5results about How to "With self-learning function" patented technology

The application provides a power-oriented electric propulsion gas supply self-learning closed-loop control system and method, which is composed of a storage and supply unit, a power processing unit, a control unit and an electric thruster; the working current of the electric thruster is monitored, if the current is lower than the lower limit, the valve is opened to supplement the gas for a certain time length, the current value of the electric thruster working maximum 3s sliding average within a certain time length is monitored after the gas supplement is completed, and the gas supplement time length is adjusted according to the maximum value with a certain step distance, and the step distance is corrected according to the number of adjustments. The application solves the working condition drift problem of the electric thruster caused by the state change of the spacecraft or the erosion of the thruster itself in the prior art by using the multi-stage self-learning adjustment mode, under the premise of limited on-orbit computing resources, and collects the working parameters for data accumulation and open-loop control of the fault mode.

The application provides a coal-fired power station parallel operation axial flow induced draft fan stall failure early warning method, data screening judgment under the steady state condition is carried out through the sliding window based on information entropy, and the normal current curve band corresponding to the unit load is given. The trend test is carried out on the key data of the induced draft fan based on the non-parametric statistical test method, and the significant trend judgment under the confidence degree is given. The collected induced draft fan data are calculated in real time based on the historical failure data and the failure early warning model, the fan stall early warning information is given by comprehensively considering the relationship between the moving blade opening and the flue gas flow and the current state, and considering the operation state of another induced draft fan in parallel operation. The system has a self-learning function, can optimize the early warning model along with the accumulation of equipment operation data, and can be integrated with the monitoring system of the power station to realize automatic early warning. Finally, the accuracy and timeliness of the failure early warning are improved, and the system has a high popularization prospect.

The invention relates to a digital security and protection system trust regulation and control method and device based on emotion calculation. According to the method, multi-modal data such as videos, voices and behaviors are collected and subjected to desensitization and encryption processing, emotion vectors are generated through an emotion recognition model, risk perception indexes are calculated in combination with privacy risks, bias risks and control risks, a trust prediction model is constructed to obtain trust willingness of a user, and when the trust willingness is lower than a preset threshold value, the trust of the user is predicted. And the system automatically executes algorithm transparency improvement and risk communication feedback, and iteratively updates model parameters through a self-learning mechanism, thereby realizing dynamic regulation and self-adaptive optimization of user trust in the digital security and protection system. The application has the effect of improving the security, credibility and user acceptability of the system.

The invention provides an optimization method and system for a process for removing calcium and magnesium from calcium and magnesium containing waste liquid. The optimization method comprises the following steps: S1, by taking sodium fluoride as a precipitator, setting initial process parameters of the calcium ion and / or magnesium ion-containing waste liquid, and obtaining initial parameters in the calcium ion and / or magnesium ion-containing waste liquid; s2, performing formula model construction through the initial parameters and the initial process parameters to obtain a calculation K value; s3, comparing the calculated K value with a target K value, and optimizing the process parameters and / or the initial parameters according to a comparison result until the target K value is reached; and S4, removing calcium ions and / or magnesium ions in the waste liquid by adopting the optimized parameters capable of reaching the target K value and taking sodium fluoride as a precipitator. According to the method, a plurality of key parameters in the calcium and magnesium removal process of sodium fluoride can be comprehensively regulated and controlled, and a judgment constant (K value) is output through an original mathematical formula model and is used for guiding or automatically controlling process operation, so that the optimal calcium and magnesium removal effect is achieved.

This invention relates to the field of dust collector fan technology, and more particularly to a method and system for detecting faults in dust collector fans. The method includes collecting normal operating data of the target fan; inputting the normal operating data into a neural network model; calculating and outputting the motor operating current and motor speed values ​​through the neural network model; and obtaining a neural network mathematical model through training. The advantages of this invention are: by using an edge warning terminal to collect multi-dimensional operating data (vibration, noise, temperature, speed, and current) in real time at the dust collector fan equipment site, and by continuously training the operating data to construct a neural network mathematical model, it can capture subtle changes in operating parameters that deviate from a healthy state, issuing warnings in the early stages or potential phases of fault occurrence. This allows maintenance personnel to shift from traditional "reactive maintenance" to "predictive maintenance," intervening early in the nascent stage of a fault and effectively avoiding production losses caused by unplanned downtime.