the scope of protection of the present invention. It should be noted that when a component is referred to as being "fixed to" another component, it can be directly attached to the other component or there can be a central component. When a component is referred to as being "connected to" another component, it can be directly attached to the other component or there can be a central component. When a component is referred to as being "disposed on" another component, it can be directly disposed on the other component or there can be a central component. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only. Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by persons skilled in the art to which this invention relates. The terms used in the present description are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0025] like Figure 1 As shown, the present invention provides a diverse biological collection and identification system, characterized by comprising an unmanned aerial vehicle platform equipped with a high-definition camera and a microphone. The high-definition camera is used to capture real-time images of the forest, and the microphone is used to collect forest sounds. At the same time, the microphone can be used to play sounds to attract biological species. The system also includes an identification system, which includes a database, a data collection module, an image analysis module, a habit analysis module, a data storage module, a sound recognition module, and a comprehensive evaluation module.

[0026] The database includes several groups of species and their living habits, with species corresponding to their living habits. Living habits include sound characteristics, living traces, and habitats. Living traces include activity paths, nest locations, and foraging areas. Habitats include the temperature, humidity, and activity times that are suitable and preferred by the species.

[0027] The data acquisition module is used to control the UAV to collect image data and sound data in a specified area according to a preset flight strategy. The data acquisition module is used to control the UAV to implement a specified flight strategy and collect data within the specified area. Different flight strategies are adjusted according to different animal living habits and environmental information. The flight strategy includes the adjustment of parameters such as flight path, altitude, and speed, and controls the UAV to fly stably in the specified area, including the execution of flight actions such as hovering, straight flight, turning, and landing. Through the preset flight strategy, every corner of the specified area can be more comprehensively covered, thereby collecting more comprehensive and detailed information.

[0028] The image analysis module is used to acquire image data in real time as images to be analyzed, identify the organisms to be analyzed, and obtain the number of organisms to be analyzed in the images to be analyzed through image recognition technology. The image analysis module receives image data collected by drones to ensure the timeliness of data collection and analysis, and pre-processes the acquired image data to obtain images to be analyzed. Based on computer vision and machine learning algorithms, it extracts features and classifies the images to be analyzed, determines the species of organisms to be analyzed in the images, and further counts the number of organisms to be analyzed based on the identified species.

[0029] It also includes a flight control module, which is used to obtain the living habits of the organisms to be analyzed and dynamically adjust the flight path of the drone based on the image data collected in real time. By obtaining the living habits of the organisms to be analyzed and based on the image data collected in real time, the flight path of the drone is dynamically adjusted.

[0030] The habit analysis module is used to obtain animal traces in the image to be analyzed in the image analysis technology. Animal traces include footprints, feces, nests, etc., and compare all animal traces with the life traces of the organism to be analyzed. The database contains the life trace samples and feature descriptions of the organism to be analyzed, and performs accurate matching. Based on the comparison results, the reliability of life traces of different animal traces is calculated. The reliability of life traces involves the sum of multiple factors such as matching degree, trace freshness and environmental factors. Through the reliability of life traces, the existence and distribution of the organism to be analyzed in the specified area can be further determined, which is convenient for the ecological analysis of the organism to be analyzed. Detection, and the reliability of life traces reflects the activity level of organisms in the area, which helps to understand the ecological habits and behavioral patterns of organisms, facilitates the demarcation of protected areas for them, and specifies protection measures; and a reliability threshold is preset. If the reliability of life traces is greater than the reliability threshold, the animal traces are marked, and the habit impact value is calculated through the reliability of life traces of all marked animal traces; by comparing the reliability of life traces with the reliability threshold, some animal traces that are not the noise of the organism are screened, and the life traces that may be produced by the organism to be analyzed are retained, and the habit impact value of the specified area is calculated through the reliability of all marked life traces;

[0031] The habit analysis module is provided with a scratch analysis unit, which is used to obtain scratch image information in the image to be analyzed and compare the scratch image information with the life traces in the database.

[0032] The habit analysis module includes a nest recognition unit equipped with a pre-set target detection model. This unit uses the target detection module to analyze and identify the image to be analyzed, identifying target points and determining nest credibility. A pre-set credibility threshold is used. If the nest credibility exceeds the threshold, a habit influence value is calculated based on the habit credibility and nest credibility. The target detection model, such as a convolutional neural network model based on deep learning, has been trained with a large amount of labeled data to rapidly identify nest features of the organisms being analyzed in the image. After drone-generated image data, the animal nest recognition unit uses the target detection model to identify the image to be analyzed. By extracting feature information from the image and comparing it with a pre-trained feature library, the unit determines the presence of a nest and determines its target point, i.e., the specific location of the nest. During the recognition process, nest feasibility is calculated based on the nest's clarity, completeness, and feature matching. A pre-set credibility threshold is used to determine the reliability of the recognition result. If the credibility exceeds the threshold, the recognition result is considered reliable; if it is less than the threshold, further processing or calibration is required.

[0033] The habit analysis module also includes an identification calibration unit. When the nest credibility is less than the credibility threshold, the identification calibration unit intervenes to improve the accuracy of the identification. The identification calibration unit is used to obtain the nest credibility and compare it with the minimum threshold within the identification calibration unit. If the nest credibility is greater than the minimum threshold, the target point is obtained and an execution command is generated. The identification calibration unit has a minimum threshold for determining whether further processing is required. If the animal nest credibility is greater than the minimum threshold but less than the credibility threshold, it means that although the identification result is not reliable, it can still be improved through calibration. In other words, there may be obstructions on the nest surface that affect the judgment. The drone is equipped with an air blowing component and also includes an execution module. The execution module is used to obtain the execution command and target point in the identification calibration unit, control the drone to move to the target point, and drive the air blowing component to move to remove the obstruction on the nest surface and control the nest identification unit to re-identify. The execution module controls the drone to move to the target point (i.e., the nest location) and drives the air blowing component on the drone to move. The blowing component attempts to remove obstructions (such as fallen leaves, weeds, etc.) on the surface of the nest by blowing air, so as to more clearly expose the characteristics of the nest; after the blowing operation is completed, the animal nest recognition unit re-identifies the target point and recalculates the credibility of the animal nest.

[0034] The sound recognition module is used to obtain animal calls or activity sounds in the sound data as audio segments to be analyzed, compare all the audio segments to be analyzed with the sound features of the organism to be analyzed, and obtain the sound similarity. A similarity threshold is preset. If the sound similarity is greater than the similarity threshold, the audio to be analyzed is marked, and the sound impact value is calculated based on the sound similarity of the marked audio to be analyzed; each audio segment to be analyzed is compared with multiple samples in the sound feature library of the organism to be analyzed, and the degree of match between the two is evaluated by calculating the similarity of audio features (such as frequency, pitch, length, timbre, etc.); the preset similarity threshold is used to determine the similarity between the audio segment and the organism to be analyzed The method is to determine whether the similarity between the sound characteristics of the analyzed organism is high enough to identify the audio clip as the call or activity sound of the organism; each audio clip to be analyzed will be compared with multiple samples in the sound characteristic library of the organism to be analyzed, and the degree of match between the two will be evaluated by calculating the similarity of audio characteristics (such as frequency, pitch, length, timbre, etc.); the preset similarity threshold is used to determine whether the similarity between the audio clip and the sound characteristics of the analyzed organism is high enough to identify the audio clip as the call or activity sound of the organism to be analyzed; by obtaining the sound similarity of all marked audio clips to be analyzed and obtaining the sound influence value, the sound influence value is used to obtain the possible number of organisms to be analyzed in the specified area.

[0035] The sound recognition module is also equipped with a sound simulation unit, which is preset with the audio information of the creature to be analyzed. When the current volume in the sound data is too low, the sound simulation unit can be used to randomly play the audio information of the creature to be analyzed; when the volume of the environmental figure data is detected to be too low, the sound effects can be played randomly to simulate the real calls and activity sounds of the creature to be analyzed to attract the calls of the creature to be analyzed, so as to facilitate the collection of the calls of the creature to be analyzed.

[0036] The sound recognition module also includes an environmental judgment unit. This unit is used to collect environmental characteristics, including sampling time, ambient temperature, and ambient humidity, and to determine the habitat of the organism being analyzed. Based on this habitat, it obtains standard characteristics, which reflect a suitable habitat for the organism being analyzed. The standard characteristics are then compared with the standard characteristics to generate a judgment value. The sound impact value is then determined based on the judgment value and sound similarity. The environmental judgment unit is responsible for collecting and analyzing environmental characteristics, including sampling time, ambient temperature, and ambient humidity, to assess whether the current environment is suitable for the organism being analyzed. The environmental judgment unit compares the current environmental characteristics with the standard characteristics to generate a judgment value. The high or low judgment value reflects the degree of fit between the current environment and the standard environment. Combining the judgment value with sound similarity can further refine the calculation of the sound impact value. For example, even if the sound similarity is high, if the current environment is unsuitable for the organism's survival, the sound impact value may be reduced accordingly.

[0037] The comprehensive evaluation module is used to obtain the number of organisms to be analyzed, the influence value of habits, and the influence value of sound, and obtain the expected number of organisms to be analyzed in the specified area through statistical algorithms; the comprehensive evaluation module uses a series of statistical algorithms (such as regression analysis, machine learning models, Bayesian networks, etc.) to integrate the above data sources and predict the expected number of organisms to be analyzed in the specified area; the comprehensive evaluation module uses a series of statistical algorithms (such as regression analysis, machine learning models, Bayesian networks, etc.) to integrate the above data sources and predict the expected number of organisms to be analyzed in the specified area.

[0038] The data storage module is used to acquire and store the species and number of organisms to be analyzed, and to construct a biodistribution model based on the distribution of the organisms to be analyzed. The data storage module collects the number and distribution of different organisms to be analyzed, and based on this collected data on the distribution of the organisms to be analyzed, the data storage module can construct a biodistribution model. These models typically use geographic information system (GIS) technology to combine biodistribution data with geographic spatial information to graphically display the distribution of organisms in different regions. The data storage module enables rapid retrieval, query, and analysis of data, providing the necessary data support for the comprehensive evaluation module and other related systems.

[0039] Determining the organism to be analyzed includes obtaining a moving object in the image to be analyzed as a feature object to be analyzed, obtaining a type of the feature object to be analyzed based on image recognition technology, and comparing the type of the feature object to be analyzed with the species of the organism to be analyzed in the data storage module. If the type of the feature object to be analyzed is the same as the species of the organism to be analyzed in the data storage module, the organism to be analyzed is re-determined. If the type of the feature object to be analyzed is different from the species of the organism to be analyzed in the data storage module, the feature object is marked as the organism to be analyzed. Obtaining a moving object in the image to be analyzed includes detecting and extracting all moving objects from the image as candidate feature objects to be analyzed, identifying the type of each candidate feature object using image recognition technology (such as a convolutional neural network), and comparing the type of each identified feature object with the species of the organism to be analyzed pre-stored in the data storage module. If there is a match (i.e., the type of the feature object is the same as the species of a certain organism to be analyzed), the feature object is ignored and the feature object is re-determined or marked as an analyzed feature object. If there is no match (i.e., the type of the feature object is different from all pre-stored species of the organism to be analyzed), the feature object is marked as not a organism to be analyzed.

[0040] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions based on the principles of the present invention are within the scope of protection of the present invention. It should be noted that improvements and modifications that do not depart from the principles of the present invention are within the scope of protection of the present invention.

Claims

1. A diverse biometric collection and recognition system, characterized in that: The system comprises an unmanned aerial vehicle (UAV) platform equipped with a high-definition camera and a microphone, and an identification system comprising a database, a data acquisition module, an image analysis module, a habit analysis module, a data storage module, a sound recognition module, and a comprehensive evaluation module. The database includes several groups of biological species and living habits, wherein the biological species correspond to the living habits one by one, and the living habits include sound characteristics, living traces, and habitats; The data acquisition module is used to control the UAV to collect image data and sound data in a specified area according to a preset flight strategy; The image analysis module is used to acquire image data in real time as an image to be analyzed, determine the organisms to be analyzed, and obtain the number of organisms to be analyzed in the image to be analyzed by image recognition technology; The habit analysis module is used to obtain animal traces in the image to be analyzed in the image analysis technology, and compare all animal traces with the life traces of the creature to be analyzed, respectively, to obtain the reliability of the life traces, and preset a reliability threshold. If the life trace reliability is greater than the reliability threshold, the animal trace is marked, and the habit influence value is calculated based on the life trace reliability of all marked animal traces; The habit analysis module is provided with a nest recognition unit, and the nest recognition unit is preset with a target detection module. The target detection module is used to analyze and identify the image to be analyzed, obtain the target point, and obtain the nest credibility. A credibility threshold is preset. If the nest credibility is greater than the credibility threshold, the habit influence value is calculated based on the living habit reliability and the nest credibility. The habit analysis module is further provided with an identification calibration unit. When the nest credibility is less than the credibility threshold, the identification calibration unit is used to obtain the nest credibility and compare it with the minimum threshold in the identification calibration unit. If the nest credibility is greater than the minimum threshold, the target point is obtained and an execution command is generated. The drone is provided with an air blowing component and also includes an execution module. The execution module is used to obtain the execution command and the target point in the identification calibration unit, control the drone to move to the target point, and drive the air blowing component to move, so as to move the obstruction on the surface of the nest and control the nest identification unit to re-identify; The sound recognition module is used to obtain animal calls or activity sounds in the sound data as audio segments to be analyzed, compare all the audio segments to be analyzed with the sound characteristics of the creature to be analyzed, and obtain sound similarity. A similarity threshold is preset. If the sound similarity is greater than the similarity threshold, the audio to be analyzed is marked, and the sound influence value is calculated based on the sound similarity of the marked audio to be analyzed; The comprehensive evaluation module is used to obtain the number of organisms to be analyzed, the influence value of habits and the influence value of sound, and obtain the estimated number of organisms to be analyzed in the specified area through statistical algorithms; The data storage module is used to obtain and store the types and quantities of organisms to be analyzed, and to construct an organism distribution model according to the distribution of the organisms to be analyzed.

2. A diverse biometric collection and recognition system according to claim 1, characterized in that: The living traces include activity paths, nest locations, and foraging areas.

3. The diverse biometric collection and recognition system according to claim 1, characterized in that: The habit analysis module is provided with a scratch analysis unit, which is used to obtain scratch image information in the image to be analyzed and compare the scratch image information with the life traces in the database.

4. The diverse biometric collection and recognition system according to claim 1, characterized in that: The sound recognition module is further provided with a sound simulation unit, which is preset with audio information of the organism to be analyzed. When the current volume in the sound data is too low, the sound simulation unit can be used to randomly play the audio information of the organism to be analyzed.

5. The diverse biometric collection and recognition system according to claim 1, characterized in that: The sound recognition module is also pre-set with an environmental judgment unit, which is used to collect environmental characteristics, including sampling time, ambient temperature and ambient humidity, and obtain the habitat of the organism to be analyzed, obtain standard characteristics based on the habitat, and the standard characteristics reflect the habitat suitable for the life of the organism to be analyzed. The environmental characteristics are compared with the standard characteristics, and a judgment value is generated. The sound impact value is obtained through the judgment value and the sound similarity.

6. The diverse biometric collection and recognition system according to claim 1, characterized in that: It also includes a flight control module, which is used to obtain the living habits of the organisms to be analyzed and dynamically adjust the flight path of the drone based on the image data collected in real time.

7. The diverse biometric collection and recognition system according to claim 1, characterized in that: The determining of the organism to be analyzed includes obtaining a moving object in the image to be analyzed as a feature object to be analyzed, obtaining a type of the feature object to be analyzed based on image recognition technology, and comparing the type of the feature object to be analyzed with the type of the organism to be analyzed in the data storage module. If the type of the feature object to be analyzed is the same as the type of the organism to be analyzed in the data storage module, the organism to be analyzed is re-determined. If the type of the feature object to be analyzed is different from the type of the organism to be analyzed in the data storage module, the feature object is marked as the organism to be analyzed.

Images