5results about How to "Efficient feedback" patented technology

The invention discloses a photovoltaic power station multi-source fusion remote monitoring method and system based on a cloud platform, and relates to the technical field of photovoltaic power generation, and the method comprises the steps: deploying a plurality of data collection units at a target photovoltaic power station, collecting the multi-source operation data of the target photovoltaic power station in real time, uploading the multi-source operation data to a cloud data processing platform, and carrying out the fusion and correlation analysis, power generation performance deviation is identified, an abnormal cause is positioned, and a fault analysis result is generated; and generating a fault processing scheme according to a fault analysis result, and pushing the operation and maintenance work order to an operation and maintenance personnel terminal to execute field processing. The technical problems that the operation and maintenance efficiency is low and the power generation efficiency is limited due to the fact that operation and maintenance of an existing photovoltaic power station depend on manual inspection, the fault diagnosis period is long and the accuracy is poor are solved, multi-source fusion remote monitoring based on the cloud platform is achieved, real-time accurate diagnosis and quick response to faults of the photovoltaic power station are achieved, and the working efficiency of the photovoltaic power station is improved. And the operation and maintenance efficiency and the power generation benefit are improved.

The application provides a kind of smart textile with visual sensing and interactive function and its application, comprising: textile display screen formed by interweaving two different fibers, textile sensor and control chip; textile display screen is connected with textile sensor through control chip, information collected by textile sensor is transmitted to control chip, control chip transmits feedback signal to textile display screen by information processing and analysis, so as to display different graphic pattern information.The smart textile of the application has low manufacturing cost, short process and wide application range.

The utility model provides a kind of gas-liquid two-phase self-circulation temperature control system, including booster unit, temperature compensation unit, pressure and temperature monitoring unit, the temperature compensation unit is connected with the export pipeline of space environment medium carrier, the booster unit uses wide temperature range high pressure fan, the rear end pipeline of the booster unit is equipped with medium compensation unit, the parallel pipeline of the medium compensation unit is equipped with electric heater, the output of the medium compensation unit is connected the import pipeline of space environment medium carrier;The front end of the booster unit is equipped with temperature compensation unit, the front end of the temperature compensation unit is equipped with pressure and temperature monitoring unit.It solves the technical problem that the existing temperature control system needs to first rise and fall temperature to the medium before pressure boosting, resulting in energy loss and complex pipeline layout, low space utilization, higher equipment investment cost.The utility model can be widely applied in temperature control system.

This invention discloses an immediate reward learning method based on self-supervised reinforcement learning, belonging to the field of artificial intelligence reinforcement learning technology. First, the invention calculates a first prediction error between the agent's predicted state and the actual state. Second, it reconstructs action data using an inverse dynamics model and calculates a second prediction error. Then, it constructs a causal confidence factor based on the second prediction error to generate an effective prediction error. Next, it generates a fast-channel reward and calculates the volatility of the reward signal using an oscillation index. Finally, it generates the final immediate reward. By integrating a multi-layered mechanism of prediction error correction, causal relationship modeling, reward stability assessment, and policy stochastic adjustment, the stability and reliability of the reward signal can be effectively enhanced, noise interference reduced, and training oscillations avoided. Simultaneously, it significantly improves the quality of immediate reward generation in sparse reward environments, accelerates policy convergence, and enhances the efficiency and stability of self-supervised reinforcement learning in complex tasks.

The application provides a steel enterprise product adjustment effect evaluation system, method, equipment and medium, and relates to the technical field of data processing, and comprises the following steps: a data preprocessing module is used for preprocessing original transaction data corresponding to an evaluated object to obtain target transaction data, wherein the original transaction data comprises steel order data and steel gross profit data corresponding to a plurality of steel products sold by the evaluated object; a product adjustment effect evaluation module is used for constructing a product adjustment evaluation matrix corresponding to the evaluated object based on initial index values corresponding to multidimensional index items, so as to determine a product adjustment evaluation index corresponding to the evaluated object, and elements of the product adjustment evaluation matrix are used to describe target index values of the evaluated object relative to the multidimensional index items; and an evaluation visualization module is used for visualizing the product adjustment evaluation index. The application can more comprehensively and objectively quantify the product adjustment effect, thereby providing effective feedback and guidance for product adjustment decision-making.