16results about How to "Optimal Control Strategy" patented technology

The invention belongs to the field of wastewater treatment and industrial automatic control, and particularly relates to an intelligent dosing control method based on water quality online monitoring. The method comprises the following steps: capturing a water body spectrum fingerprint spectrum by using a full-spectrum scanning module to obtain a characteristic signal sequence; a chemical informatics molecular algorithm is introduced for preprocessing and feature enhancement, and molecular fingerprint information is recognized; a convolutional neural network model is constructed to decouple chemical oxygen demand, ammonia nitrogen, total phosphorus and water toxicity indexes from the spectral signals, and multi-parameter parallel inversion is achieved; and based on the inversion index and the spectral dynamic trend, the dosage is calculated through an intelligent decision-making algorithm, and accurate dosing of the medicament is driven. According to the invention, through fusion of full spectrum scanning and deep learning, high-precision anti-interference water quality perception is realized, potential risks can be identified and preventive intervention can be carried out, and the intelligent level and economical efficiency of dosing control are improved.

This invention discloses an intelligent combustion control method and system based on online fiber optic detection of flame signals. The method monitors the flame light signal emitted by the burner via fiber optic cable, analyzes the signal to predict pollutant concentrations, and performs fuzzy adjustment and defuzzification on the burner's feedback signal based on the predicted values, thereby achieving intelligent and adaptive control of the combustion process. This method can quickly respond to changes in combustion conditions, optimize control strategies, ensure the stability and environmental friendliness of the combustion process, overcome the technical bottlenecks of high cost and poor real-time performance, effectively reduce system hardware costs, and improve the intelligence level of the control process.

This invention discloses an intelligent model predictive control method for intensive mixing processes based on reinforcement learning. Addressing the degradation of control performance caused by model mismatch due to modeling errors, abrupt state changes, and component degradation in traditional model predictive control, this invention introduces reinforcement learning into the model predictive control framework. The method designs an intelligent model predictive control approach based on reinforcement learning to solve a standard quadratic programming problem with respect to optimization variables. Reinforcement learning selects compensation terms to compensate for model deviations caused by model mismatch, combining this with the controller output and applying it to the system. Finally, the reward is calculated and updated to optimize the control compensation selection for future time steps. As the control cycle progresses, new information is continuously integrated, model predictions are updated, and control inputs are optimized to adapt to potential changes in system behavior or external disturbances. This improves system stability and control accuracy, ensuring that the intensive mixing process can track a preset trajectory.

This invention discloses a fuel cell energy management optimization method, comprising the following steps: 1) The fuel cell energy management system maximizes the utilization of fuel cell energy by adjusting and optimizing system parameters; 2) Modeling the fuel cell energy management system, including the stack, battery management system, and DC / DC converter; 3) Simulating and analyzing the fuel cell energy management system, considering the system's operating state, load requirements, and environmental conditions; 4) Optimizing and analyzing the simulation results to obtain the optimal parameter configuration and control strategy; 4-1) Optimization analysis of the fuel cell energy management system; 4-2) Adjustment analysis of the fuel cell energy management strategy; 4-3) Adjustment analysis of the fuel cell energy management system.

The application provides a residential energy system optimal control method based on data safe transmission, comprising: establishing a residential energy system model; obtaining electricity price, residential load and total cost function at each moment; taking minimizing the value of the total cost function as a target, iteratively optimizing the residential energy control sub-model to obtain an optimal residential energy control strategy; controlling and scheduling the residential energy according to the optimal residential energy control strategy; in the scheduling process, storing the optimal control data into a corresponding database in the blockchain after encryption; a queryer queries the data of each database in the blockchain through a proxy re-encryption mechanism and calculates in a secure multi-party calculation mode; the application can effectively perform optimal control on the residential energy; in addition, the application also introduces the blockchain technology, realizes safe encryption collection, transmission and interaction of the decentralized, tamper-proof, controllable and traceable residential energy optimal control data.

This invention relates to a dual positioning method for objective lens converters, combining mechanical locking and electronic calibration, belonging to the field of precision optical instrument control technology. The method includes: performing correlation analysis on the rotation angle and initial deviation value of the turntable reaching the target position to generate coarse positioning control parameters; calling the self-calibration operation dependency of the coarse positioning control parameters, and calibrating the confidence level of the contribution weight corresponding to the self-calibration operation dependency to obtain an electronic calibration positioning control strategy; dynamically monitoring the drive response state and locking execution state during the objective lens conversion process, comparing them with historical degradation samples, and outputting a positioning calibration feedback report; controlling the coaxiality deviation of the optical axis to meet a preset locking threshold during positioning, and compensating for the sources of positioning errors and mechanical locking stability during the objective lens conversion process based on the positioning calibration feedback report, thereby generating a dual positioning scheme for the objective lens converter.

The invention relates to the technical field of equipment control, in particular to a nitrogen spring assembly production line based on man-machine collaboration, which comprises the following steps: a state sensing module acquires real-time data of a plurality of stations in production line equipment, processes and generates a standard station state vector set, and sends the standard station state vector set to an assembly situation fusion center; the assembly situation fusion center is provided for the rhythm deduction module and the decision optimization module; the rhythm deduction module calculates a rhythm offset quantitative index based on the virtual production line dynamic mirror image and sends the rhythm offset quantitative index to the decision optimization module; the decision optimization module generates a cooperative control instruction set based on the virtual production line dynamic mirror image and the beat offset quantitative index and sends the cooperative control instruction set to the execution cooperation module; and the execution collaboration module converts the collaboration control instruction set into an equipment control signal to be issued and executed, collects an execution state, generates execution feedback data and returns the execution feedback data to the state sensing module and the assembly situation fusion center. According to the invention, assembly rhythm dynamic balance and material circulation self-adaptive control are realized, and the production efficiency is improved.

This invention provides a water hammer prediction system and device. The invention relates to the field of pipeline fluid transport safety technology. The water hammer prediction system is configured to: acquire static topology and physical attribute information and multi-dimensional real-time operating condition data of the fluid transport system, and pre-calibrate a dynamic simulation model based on the information and data; when a water hammer triggering event is detected, invoke the pre-calibrated dynamic simulation model to perform an initial prediction to generate an initial prediction result, and issue a pre-control command to the first-level protection device based on the initial prediction result; acquire the measured impact index at the first-level protection device and compare it with the initial prediction result to determine the prediction error; and, based on the prediction error, correct the dynamic simulation model in real time, and drive the corrected dynamic simulation model to perform cascade control of the next-level protection device.

The application discloses a kind of reward methods based on pipeline transportation particle deposition rate and energy consumption constraint.The method combines and constructs into reward function with key parameters such as particle deposition rate in pipeline, inlet flow rate and pipeline pressure drop, and trains neural network based on the reward function to obtain suitable control strategy, so as to achieve the purpose of reducing deposition and optimizing flow state.The application solves the problem that deposition inhibition effect and system energy consumption optimization cannot be considered simultaneously in the existing particle deposition control method in pipeline.Through the continuous interaction of artificial deep neural network and flow field numerical simulation environment, network parameters are dynamically updated, and the optimal control strategy is gradually obtained.And accordingly adjust inlet fluid velocity, change the flow state in pipe, realize the effective inhibition of particle deposition.Through the reward mechanism based on particle deposition rate and energy consumption, deposition can be effectively reduced and eliminated, flow stability is improved, and the effect of reducing deposition is better than that of traditional method.

The utility model relates to prefabricated component forming processing technical field especially relates to a kind of visual prefabricated component mould, the utility model embodiment provides visual prefabricated component mould includes: two side plates of relative arrangement, at least one of two side plates adopts transparent material;Side plate assembly, between two side plates, side plate assembly and two side plates are enclosed into grouting cavity, grouting cavity is equipped with grouting port and outlet port;Connecting assembly, connect side plate assembly and side plate, the utility model embodiment provides visual prefabricated component mould can observe grouting process in real time, improve the production quality and efficiency of prefabricated component.

The invention relates to the technical field of oil-gas well automatic control systems, in particular to an oil-gas well automatic control system based on cloud computing. The technical problems are as follows: when an automatic control system of an oil and gas well is used, data processing and control are performed depending on a local server or an industrial personal computer, the real-time performance is relatively high, but the local computing power and the network bandwidth are limited, and multi-data analysis on the internal condition of the oil and gas well is inconvenient; according to the technical scheme, the automatic control system for the oil and gas well comprises a data analysis module; compared with a traditional oil-gas well automatic control system, the oil-gas well automatic control system depends on a local server or an industrial personal computer to carry out data processing and control, but is limited by local computing power and network bandwidth, so that multi-data analysis on the internal condition of an oil-gas well is inconvenient; according to the automatic control system for the oil and gas well, deep analysis and processing are carried out through the powerful computing power and storage resources of cloud computing, and the cloud end can optimize a control strategy in real time and issue an instruction to the edge end for execution.

This invention discloses a multi-voltage power supply management and control method for mine environmental monitoring, relating to the field of mine power management technology. The method uses a PLC controller to synchronously collect data from the main power supply, backup power supply, auxiliary power supply, transformer, battery management system, and mine environmental parameters. These environmental parameters include at least methane concentration, smoke concentration, temperature, humidity, water level, and ventilation status. Based on the collected data, an environmental risk index and a power demand index are constructed, and the operating status is classified into normal, warning, emergency, and critical levels. This allows for optimal switching between the main, backup, and auxiliary power supply paths. This method enables coordinated control of mine environmental risks and power management, reduces switching impact, ensures continuous online operation of mine monitoring, communication, alarm, and interlocking links, and improves the safety and continuity of mine power supply.

This invention belongs to the field of industrial control and monitoring. Addressing the problems of low intelligence, insufficient computing power, reliance on manual parameter adjustment, simple data processing, and lagging cloud collaboration in traditional industrial control systems, it proposes an Artificial Intelligence Box (AIBox) based on an industrial control computer. This AIBox uses the Rockchip RK3588 chip industrial control computer as its core, integrating a protocol conversion gateway, I / O module, LoRa communication module, and various sensors and controllers, possessing superior computing power and industrial-grade stability. Its core implementation process includes: multi-interface, multi-protocol access to field sensors and existing systems to complete data acquisition; improving data quality through validity verification, filtering, and standardization; real-time calculation based on built-in algorithms and issuing safe and compliant control commands; uploading data to the cloud platform via the MQTT protocol, while simultaneously receiving optimized commands generated by high-dimensional fusion from the cloud platform via 4G / 5G; and providing feedback after command execution to assist the cloud platform in optimizing algorithms, forming a closed-loop control. This invention achieves multi-device access compatibility, standardized data processing, reliable cloud interaction, and intelligent control, improving the control accuracy and energy efficiency of industrial equipment, and is applicable to various industrial control and monitoring scenarios.

The application discloses a slow active hydraulic suspension system based on a reversing valve and a control method thereof, which comprises a hydraulic system, an air spring system, vehicle-mounted sensors and an ECU; the hydraulic system comprises a one-way pump, a two-position four-way electromagnetic valve, an adjustable damping valve and a hydraulic cylinder, the hydraulic cylinder is fixed on a wheel, a hydraulic cylinder piston rod is fixed on a vehicle body, the adjustable damping valve is installed between a hydraulic cylinder upper cavity and a first accumulator, a hydraulic cylinder lower cavity is communicated with a second accumulator, the first accumulator and the second accumulator are connected with the two-position four-way electromagnetic valve respectively, the two-position four-way electromagnetic valve is communicated with the one-way pump to form a hydraulic system loop; the air spring system comprises an air spring, an air pump and a motor, the air spring is connected with the vehicle body and the hydraulic cylinder, the motor is connected with the air pump through a first clutch, and the air pump is connected with the air spring; the hydraulic system and the air spring system share the motor, and the motor is further connected with the one-way pump through a second clutch.

This invention discloses a gas holder operation control method and system, relating to the field of gas holder control technology. The method includes: acquiring real-time pressure data inside the gas holder and processing the real-time pressure data to extract pressure characteristic data; using an anomaly identification algorithm to perform anomaly analysis on the pressure characteristic data to identify key factors affecting the pressure stability of the gas holder; establishing a pressure regulation model based on the key factors; dynamically adjusting the real-time operating parameters of the gas holder using the pressure regulation model; and optimizing the control strategy. This invention not only improves the automation and intelligence level of gas holder operation but also effectively optimizes the control strategy, ensuring the safe and stable operation of the gas holder under high load and complex operating conditions, reducing the risk of failure, and thus improving the overall reliability and efficiency of the production system.

The invention discloses a method for improving communication delay in distributed secondary control of a direct-current micro-grid. The method comprises the following steps: S1, constructing a three-level cooperative control architecture of a cloud end, a side end and an end layer; s2, decoupling the distributed secondary control algorithm into a plurality of micro-services with independent functions, and packaging each micro-service into a container mirror image supporting cross-cloud-side end node migration, resource isolation and standardized communication; s3, the cloud monitors the communication link delay and stability of each side end node in real time through periodic detection and data fusion; s4, dynamically deciding and calculating the deployment positions of the intensive and lightweight micro-service containers at the cloud-side end node; s5, container deployment and arrangement are executed; according to the invention, the real-time performance, the stability and the reliability of the secondary control of the DC micro-grid are improved by adopting a collaborative mode of a three-level collaborative architecture, micro-service dynamic deployment and digital twin delay compensation.