15 results about "Real time data acquisition system" patented technology

The invention belongs to the technical field of precise environment control for pig breeding, and particularly relates to a method and system for pig breeding environment-behavior correlation modeling and cooperative regulation and control. The method comprises the following steps: S1, modeling an association mechanism of a multi-element environment factor and a pig abnormal behavior: fully mining the association between the multi-element environment factor and the pig abnormal behavior in an existing knowledge base by introducing a pre-trained large language model LLM, and modeling; s2, construction of an environment-behavior collaborative decision-making system guided by the large language model: constructing a multi-source real-time data acquisition system, synchronously acquiring and fusing environment and pig behavior data, and constructing the environment-behavior collaborative decision-making system based on the fine-tuned large language model for realizing multi-factor collaborative attribution and intelligent regulation and control. According to the method, the pre-trained large language model and the multi-source perception data are fused, and the method has the advantages that the processing efficiency can be guaranteed, and meanwhile accurate modeling and analysis of the complex environment-behavior relation can be achieved.

The application discloses an intelligent accounting method for industrial water consumption, and relates to the technical field of industrial water consumption monitoring and intelligent accounting, and comprises the following steps: step one: receiving and analyzing water consumption accounting requirements, simultaneously acquiring historical data and real-time data from a historical data warehouse and a real-time data acquisition system, respectively, pre-processing, and generating a pre-processed data set. The intelligent accounting method for industrial water consumption realizes real-time self-adaptive adjustment of time-varying factors in industrial water consumption accounting by adopting multi-level data fusion, an improved deep learning network, data sliding window online learning, feedback closed-loop control and a hierarchical incremental updating strategy, significantly improves the prediction accuracy and system response speed of nonlinear water consumption behavior, and thus effectively overcomes the deficiency of static offline training of an accounting model in the prior art.

The present invention discloses a monitoring method for a transmission system of an offshore wind turbine, comprising the following steps: S1. According to the characteristics of the offshore wind turbine, select multiple components of the transmission system of the offshore wind turbine as monitoring points; S2. Arrange sensors at the monitoring points for monitoring; S3. Establish a real-time data acquisition system; S4. Establish a satellite communication system, and transmit the monitored parameters collected in real time to a ground station through the satellite communication system; S5. Establish a cloud data center, which is communicatively connected to the ground station and receives the monitored parameters transmitted by the ground station; S6. Establish a central data processing platform to receive the monitored parameters transmitted by the cloud data center and realize the processing and analysis of the monitored parameters. The present invention realizes the low-cost operation and maintenance of the offshore wind turbine, realizes the real-time monitoring of the parameters of each component of the offshore wind turbine, can timely know the operation situation, perform maintenance treatment according to the operation situation, and avoid greater economic losses caused by greater damage.

The present invention relates to the technical field of operation and maintenance of large steel structures, and discloses an intelligent operation and maintenance system for the whole life cycle of large steel structures, including an intelligent recognition and diagnosis module for obtaining inspection images taken by inspection equipment during the inspection process and performing damage recognition and positioning based on a pre-trained damage recognition model; a digital twin model construction module for constructing a digital twin model and performing online synchronization based on strain, temperature or vibration distribution data obtained by a real-time data acquisition system; and a digital twin model visualization module for, in response to a visualization request, obtaining the current digital twin model and synchronizing the damage recognized based on the inspection images, as well as the information of the inspection equipment, welding equipment and repair equipment in the working state, to the digital twin model and performing visualization. On the basis of realizing the digital twin model, the present invention superimposes the damage and related operation and maintenance equipment, and can more intuitively remotely display the real-time state of the large steel structure to the operation and maintenance personnel.

The invention discloses a coal-fired power plant carbon cost dynamic optimization method based on operation small indexes, and belongs to the technical field of data processing, and the method comprises the steps: obtaining an operation small index data set of a coal-fired power plant through a real-time data collection system; performing dynamic calculation based on the operation small index data set in combination with the carbon price information to obtain operation carbon cost data; evaluation is carried out according to the operation carbon cost data, multi-target iterative optimization is carried out according to a carbon cost evaluation result, and a target operation parameter combination is constructed; and according to the target operation parameter combination, the operation state of the coal-fired power plant is adjusted in real time, operation adjustment data is fed back to the operation small index data set for updating optimization, and dynamic closed-loop optimization of the carbon cost is achieved. The technical problem that in the prior art, coal-fired power plant carbon cost static management cannot adapt to carbon price fluctuation and unit working condition changes, and consequently carbon cost management and control are not accurate is solved.

The invention relates to the technical field of intelligent agricultural Internet of Things and facility automation control, in particular to an intelligent greenhouse real-time data acquisition system based on a cloud platform and a control method. Comprising the following steps: constructing a double-end cooperative control model composed of a cloud global optimization model and an edge lightweight prediction model; the system collects real-time state data of the intelligent greenhouse, calculates a prediction value by using a lightweight model and solves a prediction residual error; if the residual error is smaller than the threshold value, the gateway executes communication silence, and a local control signal is generated by directly utilizing a predicted value to drive an execution mechanism; and if the residual error is greater than or equal to the threshold value, the gateway uploads data to the cloud, the cloud server calculates model updating parameters based on the global model and issues the parameters to the edge, and the lightweight prediction model is corrected and updated in real time, so that efficient cooperation is realized. According to the method, the load pressure of the cloud server for processing massive concurrent data is effectively reduced, and reasonable distribution and efficient utilization of cloud side computing resources are realized.

A kind of quasi-real-time data acquisition system adapting competitive power market, system includes: distributed data collection unit, centralized data collection unit and acquisition master station, distributed data collection unit is connected with intelligent electric energy meter by its internal downlink communication module, and the acquisition data item parameter and task execution parameter are configured by its internal acquisition task and acquisition scheme configuration module, including: quasi-real-time acquisition task, 15 minute curve task, day frozen data task, month frozen data task;Centralized data collection unit is connected with acquisition master station by its internal file conversion and uplink communication module;Centralized data collection unit and several distributed data collection units are connected by HPLC local communication network formed by HPLC communication main module in centralized data collection unit and HPLC communication submodule in distributed data collection unit.The present application realizes concurrent asynchronous reading, greatly improves the timeliness of electric data, and has good application prospect.

An intelligent operation and maintenance system for full life cycle of a large-scale steel structure, may include: an intelligent identification and diagnosis module configured to acquire an inspection image captured by inspection equipment during the inspection process and to perform damage identification and localization based on a pre-trained damage identification model; a digital twin model construction module configured to construct a digital twin model and to perform online synchronization based on strain, temperature, or vibration distribution data acquired by a real-time data acquisition system; and a digital twin model visualization module configured to, in response to a visualization request, acquire a current digital twin model, and to synchronize all the damage identified based on the inspection image, as well as information on inspection equipment, welding equipment, and repair equipment in working conditions, to the digital twin model, and to perform visualization thereon.

The invention relates to the technical field of biological environment engineering, and provides a fucoxanthin synthesis optimization method and system based on reinforcement learning, and the method comprises the following steps: a real-time data acquisition system; constructing a state space; a deep reinforcement learning decision network; optimizing a dynamic strategy; and strategy verification is linked with the process. Through a light quality-stimulant collaborative reinforcement learning regulation strategy, the static operation limitation of a traditional process is broken through, the photosynthetic physiological state of microalgae is perceived in real time, the light environment and chemical signals are dynamically optimized, the fucoxanthin synthesis efficiency is remarkably improved, and a multi-omics closed-loop verification mechanism is combined, so that the fucoxanthin synthesis efficiency is improved. The molecular cascade effect of a light signal driving pigment synthesis pathway is illuminated, and a precise target spot is provided for metabolic engineering transformation.

The invention discloses a fixing mechanism and a slip ring performance testing method. The fixing mechanism comprises an operation table and a testing assembly arranged at the end of the operation table. The fixing assembly is used for fixing the sliding ring; the test assembly is connected with the slip ring through a connecting piece, the test assembly can change the stable state of the slip ring, the slip ring comprises a stator and a rotor which are arranged in the slip ring, and the slip ring is further provided with a power input terminal, a power output terminal, a communication input terminal and a communication output terminal. The test assembly simulates the real environment in the operation of the fan, and can test the static resistance and dynamic resistance of the slip ring. The real-time data acquisition system is used for carrying out communication data transceiving acquisition analysis on encoder data, and the contact stability and the vibration resistance of the slip ring are evaluated by comparing the resistance value change rate and the communication signal bit error rate under the static and dynamic working conditions.

The invention discloses a continuous casting process crystallizer liquid level initiative rapid control system based on mechanism fusion AI. The system comprises a real-time data acquisition system, a PINN prediction model, a crystallizer liquid level BP-PID controller and a stopper rod automatic controller. Wherein the real-time data acquisition system is responsible for acquiring key parameters of a continuous casting process and sending the key parameters to the designed PINN prediction model and the crystallizer liquid level BP-PID controller. And further obtaining complete flow field information and nozzle flow velocity through the prediction model. And then PID parameters of the crystallizer liquid level BP-PID controller are changed based on BP neural network calculation according to the data and the collected parameters, a more reasonable stopper target set height is output, and a stopper quickly and accurately reaches a control position through a stopper position PID controller. The metallurgical reaction state of the real-time continuous casting process can be rapidly provided, the water inlet and outlet flow speed can be predicted according to the liquid level height and the stopper rod position, the parameters of the crystallizer liquid level controller are actively and automatically adjusted, and closed-loop optimization control over the metallurgical process is achieved.

The invention discloses a big data analysis method based on deep learning, and the method comprises the following steps: 1, data collection: obtaining a real-time data stream containing structured data and unstructured data from a production line or a user terminal through a real-time data collection system, carrying out the cleaning and integration of the data stream, and carrying out the collection of the data stream; generating a data stream set in a unified format; step 2, acquiring behavior pattern information; when the method is implemented, parallel processing of structured and unstructured data is supported through real-time data acquisition and a distributed computing framework, and the problem of low data integration efficiency of a traditional method is solved; through combination of the graph neural network and the dynamic data graph, data association change can be captured in real time, a trend can be predicted, and timeliness and accuracy of decision making are improved. And through real-time feedback of decision support information, a dynamic adjustment mechanism of a production strategy is formed, and the adaptability of the system is enhanced.

The application relates to a real-time data acquisition system and method for a motor driver, comprising: a data source and a sending unit, which are used for acquiring operation state quantities of a motor, performing Manchester coding, and obtaining Manchester coding signals; a signal conversion unit, which is used for converting the Manchester coding signals into differential signals and transmitting the differential signals, and performing electrical isolation between a high-voltage side and a low-voltage side through a pulse transformer during the transmission process; and a receiving and decoding unit, which is used for receiving the differential signals, decoding the differential signals in the form of a serial data stream, and restoring the operation state quantities. The application realizes completely transparent monitoring of a control loop of up to 100 kHz without interfering with a motor control algorithm, occupying very few CPU computing powers, and needing no secondary-side isolation power supply, and provides a low-cost and high-performance solution for optimization and fault diagnosis of a motor control algorithm.

This invention discloses a wireless real-time data acquisition system for coal magnetic intensity and marker gas concentration, belonging to the field of coal spontaneous combustion experiment technology. The method includes: mixing dry air with a mixed gas and then introducing it into a vertical tubular furnace; placing a coal sample in the furnace for programmed temperature rise; arranging temperature and magnetic intensity sensors within the furnace and connecting them to a microcontroller unit to collect temperature and magnetic intensity data; after programmed temperature rise and oxidation, the gas enters a flow meter and then flows through a gas concentration sensor; the gas concentration sensor is connected to the microcontroller unit to collect the marker gas concentration; the microcontroller unit communicates with a WiFi module via RS485 or a serial port, and the WiFi module transmits the collected data to a mobile application using the MQTT / TCP communication protocol. This invention solves the problem of wireless real-time acquisition of coal magnetic intensity and marker gas concentration during programmed temperature rise experiments.

The invention belongs to the technical field of intelligent parking guidance, and particularly relates to an intelligent parking space guidance system based on edge calculation, and the system comprises a multi-source real-time data collection system, a distributed edge calculation architecture module, and an intelligent decision core operation module. The system can help a driver to quickly find a parking space and optimize the parking lot management efficiency. According to the system, through full-chain optimization of perception-decision-execution, qualitative change and improvement of the operation efficiency of the parking lot are realized. According to the invention, through full-chain innovation of accurate perception, intelligent decision-making and humanized interaction, transformation of parking lot operation from experience driving to data intelligent driving is realized, new industry benchmarks are established in three dimensions of economic benefit, user experience and social value, and reproducible examples are provided for smart city infrastructure construction.