Abstract: The automatic and accurate estimation method for gross primary productivity of a forest ecosystem based on a remote sensing technology includes: the real-time position signals of a remote sensing collection device is acquired, the forest map is acquired and the real-time position signals is displayed on the map; the meteorological parameters and forest characteristic parameters of the forest are collected by a remote sensing collection device, and the preliminary estimation on the GPP of the forest ecosystem is performed by a ground estimation model; the geomorphological features of the map are collected, it is judged whether to correct the preliminary estimation result according to the geomorphological features, and if the correction is needed, the preliminary estimation result is adjusted; and the adjusted estimation result is stored, and the dynamic fluctuation chart of the adjusted estimation result is displayed on a visual device.

Abstract: Embodiments of the disclosure provide a recognition method, apparatus, device, and storage medium and relates to the field of artificial intelligence technology. The method includes obtaining a second decision-making threshold of a feature-matching model in a target scenario by joint testing of the feature-matching model and auxiliary detection model. The method takes into full consideration the mutual influence between different algorithm models in a scenario when multiple algorithm models are used for facial recognition. Compared to manually setting a decision-making threshold for each algorithm model independently, the methods in the disclosure are more adaptable to changing scenarios and scenarios with multiple models used in facial recognition. This improves the accuracy and efficiency of the obtained decision-making thresholds, thereby enhancing the accuracy of multi-model facial recognition.

Abstract: The present application discloses a method, an apparatus, and a device for updating a feature vector database, and a medium. The method includes: acquiring a first biological feature in a service request; obtaining, according to the first biological feature, a first feature vector and a second feature vector respectively through a first algorithm model and a second algorithm model, in which a first feature vector database include sample feature vectors obtained based on the first algorithm model; performing validity verification on the second feature vector according to an associated feature vector for a first user corresponding to a first sample feature vector; and obtaining, under a condition that the validity verification on the second feature vector passes, a second sample feature vector for the first user based on the second feature vector, and storing the second sample feature vector in a second feature vector database.

Abstract: The present application discloses a method, an apparatus, and a device for updating a feature vector database, and a medium. The method includes: acquiring a first biological feature in a service request; obtaining, according to the first biological feature, a first feature vector and a second feature vector respectively through a first algorithm model and a second algorithm model, in which a first feature vector database include sample feature vectors obtained based on the first algorithm model; performing validity verification on the second feature vector according to an associated feature vector for a first user corresponding to a first sample feature vector; and obtaining, under a condition that the validity verification on the second feature vector passes, a second sample feature vector for the first user based on the second feature vector, and storing the second sample feature vector in a second feature vector database.

Abstract: Embodiments of the disclosure provide a recognition method, apparatus, device, and storage medium and relates to the field of artificial intelligence technology. The method includes obtaining a second decision-making threshold of a feature-matching model in a target scenario by joint testing of the feature-matching model and auxiliary detection model. The method takes into full consideration the mutual influence between different algorithm models in a scenario when multiple algorithm models are used for facial recognition. Compared to manually setting a decision-making threshold for each algorithm model independently, the methods in the disclosure are more adaptable to changing scenarios and scenarios with multiple models used in facial recognition. This improves the accuracy and efficiency of the obtained decision-making thresholds, thereby enhancing the accuracy of multi-model facial recognition.