15results about How to "Reduce blindness" patented technology

The application is named reservoir scale intelligent optimization demonstration system and method based on multi-target risk coupling, and belongs to the technical field of intelligent decision-making of water conservancy and hydropower engineering. The technical problem to be solved is that the traditional reservoir scale demonstration relies on static scenarios, cannot quantize the multi-target conflict coupling relationship, does not systematically consider multiple uncertainties and risks, has low optimization efficiency and insufficient scheme robustness. The technical solution points are that the application sets six modules of hydrological risk modeling, multi-target coupling analysis, scale optimization decision, uncertainty propagation simulation, comprehensive resilience evaluation and intelligent demonstration integration, generates dynamic hydrological risk indexes by fusing climate scenarios and historical data, quantizes the multi-target coupling relationship, optimizes the reservoir scale parameters by combining reinforcement learning and risk constraint, and automatically generates the optimal reservoir scale demonstration scheme with high robustness through uncertainty simulation and resilience evaluation.

This application provides a core-shell structure microwave absorbing material and its thickness optimization design method and application, relating to the field of electromagnetic wave absorbing materials technology. The core-shell structure microwave absorbing material comprises a sheet-like magnetic metal core and a silica shell covering the core, the silica shell being prepared via a sol-gel method. The optimization design method includes: obtaining the intrinsic electromagnetic parameters of the constituent materials; constructing a single-particle simulation model with shell thickness as the variable; calibrating the simulation parameters through a decoupling-testing-verification iterative process; optimizing the solution for the optimal thickness based on the calibration model with the goal of maximizing the effective absorption bandwidth of reflection loss; and finally determining the optimal thickness range through experimental verification. This application achieves synergistic optimization of microwave absorption performance and corrosion resistance, solving the problems of long development cycles and high costs associated with traditional trial-and-error methods. The designed core-shell structure microwave absorbing material can be widely used in fields such as electronic equipment shielding, stealth technology, and microwave anechoic chambers.

The application relates to a detection and communication integrated identification method for instantaneous permanent fault of a power collection line by means of IIDG active energy injection, and belongs to the technical field of power system relay protection and automation. The method comprises the following steps: after a fault occurs in a power collection line, starting an IIDG with network construction capability, realizing zero starting voltage rise through VF control, establishing a characteristic voltage, judging whether a composite voltage criterion is met, if yes, the fault is permanent, and the injection source exits after maintaining a constant frequency transmission; if not, the fault is instantaneous, and the injection source sends a frequency shift information after a time delay. Voltage is continuously monitored during the injection, and whether a complete frequency shift signal is received is determined through a frequency variance criterion and data segment correlation. If the voltage does not meet the composite voltage criterion during the detection process and the complete frequency shift signal is received, reclosing is allowed after power loss again, otherwise, reclosing is locked. The method aims to solve the technical problem that the fault nature of the power collection line under a voltage loss condition is difficult to identify in the prior art.

The invention discloses an endangered plant phoebe hainanensis identification and systematic monitoring method based on multi-source data fusion, and belongs to the technical field of endangered plant protection. According to the method, a potential distribution area is delineated through a species distribution model, individual accurate identification and filing are achieved through a multi-branch feature fusion model, multi-scale growth and microenvironment data are collected through Internet of Things nodes, and finally a'discovery-identification-monitoring-protection 'whole-process closed loop is formed through a unified data management platform. According to the method, phenological feature extraction, transfer learning modeling, man-machine collaborative verification and low-power-consumption Internet of Things monitoring technologies are adopted, and the problems that a traditional method is low in efficiency, insufficient in recognition precision and fragmented in monitoring are solved. According to the method, efficient discovery, accurate identification and long-term dynamic monitoring of the wild population of the phoebe hainanensis are achieved, scientific support is provided for protection decision making, and the method is convenient to operate and high in practicability and has protection value and application prospects.

This invention provides a collaborative control system and method for urban lake stormwater and flood control, relating to the field of flood management. The system and method utilize a multi-source heterogeneous information fusion system, collecting and standardizing multi-dimensional data such as quantitative precipitation forecasts, hydrology, urban flooding, and water quality to construct a unified data lake. This achieves high-precision, multi-dimensional information perception, providing comprehensive and high-quality data support for control decisions. It addresses the problems of incomplete information perception and inconsistent data standards in existing technologies. Furthermore, it establishes a dynamic risk assessment model integrating flood control, ecological water quality, social, and water resource utilization risks. Risk indicators serve as both hard constraints and soft costs in scheme comparison, enabling quantitative assessment and control of multiple risks. This allows decision-makers to clearly understand the potential risks of different schemes, significantly reducing the blindness of decision-making and improving the safety of control schemes.

This invention relates to the field of management technology and discloses a method and system for generating management suggestions for long-term, low-maintenance mixed flower borders. The method includes: acquiring plant growth data and community succession data based on the spatial distribution characteristics of the sample plot, plant growth morphology characteristics, and environmental characteristics during the flower border's growth cycle; extracting characteristic data on plant morphology, growth rhythm, and tolerance performance; classifying all plant feature vectors based on a decision tree model; determining distance parameters between different plant combinations based on the K-nearest neighbor algorithm and judging the stability trend and substitution relationship of each plant combination; analyzing the vertical hierarchy, coverage relationship, and color coordination relationship of plants based on the plant combination structure; determining the association results between the community discrimination results and the flower border structure layout based on an association rule algorithm; and determining management suggestions based on the association results and a planning suggestion model. This invention ensures the reliability of mixed flower border management.

A pollution source data quality problem intelligent diagnosis method based on a knowledge graph comprises the steps that the knowledge graph is constructed, and abnormal type diagnosis and abnormal root marking are carried out on the knowledge graph; converting the multi-source data stored in the database at the current moment into quadruple data, accessing the quadruple data into the knowledge graph to execute an intelligent diagnosis process, if an exception type is generated, performing confidence detection on the exception type, and screening a core exception type; abnormal nodes of core abnormal types and potential source nodes are constructed, abnormal source tracing is carried out, and a standardized tracing report is generated based on a tracing result and is fed back to a pollution discharge unit; and if the exception type is not generated, constructing a critical feature database, performing retrospective detection on the tetrad data, and judging whether to generate an estimated exception root or not according to a retrospective detection result and feeding back the estimated exception root to a pollution discharge unit. According to the method, the key data quality problem is effectively focused, irrelevant anomaly interference is reduced, the anomaly recognition accuracy is improved, and resource waste in low-value anomaly processing is avoided.

This application relates to a multi-level adaptive control method, apparatus, device, and storage medium for power quality, belonging to the field of power grid service technology. The method includes: determining the disturbance condition type of the power grid based on target indicator data; selecting a first control strategy from a control strategy template based on the disturbance condition type; updating the strategy parameters of the first control strategy based on the target indicator data and the disturbance condition type to obtain a second control strategy; and managing power quality of the power grid according to the second control strategy. This application effectively improves the efficiency and effectiveness of power grid power quality management, while relying on data-driven strategy selection and parameter optimization to reduce the blindness of management and ensure the scientific nature and effectiveness of power grid power quality management.

This invention discloses a method, equipment, and medium for airline allocation in collaboration between base airlines and alliances, relating to the field of airport operation planning technology. By predicting the alliance's capacity demand within a preset future timeframe, and integrating base airline capacity growth forecasts, alliance collaboration needs, and terminal capacity constraints, a multi-objective optimization model is constructed. This model achieves precise matching and dynamic adjustment between airlines and terminals, strengthens the adaptability constraint between the alliance's future total capacity and the terminal's design capacity, ensures that the alliance's future total capacity in the current allocation does not exceed the terminal's design capacity, and provides quantitative capacity recommendations for future terminal planning. It solves the problems of traditional methods neglecting alliance collaboration, capacity adaptability, and planning foresight, reduces cross-terminal transfer costs, improves airport operational efficiency and passenger experience, and provides a scientific basis for future terminal planning. It is applicable to the long-term planning and daily scheduling of large multi-terminal hub airports.

This invention relates to the field of power generation forecasting technology, specifically to a power generation forecasting method and system based on multimodal data fusion. The method includes: constructing a power generation forecasting model based on multimodal data; using the power generation forecasting model to predict power generation at a specific future time scale to output power generation forecast data; and calculating prediction deviation data by combining actual power generation data; decomposing and quantifying the prediction deviation data based on the degree of influence on the power generation deviation to obtain dispatch strategy deviation, equipment availability deviation, meteorological forecast deviation, and model inherent deviation; optimizing the parameters of the power generation forecasting model based on at least one of the dispatch strategy deviation, equipment availability deviation, meteorological forecast deviation, and model inherent deviation to obtain an optimized power generation forecasting model; and predicting power generation at a specific future time scale in the next stage based on the optimized power generation forecasting model, thereby continuously improving the accuracy and stability of power generation forecasting.

The application discloses a kind of radioactive contaminated clothing intelligent management method and related equipment.The method comprises the following steps: collecting the initial radiation dose of the contaminated clothing and generation time;Based on the nuclide decay formula, a decay tracking calculation process is constructed, the current dose is calculated in real time according to the nuclide half-life parameter, and the total dose is obtained by superimposing the contribution of each nuclide when there is multi-nuclide contamination;The current dose calculated in real time is compared with the preset control threshold, and the washable instruction is pushed to the medical terminal when the control threshold is met, and the remaining decay time prompt is output when the control threshold is not met.It can solve the problem that the management of radioactive contaminated clothing is highly dependent on manual, which can easily lead to waste of manpower, missed or wrong recording, and occupational health hazards caused by frequent close contact of medical staff with high-activity contaminated clothing during manual monitoring.

This application discloses a photovoltaic-driven soil moisture feedback irrigation device, comprising: a main body, a water storage tank, an electrical box, a photovoltaic module, and a sprinkler assembly. The photovoltaic module is installed on the top of the main body. The electrical box and the water storage tank are sequentially arranged on the main body and located below the photovoltaic module. The sprinkler assembly is disposed on the outer peripheral wall of the water storage tank and connected to the interior of the water storage tank. The electrical box contains a control component for the photovoltaic module. The bottom of the main body is conical and is equipped with a humidity sensor for detecting soil moisture. The humidity sensor is electrically connected to the control component, and the control component is electrically connected to the sprinkler assembly to control the sprinkler assembly to start spraying water. This device can adaptively adjust the spray volume according to the soil moisture conditions to achieve a reasonable irrigation effect.

This invention discloses a training method and system for quadruped robots based on deep reinforcement learning. The AMP training framework significantly enhances the robot's adaptability to complex environments. By distinguishing the robot's current motion sample distribution from that of a reference sample, the training aims to maximize the probability of misjudgment and the reward for reinforcement learning, enabling the robot to learn motion patterns that meet actual needs. When the environment changes, the robot can adjust its strategy promptly based on new information without remodeling, solving the problem of traditional methods being sensitive to environmental changes. Furthermore, this framework introduces imitation learning into reinforcement learning, providing effective guidance for robot learning, reducing the blindness of random exploration, accelerating the learning speed of effective strategies, and improving training efficiency. Simultaneously, the integrated hierarchical LSTM Actor network can extract and aggregate motion features at multiple time scales, allowing the robot to generate actions that consider both short-term gait and long-term trends, resulting in more natural and fluid movements. It also fully utilizes existing sample information, improving sample utilization and reducing training costs.

This invention discloses a hybrid attention network method for 3D prediction of deep-sea rare earth elements, integrating spatial and depth data, and relating to the interdisciplinary fields of artificial intelligence and marine mineral resources. The method first acquires multi-source environmental and depth data, which are then integrated, cleaned, spatially aligned, interpolated, and standardized to construct a model input containing environmental feature image patches and standardized depth values. Spatial feature vectors are extracted using a convolutional neural network, and depth feature vectors are encoded using a multilayer perceptron. The two types of features are concatenated, and dynamic weights are generated and weighted using an attention network to obtain a weighted feature vector. This weighted feature vector is input into the prediction head module, and the model is trained using a loss function, optimizer, and early stopping mechanism. The trained model is applied to a 3D mesh of the target region, and after inference prediction and de-standardization, the predicted 3D rare earth element content is output. Simultaneously, the feature contribution is analyzed through an interpretable model.