Multi-parameter Monitoring System Research Articles

A multi-parameter monitoring system and early warning model for rainfall-induced landslides

A multi-parameter monitoring system and early warning model for rainfall-induced landslides

Research of low-cost air quality monitoring models with different machine learning algorithms

Abstract. To improve the performance of the calibration model for the air quality monitoring, a low-cost multi-parameter air quality monitoring system (LCS) based on different machine learning algorithms is proposed. The LCS can measure particulate matter (PM2.5 and PM10) and gas pollutants (SO2, NO2, CO and O3) simultaneously. The multi-input multi-output (MIMO) prediction model is developed based on the original signals of the sensors, ambient temperature (T) and relative humidity (RH), and the measurements of the reference instrumentations. The performance of the different algorithms (RF, MLR, KNN, BP, GA–BP) with parameters such as determination coefficient R2, root mean square error (RMSE), and mean absolute error (MAE) are compared and discussed. Using these methods, the R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the PM is in the range 0.68–0.99; the RMSE values of PM2.5 and PM10 are within 2.36–18.68 and 4.55–45.05 µg m−3, respectively; the MAE values of PM2.5 and PM10 are within 1.44–12.80 and 3.21–23.20 µg m−3, respectively. The R2 of the algorithms (RF, MLR, KNN, BP, GA–BP) for the gas pollutants (O3, CO and NO2) is within 0.70–0.99; the RMSE values for these pollutants are 4.05–17.79 µg m−3, 0.02–0.18 mg m−3, 2.88–14.54 µg m−3, respectively; the MAE values for these pollutants are 2.76–13.46 µg m−3, 0.02–0.19 mg m−3, 1.84–11.08 µg m−3, respectively. The R2 of the algorithms (RF, KNN, BP, GA–BP, except for MLR) for SO2 is within 0.27–0.97, the RMSE value is in the range 0.64–5.37 µg m−3, and the MAE value is in the range 0.39–4.24 µg m−3. These measurements are consistent with the national environmental protection standard requirement of China, and the LCS based on the machine learning algorithms can be used to predict the concentrations of PM and gas pollution.

Design of Crop Irrigation Decision-Making System Based on ZigBee Technology

Abstract: To promote the high-quality development of agriculture, meet the needs of agricultural products grown in greenhouses, and achieve precise monitoring of greenhouse plants, a small greenhouse micro-environment multi-parameter monitoring system was designed and implemented. The system consists of three parts: a monitoring node, a gateway node, and a remote management platform. The monitoring node used the ESP32 microcontroller as the main control chip, combined ZigBee technology, and embedded different sensors to complete the collection and transmission of environmental parameters. In the gateway node, the 4G Data Transfer Unit module was used as the carrier, and the communication protocol was used to realize data communication between the monitoring terminal and the gateway. The remote management platform was based on the PyCharm development platform. It used the PyQt5 graphical user interface (GUI) toolkit to complete the design of the host computer monitoring platform, establish a database, and realize the storage and analysis of environmental parameters. The remote management platform embedded the crop reference evapotranspiration, online calculation model, to provide irrigation decisions for greenhouse crop management and improve the applicability and accuracy of irrigation decisions. After the experimental test platform was built to test the system communication distance, communication reliability, control reliability, and data reliability, the small greenhouse micro-environment multi-parameter monitoring system designed in this paper can operate stably for a long time. Its functions meet the expected requirements and are in line with modern requirements for multi-parameter monitoring of smart greenhouses.

Floor heave mechanism for gob-side entry retaining with concrete blocks and control method: A case study

The issue of significant floor heave deformation in gob-side entry retaining has long been a challenging problem in the context of longwall mining. This paper studies the floor heave failure mechanism and control method for gob-side entry retaining with concrete blocks in Guizhou Faer Coal Mine in China. Based on Rankine’s earth pressure theory, the effective shear stress equation for the plastic slip of roadway floor is established. The deformation mechanism of floor heave in a retaining roadway with a block wall is revealed in this study. The new comprehensive control method is proposed, encompassing roof pre-splitting blasting for pressure relief, reinforcing cables for roof control, double directions control bolts for concrete block, and pliability cushion yielding pressure. FLAC3D numerical calculation model is established, which shows that the new method can effectively reduce the average vertical stress peak value of the entity coal floor by 34.6% and significantly reduce the pressure source causing the roadway floor heave. Besides, a multi-parameter real-time online monitoring system for mine pressure was designed, and field tests were carried out. The results show that the maximum value of roadway floor heave under the new method is 163 mm, reduced by 66.9%, and the roadway floor heave is effectively controlled. These research findings offer a fresh perspective and new ideas for controlling floor heave in mining operations.

Multiparametric Monitoring System of Mt. Melbourne Volcano (Victoria Land, Antarctica).

Volcano monitoring is the key approach in mitigating the risks associated with volcanic phenomena. Although Antarctic volcanoes are characterized by remoteness, the 2010 Eyjafjallajökull eruption and the 2022 Hunga eruption have reminded us that even the farthest and/or least-known volcanoes can pose significant hazards to large and distant communities. Hence, it is important to also develop monitoring systems in the Antarctic volcanoes, which involves installing and maintaining multiparametric instrument networks. These tasks are particularly challenging in polar regions as the instruments have to face the most extreme climate on the Earth, characterized by very low temperatures and strong winds. In this work, we describe the multiparametric monitoring system recently deployed on the Melbourne volcano (Victoria Land, Antarctica), consisting of seismic, geochemical and thermal sensors together with powering, transmission and acquisition systems. Particular strategies have been applied to make the monitoring stations efficient despite the extreme weather conditions. Fumarolic ice caves, located on the summit area of the Melbourne volcano, were chosen as installation sites as they are protected places where no storm can damage the instruments and temperatures are close to 0 °C all year round. In addition, the choice of instruments and their operating mode has also been driven by the necessity to reduce energy consumption. Indeed, one of the most complicated tasks in Antarctica is powering a remote instrument year-round. The technological solutions found to implement the monitoring system of the Melbourne volcano and described in this work can help create volcano monitoring infrastructures in other polar environments.

Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation

Water quality sampling and monitoring are fundamental to water environmental protection. The purpose of this study was to develop a water quality sampling and multi-parameter monitoring system mounted on a multi-rotor unmanned aerial vehicle (UAV). The system consisted of the UAV, water sampling and multi-parameter detection device, and path planning algorithm. The water sampling device was composed of a rotating drum, a direct current (DC) reduction motor, water suction hose, high-pressure isolation pump, sampling bottles, and microcontroller. The multi-parameter detection device consisted of sensors for potential of hydrogen (pH), turbidity, total dissolved solids (TDS), and a microcontroller. The flight path of the UAV was optimized using the proposed layered hybrid improved particle swarm optimization (LHIPSO) and rapidly-exploring random trees (RRT) obstacle avoidance path planning algorithm, in order to improve the sampling efficiency. Simulation experiments were conducted that compared the LHIPSO algorithm with the particle swarm optimization (PSO) algorithm and the dynamic adjustment (DAPSO) algorithm. The simulation results showed that the LHIPSO algorithm had improved global optimization capability and stability compared to the other algorithms, validating the effectiveness of the proposed algorithm. Field experiments were conducted at an aquaculture fish farm, and the device achieved real-time monitoring of three water quality parameters (pH, TDS, turbidity) at depths of 1 m and 2 m. A rapid analysis of three parameters (ammonia nitrogen, nitrite, dissolved oxygen) was performed in the laboratory on the collected water samples, and validated the feasibility of this study.

The impact of portability of wearable monitoring device on study retention and participant experience in patients with unresectable stage III NSCLC treated with standard-of-care durvalumab: Learnings from the ON TRAX pilot study.

e20562 Background: Improvements in technology can prompt device changes during clinical studies. ON TRAX [NCT04381494] was a prospective pilot study aimed to evaluate the potential benefit of using a multiparametric remote monitoring system to detect early signs of pneumonitis in patients with Stage III NSCLC receiving standard of care durvalumab post-chemoradiotherapy. Here we report learnings from this pilot based on post-hoc analyses comparing changes in device adherence, study retention and user experience after upgrading the wearable device mid-study. Methods: Patients were monitored remotely while on durvalumab, for up to 1 year, with an FDA-cleared system (Current Health) that recorded vital signs continuously (SpO2, respiratory rate, pulse, temperature and motion). During the study, the wearable was upgraded from Generation One (G1) to Two (G2), a smaller and lighter (30 vs 115 grams) device with same capabilities. We compared daily device adherence (defined as median number of hours with wearable data transmission per 24h expressed as %) and total study participation time (days) for G1 vs G2 users by Wilcoxon Rank-Sum test. At end-of-study visit, some patients rated on a 5-point Likert scale three statements regarding comfort of wearable, intrusiveness, and overall experience. Results: Following IRB approvals, 40 patients from 16 US sites were enrolled, but only 39 patients with minimum of 1h of wearable data transmitted were included in this analysis. Nine patients used only G1, 22 only G2, while 8 used both. Collectively, the 39 patients transmitted 76,957 hours of data. The median (interquartile range) length of participation was 84 (47-208) days. Transitioning from G1 to G2 did not affect daily device adherence (G1: 79 (46-96) vs G2: 75 (58-100) %, p = 0.9), but patients using G2 exclusively participated 34 days longer than those using G1 exclusively (47 (2-57) vs 81 (50-161) days, p = 0.03). Seventeen patients responded to the user experience survey, of which 13 used only one device (G1 = 3, G2 = 10). Ten of 10 (100%) G2 users ‘agreed’/’strongly agreed’ that the wearable was comfortable, while 2 out of 3 of G1 users ‘disagreed’. Both groups ‘agreed’/’strongly agreed’ it did not impede their daily activities. Overall experience was ‘good’/’excellent’ for 9 of 10 (90%) G2 users, but varied in G1 users (1 ‘good’, 1 ‘neutral’, 1 ‘poor’). Conclusions: Following the introduction of a smaller and lighter wearable device, patients remained a median 34 days longer in this digital health pilot study and reported greater comfort and better overall experience, however the pilot was terminated early. In light of technological updates, researchers must balance consistency with data yield and patient experience. An interim review of technology and its implementation into study protocols is recommended. Clinical trial information: NCT04381494 .

Industrial Drying of Fruit and Vegetable Products: Customized Smart Monitoring and Analytical Characterization of Process Variables in the OTTORTO Project

In the era of digitalization, the process industry is one of the sectors most affected by the need for change. The adoption of IoT-based intelligent monitoring systems for the collection of real-time measurements of energy and other essential operational variables, on one hand, makes it possible to accumulate big data useful for the company management to monitor the stability of the production process over time, and on the other hand, helps to develop predictive models that enable more efficient work and production. The OTTORTO project stems from the need of the FARRIS company to adapt its production line to agriculture 4.0 policies, responding to the higher goals of digitization and technological transition imposed at the national and EU level. The objectives of the current study are (i) to present an “ad hoc” customized intelligent and multi-parameter monitoring system to derive real-time temperature and humidity measurements inside the company’s industrial drying kilns; and (ii) to show how it is possible to extract information from operational data and convert it into a decision support too and an effective knowledge medium to better understand the production process. Studying the correlations between temperature and humidity measurements showed that for most of the observation period, the system was thermodynamically quite stable in terms of major operational risks, such as humidity saturation inside the kilns causing condensation on the products to be dried. However, to remedy the occasional occurrence of such inefficiencies, implementing kilns with the introduction of forced air extraction systems could bring significant benefits in terms of improved energy-environmental performance.

A comprehensive model for evaluating infrared radiation and acoustic emission characteristics of sandstone fracture

The rock failure precursor can be used effectively for monitoring purposes and execution of underground engineering projects in a better way. To effectively determine the rock failure precursor, it is essential to consider the discreteness of the data, which researchers have not considered during the determination of rock failure precursors in the presence of infrared radiation and acoustic emission. Therefore, in this research study, the biaxial experiments of sandstone under different lateral stresses are carried out in the presence of infrared radiation and acoustic emission using the self-designed loading rock multi-parameter monitoring system. The characteristics of acoustic emission and surface infrared radiation of loaded sandstone are explained. A new index of infrared radiation of high-temperature point scale factor and cumulative high-temperature point scale factor amplitude is proposed. A comprehensive evaluation model of acoustic and thermal damage in the process of sandstone loading and fracture is constructed using the principal component analysis method based on the stress, cumulative ring count, and average infrared radiation temperature after effective denoising. The sandstone probability function of damage in the process of loading and fracture is defined, and the quantitative analysis of the influence weight of each acoustic and thermal index on sandstone fracture is defined. On this basis, a new method to determine the failure precursor of sandstone based on the first derivative of the acoustic thermal comprehensive evaluation model is proposed. This overcomes the discrete analysis defect of acoustic emission and surface infrared radiation information of loaded sandstone.

A new digital surface crack image monitoring technology for coal failure

AbstractThe experimental study on the variation law of coal fracture and stress was carried out in the laboratory and engineering fields, respectively. A multiparameter monitoring system including electromagnetic radiation and a high‐speed camera was built, and three stress paths, uniaxial compression, cyclic loading, and graded loading, were used to monitor the dynamic expansion process of surface cracks during uniaxial compression failure of coal specimens. Through the quantitative analysis of the electromagnetic radiation signal in the crack propagation process, it is found that the electromagnetic radiation and the stress change trend are consistent, and the electromagnetic radiation signal is ahead of the failure of coal‐generating rock mass 1–2 s. The surface crack changes after the peak value of electromagnetic radiation and presents a stepwise growth trend, crack length changes on the millisecond time scale, and the crack propagation speed is about 2000 mm/s. The surface cracks appear when the stress reaches a certain degree, and the propagation of the principal cracks is consistent with the failure of the specimen. The electromagnetic radiation value of the coal mass from static period to dynamic is analyzed by using electromagnetic radiation at the engineering site, and it was found that the electromagnetic radiation is consistent with the stress distribution of the mining face. Through normalization, the variation rule of electromagnetic radiation in laboratory and engineering sites is similar, but the peak value of electromagnetic radiation in engineering sites is more significant. Therefore, electromagnetic radiation has a good monitoring effect on the stress distribution and cracks propagation of underground coal mining working faces, which could guide the layout of underground drilling.

A Decade from the Costa Concordia Shipwreck: Lesson Learned on the Contribution of Infrared Thermography during the Maritime Salvage Operations

On 13 January 2012, the Italian vessel Costa Concordia wrecked on the shore of Giglio Island, about 15 km off the coast of southern Tuscany (Italy), causing the loss of 32 lives. It is considered one of the worst disasters in maritime history. Salvage operations started immediately after the wreck with the coordination of the Italian National Civil Protection Department and the technological support of several Research Centers, which were activated for the management of the consequent emergency phase. A multi-parametric and multiplatform monitoring system was promptly implemented, involving several advanced remote sensing techniques, among which was Infrared Thermography (IRT). In this framework, IRT monitoring was performed during a 35-day period (25 January–1 March 2012), using a terrestrial, hand-held thermal camera. Six different thermal images were acquired daily from the island’s coastline in three different positions, both in daylight and night times. The aim was to detect thermal anomalies connected to possible deformations of the vessel and oil spills. Between 3–4 February, IRT successfully revealed on oil spill drifting from the stern of the wreck towards the island harbor. Furthermore, the wreck’s thermal dilatation was also analyzed during a 24-day close-range monitoring, providing interesting insights for the interpretation of the deformation monitoring results. This paper presents the outcomes of these innovative and experimental monitoring activities, with the aim of testing the potential of IRT as a versatile and operative tool to be used in maritime and environmental disaster response.

Responses of well-aquifer system to four earthquakes and implication for contaminant transport in Changan landfill, China

Evaluation of aquifer response to earthquakes is important to understanding the evolution of aquifer properties and contaminant transport. In Changan Landfill, a multi parameter monitoring system was installed to collect data every 12 h. Principal Component Analysis (PCA), energy density and wavelet analysis, and tidal analysis were used to reveal the response mechanisms to four earthquakes, sensitivity of various parameters to seismic stimuli, and permeability evolution, respectively. The results showed that the chemical characteristics of two aquifers were distinct. The shallow aquifer was polluted by leachate, and the deep aquifer was less polluted. Coseismic responses were dominated by the deep aquifer and the relative contribution of each aquifer remained stable. Coseismic chemical changes were dominated by the opening and closing of fractures, and the long-term evolution was controlled by seasonality. The deep aquifer had a higher sensitivity to seismic stimuli than the shallow aquifer. Tidal analysis showed that a general permeability decrease during this monitoring period reduced contaminant transport, but some contaminants crossed the aquitard between the two aquifers after the Zizhong earthquake, due to an increase in vertical permeability.

Well head multi parameter data monitoring system based on LoRa

Oilfield wellhead data is an important indicator to reflect the normal operation of oil well equipment. This paper monitors the data of standpipe pressure, hanging weight, torque and other parameters, which is helpful to improve the safety and production efficiency of the entire oilfield. Therefore, this paper proposes a set of multi parameter monitoring system based on LoRa to further explore and study in order to reduce oilfield accidents.

Real Time In Vivo Evaluation of Mitochondrial Activity and Brain Functions during Ischemic Stroke

More than 50% of total energy consumed by the brain is utilized by active transport processes which are responsible for keeping the ionic homeostasis in the brain. Under an ischemic condition, energy availability is limited, and, as a result, inhibition of the ion pumps is unavoidable. The initial consequence of such inhibition is a gradual accumulation of K+ in the extracellular space leading to a second phase of the ischemic depolarization phenomenon. During ischemic depolarization, extracellular K+ will increase 15-20-fold, while extracellular Ca2+ is decreasing 10 fold. Another optional effect of mild ischemia is the development of Cortical Spreading Depression due to the leakage of K+ into the extracellular space. The Mongolian gerbil provides a very useful animal model to study the effects of ischemia on brain functions. The aims of the study were as follows: (1) To elucidate the mechanism behind the development of ischemic depolarization or cortical spreading depression under unilateral and bilateral carotid artery occlusion. (2) To correlate the kinetics of the recovery processes to the level of ischemia. We tested the correlation between energy depletion level (evaluated by intramitochondrial NADH redox state and Cerebral Blood Flow) and the development of ischemic depolarization or cortical spreading depression (evaluated by extracellular K+, H+, Ca2+, DC potential and 366 nm reflectance changes) under partial and complete ischemia using the multiparametric monitoring system. The results could be summarized as follows: (1) Under bilateral occlusion, in all gerbils the ischemic depolarization was recorded within 1-2 min. (2) Under unilateral occlusion, the level of ischemia obtained was significantly smaller and led to the ischemic depolarization in about 60% of the gerbils. (3) The K+ leakage during the ischemic depolarization had an 'all or none' nature in terms of maximal K÷ levels and time to reach it. (4) The main effect of various lengths of bilateral occlusion was on the recovery time of extracellular K+ level. (5) Cortical spreading depression develop in most cases during the recovery from the ischemic event when ischemic depolarization was not recorded under the ischemic episode.

Reliability analysis of multi-parameter monitoring systems for Intensive Care Units

Multi-parameter monitoring systems in Intensive Care Units (ICUs) monitor the clinical condition of critical state patients. These Systems of Systems (SoS) comprise a set of Constituent Systems (CS) to measure parameters such as heart rate, respiratory frequency, and temperature. Due to the critical nature and relevance of ICUs, such SoS shall be as reliable as possible. That is especially true in emergencies, as the COVID-19 outbreak that resulted in the burden of health care systems. We developed a modular and parametric model to perform reliability analysis and to provide insights to assist the management of multi-parameter monitoring systems used in ICUs, also considering maintenance. First, we modeled a multi-parameter monitoring system for ICUs using the CHESS methodology and modeling language. Afterward, we performed a reliability analysis using the CHESS state-based analysis plugin for different scenarios. We identified that the main power supply and the battery are the CS that present the most negative impacts on reliability. In emergencies, reduced time ranges of planned maintenance, when applied during a short period, showed to be promising strategies.

Experimental field study on the fatigue and failure mechanisms of coastal chalk cliffs: Implementation of a multi-parameter monitoring system (Sainte-Marguerite-sur-Mer, France)

Between November 2018 and January 2020, a continuous multi-parameter survey, using nine types of sensors, was carried out on a coastal chalk cliff in Sainte-Marguerite-sur-Mer (Normandy, France) with the objective of gaining a deeper understanding of the forcing agents and processes that lead to cliff fatigue and failure. This paper will present the survey instrumentation, the results on the internal characteristics of the chalk massif, initial results (from November 2018 to March 2019) of the thermal subsurface behaviour along the cliff face, and the analyses of the observed cliff-top ground motion and the movement of existing fractures on the cliff face in relation to forcing agents. Our main results show that 1) the magnitude of cliff-top displacement on this coastal chalk cliff is consistent with prior studies conducted in different settings showing rather high displacement amplitudes (up to 50 μm in relatively calm conditions) likely to be related to chalk elasticity; 2) the displacement on existing fractures is partly controlled by the tidal amplitude, with a threshold response, but not only. Statistical analyses help the identification of other controls. The processing of the entire dataset from November 2018 to January 2020 with a combined analysis of multiple sensors' output is expected to provide further insight on cliff fatigue and failure.

Monitoring of clinical signs of intravenous infusion patients with ZigBee wireless technology

At present, intravenous infusion in clinical medicine is a very important treatment method in medical care, and it is the main work content of medical staff to ensure the safety and effectiveness of intravenous infusion process. The monitoring system of infusion patient’s physical signs is integrated with sensor technology, bio-electronic technology, and computer network technology. By means of sensor detection, program control and data processing, the automatic detection and control of intravenous infusion process and real-time monitoring of the pulse information are realized. The monitoring equipment has been unable to adapt to the rapid development of medical technology. In this article, based on ZigBee module, automatic control of drip rate, abnormal alarm, real-time liquid crystal display of infusion progress, and other functions are realized without manual intervention. At the same time, the hardware part of the lower computer combines the detection of ECG and pulse to design a multi-parameter monitoring system of vital signs, and realizes the parameter synchronization management requirements of the monitoring terminal based on the local area network. The test proves that the research content in this article is higher than the traditional clinical management mode in terms of monitoring data consistency and data synchronization management accuracy, which can not only reduce the labor intensity of nursing staff, improve work efficiency, but also greatly reduce the potential risks in the nursing process, which is the future application trend of clinical medical nursing work.

Early assessment of acute ischemic stroke in rabbits based on multi-parameter near-field coupling sensing

BackgroundEarly diagnosis and continuous monitoring are the key to emergency treatment and intensive care of patients with acute ischemic stroke (AIS). Nevertheless, there has not been a fully accepted method targeting continuous assessment of AIS in clinical.MethodsNear-field coupling (NFC) sensing can obtain the conductivity related to the volume of intracranial components with advantages of non-invasiveness, strong penetrability and real-time monitoring. In this work, we built a multi-parameter monitoring system that is able to measure changes of phase and amplitude in the process of electromagnetic wave (EW) reflection and transmission. For investigating its feasibility in AIS detection, 16 rabbits were chosen to establish AIS models by bilateral common carotid artery ligation and then were enrolled for monitoring experiments.ResultsDuring the 6 h after AIS, the reflection amplitude (RA) shows a decline trend with a range of 0.69 dB and reflection phase (RP) has an increased variation of 6.48° . Meanwhile, transmission amplitude (TA) and transmission phase (TP) decrease 2.14 dB and 24.29° , respectively. The statistical analysis illustrates that before ligation, 3 h after ligation and 6 h after ligation can be effectively distinguished by the four parameters individually. When all those parameters are regarded as recognition features in back propagation (BP) network, the classification accuracy of the three different periods reaches almost 100%.ConclusionThese results prove the feasibility of multi-parameter NFC sensing to assess AIS, which is promised to become an outstanding point-of-care testing method in the future.

Design and Analysis of Industrial Automation using Speech Recognition with Wireless Technology

Today, Internet is the best place to communicate and share information among the people throughout the world and gives an endless support of knowledge and entertainment. The main objective of Internet technology is to increase efficiency and decrease human effort. With the introduction of Internet of Things (IoT) in the last decade, we have been pushing for ubiquitous computing in all spheres of life. Physically challenged people are also using the Internet with the help of Speech commands (SC). The main objective of this paper is to minimize the effort and increase efficiency of the Voice recognition and IoT based secured automation, To design multi parameter monitoring system using Microcontroller that measures and controls various global parameters and the system comprises with wireless mode of communication. These processes were managed using Arduino and Bluetooth. The parameters that can be tracked are temperature, humidity and fire. Here we design the automatic system for voice based industrial automated system. That is an android app is used to give input data as voice input to the micro controller. In the micro controller section there will be a Bluetooth module to receive the data transmitted. Once the data is received the coding is executed according to the data which the controller got. Here we can monitor the temperature, humidity and gas and fire value of the industry and according to the values the prevention action is taken. The motor, dc fan and the light is turned on and off according to the voice input. And the action done here is implemented to the IOT.

Analysis of technical measures for controlling instability of surrounding rock in jointed rock masses

The development and utilization of underground space is an inevitable choice for the development of human society, economic construction, and national security strategy. The failure mechanism of the chamber in underground rock mass engineering is extremely complex, and its stability analysis and control have always been the key problems in the field of rock mechanics. In practical engineering, due to the complexity of the structural plane shape, unreasonable layout, increasing buried depth, and great disturbance of underground rock engineering, the stress concentration in the surrounding rock is intensified, the surrounding rock is extremely sensitive to the change of external load, and the stability state is prone to mutation, resulting in the instability and failure of the chamber structure. In this article, based on the previous theoretical research results and engineering practice experience, the instability control theory of surrounding rock of jointed rock masses on the bases of four basic principles of “consolidating surrounding rock joints, improving surrounding rock strength, restoring and improving stress state, and weakening dynamic disturbance source strength” was proposed. On this basis, to maintain the stability of the surrounding rock of the chamber, the chamber support form of “grouting reinforcement + full-face bolt (mesh) shotcrete combined support + local strengthening support + constructing impact buffer structure + reducing engineering blasting disturbance” was put forward. Moreover, the technical measures for controlling the instability of the surrounding rock of the chamber in jointed rock masses were established, and the technical scheme of joint application and step-by-step implementation was suggested. In addition, the principle of considering the safety and economy of support and the necessity of establishing a multi-parameter real-time joint monitoring and early warning system were discussed. The research results can provide a certain reference for the stability control of surrounding rock in jointed rock masses in similar projects.