Artificial intelligence-based bioacoustic feature sensing and behavior regulation system and method

Abstract

This invention proposes an artificial intelligence-based bioacoustic feature sensing and behavior control system and method, primarily addressing the problem that existing acoustic enticement devices, when the effective acoustic features of the target organism are unknown, can only output at a fixed frequency or preset frequency band, failing to autonomously sense, learn, and lock onto the effective acoustic features of a specific organism, resulting in poor enticement efficiency. The core of this invention lies in its ability to automatically sense and dynamically converge to the optimal acoustic enticement parameters through a closed-loop self-learning mechanism, without the need for a pre-established database of target organism acoustic features.

Claims

1. A bioacoustic feature sensing and behavior control system based on artificial intelligence, characterized in that it comprises: a sound wave output module having a power amplification unit and a sound wave transducer, the power amplification unit being used to amplify and output at least one sound wave parameter and drive the sound wave transducer; a biosensing module being used to sense the behavioral response of a target organism to one of the sound wave parameters; a microprocessor module being electrically connected to the sound wave output module and the biosensing module, the microprocessor module having a built-in artificial intelligence decision-making unit, the artificial intelligence decision-making unit being able to automatically adjust the sound wave parameter according to the behavioral response, forming a closed-loop adaptive control architecture; and an environmental sensing module being electrically connected to the microprocessor module, the environmental sensing module being used to sense at least one environmental parameter and provide context data for the artificial intelligence decision-making unit to adjust the sound wave output strategy of the sound wave parameter using the context data; wherein... The artificial intelligence decision-making unit adjusts the acoustic parameters based on the behavioral response of the target organism sensed by the biosensing module, and establishes a self-adjusting model. This self-adjusting model can automatically search, learn, and converge to the acoustic characteristics that effectively induce or intervene in the target organism.

2. The bioacoustic feature sensing and behavior control system as described in claim 1, wherein the adaptive control architecture includes a selection strategy for receiving the behavioral response sensed by the biosensing module, calculating a corresponding benefit or a reward value, and updating the acoustic parameters based on the corresponding benefit or the reward value, thereby forming a closed-loop operation that includes sensing, evaluation, and adjustment.

3. The bioacoustic feature sensing and behavior regulation system as described in claim 1, wherein the acoustic transducer is a moving coil horn, a piezoelectric loudspeaker, or a magnetostrictive transducer.

4. The bioacoustic feature sensing and behavior control system as described in claim 1, wherein the biosensing module is one of an optical sensor, an image sensor, or an acoustic sensor, or any combination thereof.

5. The bioacoustic feature sensing and behavior regulation system as described in claim 1, wherein the environmental sensing module is used to sense an environmental parameter including ambient illumination, temperature and humidity; the artificial intelligence decision-making unit adjusts the exploration rate, frequency candidate weight, output intensity and dynamic sound wave output strategy according to the environmental parameter, so that the artificial intelligence-based bioacoustic feature sensing and behavior regulation system maintains a stable induction or intervention effect under different environmental conditions.

6. The bioacoustic feature sensing and behavior control system as described in claim 1, wherein the artificial intelligence decision-making unit further executes a reinforcement learning algorithm to dynamically balance exploration and utilization in order to gradually converge to the optimal output parameters.

7. The bioacoustic feature sensing and behavior regulation system as described in claim 6, wherein the reinforcement learning algorithm uses a weighted temporal decay update rule to adapt to non-steady-state environments.

8. The bioacoustic feature sensing and behavior control system as described in claim 6, wherein the artificial intelligence decision-making unit adopts an adaptive algorithm based on behavioral feedback, including a multi-armed slot machine model, a reinforcement learning model, or a decision-making model equivalent to the aforementioned models.

9. A method for bioacoustic feature sensing and behavior regulation, applied to a bioacoustic feature sensing and behavior regulation system as described in any one of claims 1 to 8, characterized in that the method comprises the following steps: activating an adaptive acoustic sensing mode of a microprocessor module of the bioacoustic feature sensing and behavior regulation system, and an acoustic wave output module electrically connected to the microprocessor module outputting an acoustic wave sequence varying within a preset frequency range; a biosensing module of the bioacoustic feature sensing and behavior regulation system synchronously collecting behavioral feedback from a target organism on the acoustic wave sequence and quantifying it into a response index; analyzing the correlation between a parameter of the acoustic wave sequence output and the response index through an artificial intelligence module of the bioacoustic feature sensing and behavior regulation system, and calculating a reward value; dynamically adjusting the parameter based on the reward value; determining whether the response index or the reward value has reached a preset convergence condition, and if so, locking the parameter and the acoustic wave output module continuously outputting the acoustic wave sequence.

10. The bioacoustic feature sensing and behavior control method as described in claim 9, wherein after the artificial intelligence module dynamically adjusts the parameter according to the reward value, it further includes a step: an environmental sensing module of the bioacoustic feature sensing and behavior control system senses an environmental parameter and provides the artificial intelligence module with analysis and decision-making based on the participation of the environmental parameter.

11. The bioacoustic feature sensing and behavior control method as described in claim 9, wherein the step of dynamically adjusting the parameter adopts an ε-greedy strategy.

12. The bioacoustic feature sensing and behavior control method as described in claim 9, wherein the preset convergence condition is that the response index exceeds a preset threshold value multiple times consecutively.

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