해양플랫폼의 거동 모니터링 시스템 분석

Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.

This paper presents the capabilities and operation of a logging-while-drilling (LWD) fluid analysis and sampling tool in a deep-water application in the Gulf of Mexico. The operation was performed during a drilling run in a high-pressure/high-temperature (HPHT) well with an expected downhole pressure of up to 22,000 psi and 300°F downhole temperature. This paper will show how a robust fluid analysis and sampling campaign was planned and executed, matching the various objectives and technical requirements with the appropriate technology. The challenges and opportunities of LWD sampling will be discussed, especially under tough environmental conditions. The advantages of LWD sampling systems are well known, such as shorter pump-out time due to less invasion and the ability to capture reservoir fluid samples in extended reach drilling or highly deviated wells, which provides a new application range compared to current wireline systems. As the harsh drilling environment generates severe shocks and vibration, it requires precaution in the tool design and the selection of suitable components. The influence of the downhole dynamics on the reliability and durability of the system needs to be considered. In response to these challenges, the new LWD tool incorporates an electro-mechanical driven drawdown pump that further improves the LWD fluid analysis and sampling service. This enables a nearly autonomous operation with the assurance that fluid phase integrity is being maintained. Automation allows optimal use of the available bandwidth to deliver the most complete set of fluid property data in real time for efficient decision making, including density, viscosity, sound speed and refractive index. It enables the operator to monitor the fluid identification (fluid ID) trend carefully and in real time, even from remote locations. The sampling process is performed shortly after the hole is drilled and is therefore subjected to different levels of invasion and contamination arising from the effects of the drilling fluid and the reservoir properties. It will be discussed how this effects the clean-up and the ultimately achievable contamination level. The newly introduced compressibility value derived from the electro-mechanical pump offers a bulk measurement, where localized sensors observe scattered data. Examples from the application in the Gulf of Mexico (GOM) will be shown and discussed. An outlook will be given how this technology evolves in future developments and how the operator and oil company can benefit from the new technologies in the drilling environment.

The ability to monitor data in real time has become increasingly important as the industry continues digitalization efforts. Over the past several years this contractor has upgraded the capabilities of a real time monitoring system that has been providing value through improved operational efficiency and visibility of operations across its onshore fleet. These updates open a wide set of alerting opportunities that were previously not possible by distributing the alerting system to local, rig-based devices. An overview of the technology and architecture necessary to make this system possible, as well as the impact on performance will be discussed. This contractor's efforts have focused on converting the system from a cloud-based system to an edge-based system, with all computations and alerts issued at the rig location. The architecture comprises migrating cloud applications to run on the edge and leveraging edge hardware and rig historians to enable alerting on all data available at the rig location. Streamlining and synchronizing the rules/alert definitions and notifying users instantly using cloud infrastructure is a novel implementation. Housed within the edge platform, the system can operate on batches of data in real time without being held back by cloud computing related latency. Preexisting infrastructure made it possible to receive exception-based alerting within 15 minutes of an event occurring. With the new implementation running on the edge, it is possible to receive alerts to the user's phone, email and/or web-based dashboard within a minute. Leveraging increased real-time monitoring capabilities, this contractor has been able to reduce the amount of premature equipment failures as well as improve drilling connection operations. These successes have resulted in a reduction in capital spend and improved spud to release times. The above will be reviewed in detail. This change in data architecture from a cloud-based to a distributed rig-based computing system provides a more efficient way to trigger alerts and opens the possibility of generating alerts locally at the driller's console. Previously, the cloud-based alerting system only had access to data streamed off-location, providing limited insight into equipment health and drilling conditions. As a result of this update, we can provide end users with alert notifications containing detailed messages and specific data from a wide range of local data sources, including thousands of equipment-related data feeds, downhole data, complex artificial intelligence(AI)/machine learning(ML) model outputs, and drilling performance metrics which were not previously synchronized into a system that allowed alerts to be generated across this entire dataset. Quicker computing and notification delivery times will pave the way for well control, drilling dysfunction and more sophisticated equipment health alerts on the driller's console in real time.

The monitoring performance of the photovoltaic system in real time is required for estimation and optimization purposes. This monitoring system has been the subject of extensive research, but in general it employs costly commercial software and as well as other license-based software. This paper discusses the design of a photovoltaic parameter monitoring system that uses Internet of Things technology to monitor in real time. This software is intended to be as interactive as possible in order to display all measurement data in the form of graphical according to a user-specified time interval. The constructed system is tested to display the measurement parameters of the fixed and dual axis solar tracker. The test results indicate that the system can display data in real time. Every data shown is accessible from any location and at any time, so long as the device being used is connected to the Internet. This system obviously can be applied and developed for an internet of thing-based photovoltaic parameter monitoring system to facilitate system estimation and optimization.

Monitoring of learning activities has a very important role to improve the quality of education in University. The research aims to develop a monitoring system of learning activities in real time by using simple additive weighting and radio frequency identification technology.A simple additive weighting method is used to determine the weighting of each criterion involved in the system, while radio frequency identification technology is used electronically to identify and store learning activity information. Monitoring system built using learning activities data in the room and lecturer activities using radio frequency identification tag in real time. The results of the system include the recording of monitoring data of lecturing activities that produce lecturer attendance reports on each class in real time, thus making efficient the time, effort and cost. The system also produces a ranking of lecturer discipline on each of the criteria that gives the difference of outcomes among the consistent lecturers present on schedule with inconsistent lecturers present on schedule. The results of monitoring of lecture activities can be used for university leaders as a decision-making material quickly based on actual data in real time to improve the quality of the learning process.

This paper describes the results of a field test, conducted at a test rig, of a hole cleaning and wellbore stability monitoring system. This system encompasses a laser-based sensor and digital tools designed to process the collected data in real time. Additionally, this paper outlines the method for interpretating the processed data, including the combination of the estimated volume of cuttings recovered at the surface and the expected volume obtained with a transient cuttings transport model. The test was conducted while drilling three borehole sections at a test location in Germany. The preparation for the test included the installation of an auger system to divert the cuttings from three active shale shakers to the cuttings sensor. This sensor is equipped with a high-definition camera and a laser profile scanner, enabling the collection of both 2D images and point-cloud data of the cuttings stream. This data was then fed into a digital tool capable of estimating the solids’ volume and morphological characteristics, identifying cavings and their shape and size, and providing an evaluation of the wellbore condition. The test revealed that the system can be successfully deployed in the field with a simple rig-up procedure. Additionally, it showed that the digital tool and the underlying machine-learning techniques can accurately estimate the solids’ volume and their morphological characteristics. Furthermore, a minor discrepancy between the measured volume and the expected volume according to a transient cuttings transport model showed that the sensor can reliably be used to assess hole cleaning sufficiency in real time. During these tests, artificial cavings were introduced to evaluate the system's capability of cavings detection and characterization, the latter involving the shape of cavings which is related to the wellbore failure mechanism that created them. The results indicate that the system can be used to evaluate the in-depth nature of wellbore stability in real time. This, in turn, will allow for targeted corrective action to deal with stability-related operational problems. Finally, we also identified limitations of the current system, such as the generation of nonoptimal segmentation results when the cuttings’ size ranges from medium to coarse sand size (0.25–1 mm). Based on these limitations, we have defined a plan for future improvement, further described in detail in this paper. Previous field testing of the sensor only evaluated the sensor's readiness for deployment and the software's capability to reliably measure bulk volume—largely without correction for void space. The tests presented in this paper are more comprehensive. They evaluated the system's capabilities to measure the corrected solids’ volume, their morphological characteristics, and the integration of a transient cuttings model into the system for a holistic assessment of the well condition.

Aiming at the shortcomings of poor real-time performance, low sensitivity and high cost of traditional water quality detection systems, an intelligent water quality monitoring system based on STM32 microcontroller and various water quality sensors is designed and implemented. The system can monitor surface water temperature, pH, dissolved oxygen, turbidity, conductivity and other water quality parameters in real time, display and store water quality data in real time, and finally transmit the data to the remote monitoring center through the GPRS communication module. The research shows that the system has good practicability.

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas

With the development of industry in our country, the industrial pollution becomes more and more serious. In order to reduce the environmental pollution in the industrial production process, the environmental protection department should real-time monitor the use of the pollution control equipment in the sewage enterprises, to solve this problem, a monitoring system of environmental protection power consumption based on OneNET cloud platform is designed. The monitoring system is designed by Modular rocket and is divided into power acquisition terminal, data collection and processing and cloud platform. ATT7022 three-phase electric metering chip is used to collect the electric quantity of the electric quantity collection terminal, then the data is processed by STM32F103 chip, and the data is sent to Lora Intelligent Gateway through Lora module to collect the data, send it to your PC via WiFi or 4G, and then display the data in real time via a visualization program on the cloud.

The spatially modulated imaging polarimeter is a compact, static and simultaneous polarization imaging system which can collect the interferometric data in real time. By processing the interferometric data, the complete polarization information of the incident light can be demodulated to realize the polarization detection. Based on the device principle of the spatially modulated imaging polarimeter using Savart Plates, the interference pattern processing method in the frequency domain was analyzed. Then, the physical mechanism of the modulation was explained and its corresponding processing method in the spatial domain was analyzed. Finally, in order to evaluate and compare the two methods, the simulation was performed and a prototype of the polarimeter was developed to finish the field experiment. The experimental results show that the spatial processing method can effectively extract the polarization information from the interference pattern. It can identify the artificial targets more clearly from the natural background compared with the frequency-domain processing method.

A new numerical procedure for the study of finite temperature quantum dynamics is developed. The method is based on the observation that the real and imaginary time dynamical data contain complementary types of information. Maximum entropy methods, based on a combination of real and imaginary time input data, are used to calculate the spectral densities associated with real time correlation functions. Model studies demonstrate that the inclusion of even modest amounts of short-time real time data significantly improves the quality of the resulting spectral densities over that achievable using either real time data or imaginary time data separately.

Real-time monitoring of oxygen content in fish ponds is of great significance for the intelligent management of fish ponds. This paper proposes a method to realize multi-point real-time monitoring of Dissolved Oxygen content in fish ponds by using Internet of Things technology. The system is mainly composed of three parts: measurement circuit, control circuit and data processing module. The measuring circuit completes the real-time measurement of the Dissolved Oxygen content of the fish pond; the control circuit can complete the oxygen-filling operation according to the set value of the oxygen content; the data processing module can record the historical data in real time and complete the cloud processing of the data and related background operations. After testing, the system can effectively realize real-time monitoring of Dissolved Oxygen content in multi-point fish ponds and control the cost of aerators, thereby effectively reducing losses and increasing production.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

Filtergraph: An interactive web application for visualization of astronomy datasets

Research interface on a programmable ultrasound scanner