The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information dataprocessing. Combining meteorological data and water qualitymonitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learningalgorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.
The invention discloses a charging load prediction method and device based on time sequencedecomposition and electronic equipment, belongs to the technical field of load prediction, and aims to solve the problem that a traditional prediction method is difficult to effectively capture a complex seasonal rule of charging of an electric vehicle. The method comprises the following steps: acquiring historical load data, wherein the historical load data comprises a load value and meteorological environment information in a preset period; a prior feature and a basic feature are obtained based on the historical load data, the basic feature comprises at least one of a time index feature and a meteorological environment feature, and the prior feature is obtained by decomposing a time sequence feature of the historical load data by a Prophet model; fusing the prior features and the basic features to obtain an input feature vector; and inputting the input feature vector into a pre-trained prediction module for load prediction to obtain a prediction result. According to the embodiment of the invention, the accuracy of the charging load prediction result can be improved.
This utility model discloses a printing engravingmachine with a lifting platform, relating to the field of engravingmachine technology. It includes a worktable, a support platform, an adjustment mechanism, and a lifting mechanism. The engravingmachine body is slidably mounted on the worktable. The support platform is located inside the worktable. The adjustment mechanism is located on the support platform for adjusting the angle of the support platform. The lifting mechanism is located inside the worktable for adjusting the height of the support platform. This utility model utilizes the coordinated arrangement of the support platform, adjustment mechanism, and lifting mechanism. The adjustment mechanism can adjust the tilt angle of the support platform, ensuring that the complex curved surfaces or sides of irregularly shaped workpieces are always in the optimal engraving position, eliminating the need for repeated disassembly and adjustment, thus significantly improving processing efficiency. The lifting mechanism can fine-tune the height of the support platform, ensuring that workpieces of different thicknesses can be stably fixed. It retains the stability of a traditional flat worktable while providing flexible reversibility, making it easy to use.
The application discloses a kind of intelligent text creation product integration printing delivery management method, including, obtain historical sales and social media multi-source data, generate sales forecast through adaptive weighted prediction model;The forecast is converted into atomization task with real-time order, and dynamic scheduling is carried out in digital twin environment using multi-objective optimization algorithm and objective entropy weight decision, to minimize line loss;After printing, actual image is mapped to latent space using GAN network for comparison quality inspection, and partial derivative is solved through proxy model to compensate bottom process parameters in reverse;Quality inspection is qualified, which automatically triggers coding and packaging, combines dynamic weight distribution optimal logistics company, and plans optimal collection path based on improved ant colony algorithm.The application breaks the information barrier of manufacturing and delivery chain, realizes the integrated collaborative management of high robustness prediction, flexible production scheduling, quality inspection self-healing and zero waiting delivery of text creation products.
The application relates to the technical field of computer system evaluation, and particularly discloses an evolvable and reliable cloud level calculation system and method based on game optimization; the method comprises the following steps: in the first stage, a pair comparison matrix is established based on the theory of trusted computingdependency tree and the analytic hierarchy process to generate a static weight vector; in the second stage, multi-source threat data is collected through an AI model, a double comparison matrix is constructed through double-dimension labeling classification, and a dynamic weight vector is output; through game theory, an antagonistic pair of core security mechanisms is identified, an antagonistic gradient and the dynamic weight vector are calculated, a weight convergence interval and a level promotion upper bound are predicted, and an optimal weight evolution path is generated; the system is used for realizing an evolvable and reliable cloud level calculation method based on game optimization; and the application is favorable for improving the adaptability and predictability of cloud security evaluation and providing a decision basis for cloud service security optimization.
This invention relates to a Bluetoothheadset with dual listening modes, comprising a control unit, a stand, an over-ear headset, a storage drive unit, and an in-ear headset with an ear wire and an ear tip. The over-ear headset is mounted on the stand, and both the control unit and the storage drive unit are installed in the over-ear headset. The control unit is communicatively connected to both the over-ear headset and the in-ear headset. The ear wire is installed in the storage drive unit, with one end of the ear wire passing through the over-ear headset and connecting to the ear tip. The ear tip protrudes from the over-ear headset. Listening can be performed directly using the over-ear headset; alternatively, listening can be performed by pulling the ear tip to extend the ear wire from the storage drive unit, which then locks the current position. To remove the ear wire, the storage drive unit retracts the ear wire. It features a headset, a storage driver unit, and an in-ear headset. The storage driver unit and in-ear headset are integrated into the headset, achieving a dual-mode integrated design. The headband and neckband modes can be switched simply by extending or retracting the ear cable. This design retains the sound quality advantages and wearing stability of over-ear headphones while also providing the portability of neckband headphones.