7 results about "Stability Model" patented technology

The application discloses a power electronic system stability and toughness evaluation method based on multi-granularity hybrid equivalent, and belongs to the technical field of power system stability analysis; the method comprises the following steps: dividing a power electronic system into multiple granularity model partitions, wherein model partition granularity comprises detailed model partitions and equivalent model partitions; using a multi-region hybrid equivalent method to couple the dynamic behaviors of power electronic equipment in the detailed model partitions and equivalent equipment in the equivalent model partitions, establishing a stability model of the power electronic system, solving the stability model through simulation, and mastering the dynamic characteristics of the power electronic system under different granularities; and evaluating the stability and toughness of the power electronic system based on the stability model, and simulating the voltage and current characteristics of the power electronic system under multiple different disturbance conditions. The application only focuses on the port characteristics of the system, does not involve the detailed structure and parameters of the system, is suitable for stability and toughness analysis of complex power electronic systems, and has good feasibility and practical value.

The invention discloses a conveying mechanism and method of a cork pad pasting machine, and belongs to the technical field of automation equipment.The method comprises the steps that material characteristics, mechanism states and environment parameters are obtained through a plurality of sensors and input into a material environment stability model, an execution system health model and a system diagnosis integrity model respectively; calculating to obtain a corresponding state coefficient; further, in combination with a signal-to-noise ratio index of the visual positioning system and a system clock synchronization deviation index, calculating a collaboration degree through a perception-execution collaboration model under the condition of considering the support effect of an execution and diagnosis system; and finally, based on the collaboration degree, the material environment stability coefficient and the current torque ripple coefficient, calculating and outputting an optimized target torque ripple coefficient through a dynamic adaptation model so as to adjust a driving motor. According to the method, multi-dimensional state perception, intelligent collaborative evaluation and adaptive closed-loop optimization of execution parameters in the conveying process are realized, and the conveying precision, the stability and the equipment adaptive capacity are effectively improved.

The invention belongs to the technical field of well drilling, and particularly relates to a drill string dynamic stability control method and system, a storage medium and electronic equipment, and the method comprises the steps: constructing a prediction model based on a BP neural network, and predicting PID parameters by the prediction model according to input operation parameters; the prediction model and a PID controller are connected to form a drill string dynamic stability model, and system stability verification is carried out on the drill string dynamic stability model; and dynamically adjusting the drilling speed fluctuation of the drill string in real time by using the drill string dynamic stable model which is verified to be stable by the system stability. Based on deep learning and PID control, the advanced control method is adopted, a good real-time dynamic adjustment effect is achieved, the drilling risk and damage to drilling equipment caused by too large fluctuation of the drilling speed of a drill column in the drilling process are restrained, and by means of the method, the equipment loss is expected to be reduced, and the safety and reliability of drilling operation are guaranteed.

The invention provides a server fault detection method and system based on artificial intelligence, and the method comprises the steps: obtaining the operation state information of a cloud server, carrying out the node construction of the operation state information, obtaining a plurality of cloud nodes, and determining the topological structure of all cloud nodes in the cloud server; generating an operation stability model of the cloud server through the topological structure, and determining the operation stability of the cloud server; dividing the operation state information into a normal operation record and an abnormal operation record, determining an abnormal ratio of each operation state index through the abnormal operation record, and performing balance analysis according to the abnormal ratio and the normal operation record to obtain a plurality of balance information entropies; and determining the fault risk of each operation state index of the cloud server at each sampling moment through all the balance information entropies and all the operation stability. The minority class learning ability of the classification model can be improved on the premise that the operation data of the cloud server is unbalanced.

This invention belongs to the technical field of biological product storage and control, and relates to an environmentally adaptive storage system for improving the stability of *Phaves rubra* astaxanthin. The system includes: a system initialization and modeling module for constructing a storage space integrating multi-dimensional sensing units and a physical field execution array and establishing a baseline stability model; a synchronous data acquisition module for generating a joint monitoring data stream; a dynamic feature extraction module for constructing dynamic feature vectors; a degradation mechanism identification module for identifying the dominant degradation mechanism and outputting an identifier; a targeted intervention strategy generation module for retrieving strategies and generating a priority sequence of intervention instructions; a microenvironment execution module for reconstructing the local microenvironment through physical field intervention; and a closed-loop feedback control module for switching storage modes. This invention solves the problem that traditional storage technologies struggle to effectively identify and target the degradation process dominated by *Phaves rubra* astaxanthin nanoemulsions based on dynamic environmental fluctuations.

The application discloses an experimental method based on a foundation pit dewatering simulation device, which comprises the following steps: obtaining foundation pit soil data in a dewatering simulation process; obtaining engineering parameters based on data conversion, establishing a foundation pit stability model, and obtaining the state of the current foundation pit soil; predicting the prediction curve of the foundation pit under the dewatering mode through a memory neural network; obtaining n historical experimental data matched with the current dewatering mode from a historical database, fitting and outputting a theoretical reference curve after averaging, comparing the theoretical reference curve with the prediction curve of the foundation pit, and calculating a deviation value; when the deviation value is greater than a first preset value, detecting the foundation pit soil data at the beginning of the dewatering mode, correcting the current foundation pit soil data if the foundation pit soil data is greater than a second preset value or smaller than a third preset value, or representing the abnormality of the dewatering mode; and the data analysis is more convenient, the human participation in the analysis process is reduced, and the reliability is high.

The invention discloses a vehicle-mounted edge low-delay unloading method based on reinforcement learning and Lyapunov optimization. The vehicle-mounted edge low-delay unloading method comprises the steps that system modeling is conducted, and a vehicle edge calculation physical model, a transmission model, a calculation model and a stability model are built; problem construction: long-term stability is converted into a time slot-by-slot optimization problem through a Lyapunov theory; designing a multi-agent algorithm, and constructing an MAPPO-L framework; self-adaptive exploration and strategy optimization are carried out, and the exploration rate is adjusted through a self-adaptive exploration mechanism; and deploying applications, and verifying through a simulation experiment and a real traffic scene. According to the method, Lyapunov optimization and multi-agent neighbor strategy optimization (MAPPO) are fused, long-term stability is converted into time slot-by-slot optimization, low-delay and high-stability task unloading in a dynamic environment is achieved through multi-agent distributed decision, the resource utilization rate and the system robustness are improved, and the method is suitable for a real vehicle edge calculation scene.