7results about How to "Improve regional adaptability" patented technology

The invention provides a metabolic disease diabetic foot test data early screening method and system. Belongs to the technical field of medical health. The method comprises the following steps: acquiring clinical sample data, transmitting the acquired clinical sample data to a cloud space for preprocessing, performing feature extraction on the preprocessed clinical sample data through a deep learning algorithm, and extracting features related to the metabolic disease diabetic foot; constructing a metabolic disease diabetic foot screening model based on blood routine data through a deep learning algorithm, and combining obtained clinical sample data with historical data to construct a data set; the constructed metabolic disease diabetic foot screening model is evaluated through multiple indexes, and the metabolic disease diabetic foot screening model is optimized based on the evaluation result. Feature extraction is carried out through a deep learning algorithm, complex modes and subtle changes related to the metabolic disease diabetic foot can be recognized, and the screening accuracy is improved.

This invention discloses an adaptive scheduling method, system, and medium for distribution networks under disturbances caused by renewable energy grid connection. It relates to the technical field of distribution networks and includes: real-time data collection from renewable energy grid connection points to obtain a multi-source sensing dataset for disturbance event identification, resulting in multiple disturbance events; dynamic partitioning of the distribution network based on disturbance type labels and spatiotemporal characteristics to determine multiple autonomous control areas; distributed collaborative optimization using a regional dynamic equivalent model to generate multiple local scheduling strategies; and integration of these local scheduling strategies for global coordination and verification to generate and execute adaptive scheduling instructions for the distribution network. This invention solves the technical problems of delayed response to renewable energy grid connection disturbances, poor efficiency of centralized control, and difficulty in balancing local autonomy and global coordination in existing technologies. It achieves the technical effects of improving the distribution network's rapid response capability to disturbance events, the regional adaptability of scheduling strategies, and the overall operational safety and reliability.

The invention discloses a digital pole-mounted switch, and relates to the field of outdoor overhead power distribution appliances, the digital pole-mounted switch comprises a box body and a pole-type sensor, the box body comprises a box cover consistent with the protection grade of the box body, the pole-type sensor adopts an APG integrated sealing technology, and a current sensor, a voltage sensor, a voltage sensor, a voltage sensor and a power source are arranged in the box body. The box body, the box cover and the pole type sensor are connected through a fastener to form a sealing structure, an energy storage handle is pulled, an energy storage spring in a transmission piece begins to store energy, a pointer is rotated to the position where energy is stored, after energy storage is completed, a closing indication ring in an opening and closing handle is pulled, at the moment, the energy storage spring releases energy, and then the opening and closing handle is opened. The output shaft of the pushing mechanism rotates and drives the switch to be switched on through transmission assemblies such as a crank arm and a connecting rod, and meanwhile, a switching-on pointer points to a switching-on position, so that functional modules can be flexibly selected, and the regional adaptability of products is improved.

This invention discloses a method for calculating the installed capacity of distributed generation (DG) in a distribution area based on the time-series power of distribution transformers. The method includes: classifying the target distribution area by DG type; matching the DG type classification results with corresponding time period filtering principles; filtering the corresponding pure load time periods according to the time period filtering principles; obtaining pure load time period data; classifying the pure load time period data by date type; extracting the load cycle features corresponding to each date type; fitting the load cycle features using a pre-trained LSTM neural network model; outputting the load prediction value corresponding to the pure load time period; calculating the DG output at the corresponding time based on the load prediction value; filtering the maximum DG output value for the pure load time period; assigning dynamic coefficients to the maximum DG output value; and outputting the actual installed capacity of the target distribution area. This method improves the accuracy of DG installed capacity calculation, enhances data robustness, is applicable to a wider range of application scenarios, and is low-cost and easy to implement.

The application discloses a soil heavy metal pollution ecological risk identification method and system, relates to the technical field of soil pollution identification, and couples a module to construct a dynamic model of a physical field and fuse to obtain a coupling model, takes output of the coupling model as dynamic coupling data; an optimization module constructs a double-layer optimization framework of upper-layer space layout parameter screening and lower-layer strength judgment parameter optimization based on the coupling model output, adopts a probability agent model in iteration of the double-layer optimization framework, constructs a Pareto front solution set to screen candidate schemes based on multiple groups of risk identification schemes generated by iteration, and outputs a soil heavy metal pollution ecological risk dynamic identification atlas and hierarchical management and control suggestions in combination with a dynamic checking mechanism. The identification system can output the soil heavy metal pollution ecological risk dynamic identification atlas and the hierarchical management and control suggestions, not only realizes accurate positioning and hierarchical management of risks in space, but also achieves balance between ecological safety guarantee and economic cost control at a decision-making level.

The application provides a regional pest and disease collaborative identification method and system based on privacy federated learning. The method comprises: acquiring pest and disease observation data collected by each regional local node, and performing standardization processing and privacy classification on the pest and disease observation data in the local node; performing privacy protection processing on the local model update quantity and the local pest and disease phenotype prototype according to the privacy constraint parameter; dividing each local node according to the regional ecological domain representation, and generating a shared identification model, regional adaptation parameters and a cross-regional pest and disease prototype library; performing collaborative correction on the shared identification model, the regional adaptation parameters and the cross-regional pest and disease prototype library to obtain a target collaborative identification model; inputting current pest and disease observation data of a target region into the target collaborative identification model to generate a regional pest and disease collaborative identification result. The application can improve the regional adaptation capability, balance privacy protection and identification accuracy, and improve the collaborative identification credibility.

The present invention discloses a method for complex surface processing area decomposition and process parameter optimization for multiple processes, and belongs to the technical field of robot processing of complex surface workpieces. The method first performs parameterization and discretization processing on the surface model of the complex surface, decomposes the complex surface by clustering partitions and defining boundaries to obtain multiple units, and then calculates the process operation complexity in combination with the geometric complexity of the workpiece surface and related process parameters, and adjusts the complex surface decomposition according to the process operation complexity of each unit, analyzes the influence of process parameters on operation efficiency and optimizes the process parameters in each unit, and finally completes the process adaptive unit decomposition of the complex surface, and obtains the most efficient process parameter combination, which lays a good foundation for improving the quality and efficiency of complex surface processing.