Utilization Rate Of Equipment Research Articles

Optimal Configuration of Electricity-Heat Integrated Energy Storage Supplier and Multi-Microgrid System Scheduling Strategy Considering Demand Response

Shared energy storage system provides an attractive solution to the high configuration cost and low utilization rate of multi-microgrid energy storage system. In this paper, an electricity-heat integrated energy storage supplier (EHIESS) containing electricity and heat storage devices is proposed to provide shared energy storage services for multi-microgrid system in order to realize mutual profits for different subjects. To this end, electric boiler (EB) is introduced into EHIESS to realize the electricity-heat coupling of EHIESS and improve the energy utilization rate of electricity and heat storage equipment. Secondly, due to the problem of the uncertainty in user-side operation of multi-microgrid system, a price-based demand response (DR) mechanism is proposed to further optimize the resource allocation of shared electricity and heat energy storage devices. On this basis, a bi-level optimization model considering the capacity configuration of EHIESS and the optimal scheduling of multi-microgrid system is proposed, with the objectives of maximizing the profits of energy storage suppliers in upper-level and minimizing the operation costs of the multi-microgrid system in lower-level, and solved based on the Karush-Kuhn-Tucker (KKT) condition and Big-M method. The simulation results show that in case of demand response, the total operation cost of multi-microgrid system and the total operation profit of EHIESS are 51,687.73 and 11,983.88 CNY, respectively; and the corresponding electricity storage unit capacity is 9730.80 kWh. The proposed model realizes the mutual profits of EHIESS and multi-microgrid system.

Study on recovery and utilization of cold energy for insulation design of liquid hydrogen tanks

Insulation design is one of the key technologies for Liquid Hydrogen (LH2) tanks. To enhance the insulation efficiency and energy utilization of the LH2, this study proposes a novel approach to enhance the insulation performance of LH2 tank and cold energy cascade utilization of LH2. Three Cryo-cooling Insulation Systems (CIS) with various insulation schemes are designed, analyzed and compared from the perspectives of insulation performance, consumption and energy utilization efficiency. An optimal insulation scheme among three CISs is identified and evaluated. Additionally, a dimensionless number (Ψ) is proposed to assess the insulation advantages of the CISs. And the insulation performance and economic feasibility of the system be effectively balanced. The findings reveal that the insulation performance of the proposed CIS outperforms previous insulation methods by a factor of 239.13. Among the schemes in three CISs, Schemes 2 (Position of CSs at 6th, 25th and 35th layers), Schemes 3 (Position of CSs at 10th, 40th and 45th layers), and Schemes 3 (Position of CSs at 3rd, 40th and 45th layers) exhibit superior insulation performance in Cases #1 (CIS of LN2-LAr), Cases #2 (CIS of R14-R170), and Cases #3 (CIS of R1150-R23). Notably, Case #1 achieves the highest insulation performance, showing improvements of 2.58 and 1.63 times over Case #2 and Case #3, respectively. However, the energy output in Case #1 is approximately 248.12 W, constituting 83.10 % and 71.60 % of Case #2 and Case #3, respectively. Case #1 demonstrates the highest specific energy output, with improvements of 30.12 % and 81.05 % compared to Case #2 and Case #3, respectively. In terms of the exergy utilization rate of each equipment, the exergy utilization rate of equipment in Case #1 is the highest among the three cases. The study underscore the feasibility of cold energy utilization for insulation, providing solutions for energy conversion and thermal management of LH2 tanks through cryo-cooling cascade utilization.

(Invited) Chemical Mechanical Planarization Integrated Data Collaboration: Uncover Hidden Interactions for Predictive Maintenance and PCN

This article introduces an innovative analytical methodology that employs machine learning to optimize and predict consumable impacts for Chemical Mechanical Planarization (CMP). By harnessing fleet CMP equipment performance and individual equipment characteristics, our proposed approach aims to elevate wafer-level quality, maximize equipment utilization rates, and minimize unnecessary downtime. CMP, a multifaceted process, its performance is often influenced by strong interactions from its critical components including slurry, pad, machine recipe, and dresser usage. This requires control beyond hardware advancements, especially when equipment is pushed to its limits. Therefore, the demand for higher precision on-wafer performance mandates a data collaboration with sophisticated analytical strategy. In addition, a digitalized product change notification (PCN) concept is also introduced to demonstrate effectiveness of applying integrated data analytics collaboratively across semiconductor chain to accelerate material manufacturing process changes, minimizing impacts on wafer results and shortening the learning cycle and ramp time to full production.Our study leverages data analytics to explore differences between equipment within the same fleet of similar processing. Engineers can deploy a process based on the historical performance of specific equipment, supported by a model trained on data from the same fleet. Furthermore, our approach enables each equipment to predict its own fingerprinted consumable maintenance, accurately forecasting the maintenance time for consumable changes. This concept is equally applicable for consumable PCN migrations.The first use case is to demonstrate the use of integrated data for fleet matching of CMP equipment and/or consumable batches running similar processes. This is particularly crucial as the demand for process precision increases and the need to reduce post-CMP result variability in removal rates and wafer-level uniformity intensifies. This approach could add proactive data driven strategy for process engineers who traditionally concentrate on the removal rate, a highly influential factor, with the standard deviation of platen and machine data identified as a key parameter.The second use case employs heatmaps analysis and proposes scatter plots as an intuitive method to comprehend intricate connections between average removal rates and various parameters. Machine learning is particularly advantageous in this context, as no single parameter plays a dominant role. Preliminary observations suggested, for example that dresser and pad characteristics may have compounding impacts to the wafer level performance.In summary, this article provides a roadmap for in-depth CMP data analysis fed into machine learning, aiming to unravel trends and influential factors that can enhance process optimization and shorten the PCN qualification timeline to meet the increasing demands for CMP process precision and efficiency in High Volume Manufacturing (HVM).

Energy services for solar PV projects: Exploring the accessibility and affordability of clean energy for rural China

China's rural solar photovoltaic projects (SPVPs), as an innovative initiative to improve livelihoods through clean energy, have played a positive role in poverty reduction. However, whether this measure can enhance energy services for rural families remains unconfirmed. This paper employs a quasi-experimental method from the perspectives of energy service accessibility and affordability to quantify the net contribution of China's rural SPVPs to enhancing family energy services. The results indicate: (1) SPVPs can significantly improve family energy services, with a net effect of 14.7 % on the energy service accessibility and a net effect of 44.5 % on the energy service affordability. (2) SPVPs can increase the energy consumption capacity, energy supply capacity and energy equipment utilization rate by 2.6 %, 7.1 % and 5 %, respectively. Furthermore, these initiatives can effectively slash the energy costs of rural families by 4.3 %, bolster the energy access affordability by 3.9 %, and catapult energy investments by an impressive 44.9 %. (3) These projects also perform well in the family energy consumption cleanliness. (4) The effect of SPVPs in providing energy services presents a regional difference, with the best performance observed in ‘Type III’ solar resource areas. Meanwhile, the implementation of China's rural SPVPs does not involve “elite capture.”

Analysis on personal protection in occupational population at high risk for brucellosis and influencing factor in China

Objective: To understand the current status of personal protection in occupational population at high risk for brucellosis in China and provide evidence for the evaluation of implementation of National Brucellosis Prevention and Control Plan (2016-2020). Methods: Four counties in Shanxi Province and Xinjiang Uygur Autonomous Region were selected to conduct a questionnaire survey in occupational population at high risk for brucellosis from December 2019 to July 2020 by using cross-sectional survey methods. Results: A total of 2 384 persons at high risk for brucellosis were surveyed, and the standardized utilization rate of personal protective equipment (PPE) was 20.13% (480/2 384). The utilization rate of glove, mask, rubber shoe, and work cloth were 38.26% (912/2 384), 31.80% (758/2 384), 32.01% (763/2 384) and 30.87% (736/2 384),respectively. There were significant differences in the utilization rate and standardized utilization rate of the four types of PPE among populations in different age, occupation, educational level and area groups (all P<0.001). The utilization rate and standardized utilization rate of PPE were lower in people over 60 years old, women, farmers, and those with lower educational level. The results of multivariate analysis showed that occupation and area were the influencing factors for the standardized utilization of PPE, the standardized utilization rates of PPE were higher in herdsmen and veterinarians. The standardized utilization rate of PPE in Yanggao County and Huocheng County was significantly higher than that in Zuoyun County and Hunyuan County. Conclusions: The utilization rate of the four types of PPE in occupational population at high risk for brucellosis was not high in China, and the standardized utilization rate was low, lower than the requirement in National Brucellosis Prevention and Control Plan, and there were significant differences among different areas. It is urgent to distribute PPE to occupational population at high risk for brucellosis and carry out health education about PPE utilization. Meanwhile, it is necessary to strengthen information exchange or sharing among different areas.

Toward a comprehensive life cycle aquatic ecotoxicity assessment via machine learning: Application to coal power generation in China

Life cycle aquatic ecotoxicity assessments currently lack characterization factors due to the limited availability of experimental toxicity data. In this study, this problem was solved by designing a rapid and simplified approach using machine learning algorithms. The results were further tested and validated via a case study of coal power generation in China that has been implemented since 1990. Compared with the predicted characterization factors of life cycle aquatic ecotoxicity, the predicted average ecotoxicity values (logEC50) are clearly correlated (R2 = 0.597 and RMSE = 0.64) with the reported value in USEtox database based on chemicals' physicochemical properties, degradation rates, and bioaccumulation factors in fish. Approximately 13.6% of the ecotoxicity impact of China's coal power generation is underestimated due to the missing characterization factors of chemicals. In the case study, the predicted ecotoxicity value of the newly identified essential substance via the XGBoost algorithm for the case study was close to the actual value based on the experimental data. Intensifying atmospheric pyrene and chrysene control during the coal power generation stage, increasing the utilization rate of thermal power equipment, and optimizing coal consumption efficiency are efficient approaches for reducing the overall aquatic ecotoxicity from coal power generation in China. The proposed approach can rapidly predict ecotoxicity values and calculate missing characterization factors. Consequently, life cycle aquatic ecotoxicity assessments can be improved, and management strategies can be developed.

Symmetric Two-Workshop Heuristic Integrated Scheduling Algorithm Based on Process Tree Cyclic Decomposition

The existing research on the two-workshop integrated scheduling problem with symmetrical resources does not consider the complex product attribute structure and the objective situation of plant equipment resources. This results in the prolongation of the product makespan and the reduction of the utilization rate of the general equipment in the workshop. To solve the above problems, a two-workshop integrated scheduling algorithm based on process tree cyclic decomposition (STHIS-PTCD) was proposed. First, a workshop scheduling scheme based on the sub-tree cyclic decomposition strategy was proposed to improve the closeness of continuous processing further. Second, an operation allocation scheme based on the principle of workshop processing balance was presented. On the basis of ensuring the advantages of parallel processing, it also effectively reduces the idle time of equipment resources and then optimizes the overall effect of the integrated scheduling of both workshops. Through the comparison and analysis of all the existing resource-symmetric two-workshop integrated scheduling algorithms, the scheduling effect of the proposed algorithm is the best.

Application of PLC-Based Spectrophotometric System Nitrogen Protection Device to Automated Direct Measurement of Target Substances in Zinc Hydrometallurgy

Due to the fast material reaction in zinc hydrometallurgy, the traditional national standard photometric method cannot capture the characteristic information of target substances in real time. Herein, a nitrogen protection device is built based on ultraviolet spectrophotometry, supplemented by a programmable logic controller (PLC), to form an automatic control system for the direct detection of target substances (SO42−, Pb2+ and S2−) in zinc hydrometallurgy. The baseline straightness comparison results show that the nitrogen atmosphere can effectively improve the stability of the instrument. Furthermore, the detection sensitivity of SO42−, Pb2+ and S2− under the nitrogen atmosphere is higher than that of the air atmosphere, manifesting in sensitivity increases of 16.23%, 18.05% and 17.91%, respectively. Additionally, devices based on PLC systems show advantages over manual control both in states feedback and information backtrack. Moreover, the regulation time and nitrogen consumption during the regulation process are reduced by 80% and 75%, respectively, which effectively reduces the test cost and improves the equipment utilization rate (from four cycles per day to six cycles per day). The device can meet the requirements of different target substances and different process conditions by changing the electronic control parts and air source, so it has great application potential in the automatic direct measurement of target substances in zinc hydrometallurgy.

Design and Application of Medical Equipment Management System Based on B/S Architecture and WeChat Official Accounts

In order to solve the problems of equipment comprehensive management in grade A hospitals, such as complicated work, low maintenance efficiency, error-prone, non-standard management process, et al. A set of efficient information-based medical management equipment platform was designed to serve the concept of medical departments. B/S (browser and server) architecture and WeChat official accounts technology were adopted to construct the application end and WeChat official accounts client developed by Web technology, and MySQL server was selected as the system database technology. Asset management, equipment maintenance, quality control management, equipment leasing, data statistical analysis and other modules were integrated into the system, which optimized and standardized the medical equipment management process, improved the work efficiency of equipment management personnel, and improved the opening rate of medical equipment. Intelligent management by means of computer technology can effectively improve the utilization rate of hospital equipment, improve the level of hospital informatization and fine management, and contribute to the informatization development of medical engineering department.

Analysis of the Spatial Differentiation and Promotion Potential for Agricultural Eco-Efficiency-Evidence of Pollution's Strong Disposability.

Agricultural eco-efficiency is an important indicator used to measure agriculture's high-quality and sustainable development. Therefore, this paper uses the EBM-Super-ML method with strong disposability of undesired output to calculate Chinese agricultural eco-efficiency and uses a geographical detector to measure the driving force of the factor. The research conclusions are mainly reflected in three aspects. Firstly, from the perspective of eco-efficiency changes, the overall mean value of agricultural eco-efficiency increased by 3.5%, and the regional heterogeneity is significant, with the fastest growth in the eastern region. Secondly, the results of driving force analysis show that the main driving factors of agricultural eco-efficiency divergence are capital inputs, total carbon emissions, labor inputs, agricultural film residues, fertilizer use, and pesticide residues, with driving forces of 0.43, 0.37, 0.34, 0.31, 0.28, and 0.20, respectively. Finally, from the perspective of eco-efficiency improvement potential, the mean value of output improvement potential is 5%, and the input factor is 7%. Among the non-desired outputs, the reduction rate of agricultural films can reach 40%. Among the input factors, labor input has the highest potential for intensive use, while agricultural machinery has a negative effect. Therefore, strengthening the development of the agricultural service industry is of great significance to improve the utilization rate of mechanical equipment and reduce the undesired output of agriculture.

Construction of medical equipment maintenance network management platform based on big data

Introduction: Medical equipment is an indispensable part of hospitals. It is the basic condition and guarantee for the hospital to carry out medical services, scientific research, teaching, and other activities, and it plays an irreplaceable role in the entire medical process of the hospital. Therefore, the maintenance management of equipment is also the focus of our attention. In the past, traditional management methods such as paper were mainly used for equipment maintenance management, and it was difficult to share data.Methods: In today’s era of rapid development of information technology, we will use information technology to maintain and manage medical equipment. Through big data analysis and other technologies, the drawbacks of the existing traditional management methods are improved, so that medical equipment can be managed scientifically. By maximizing its functions, it can ensure the normal operation of medical facilities, improve the utilization rate and integrity rate of equipment, and reduce maintenance costs and unnecessary losses.Results: According to the research findings, based on the background of big data, a de-Bayesian network is used for data mining to build a medical equipment maintenance platform. Through the data in the platform, we can better discover the distribution and reasons of equipment maintenance, and at the same time conduct an analysis to provide reference for the formulation of preventive maintenance plans, reduce equipment failure rate and maintenance costs and improve equipment utilization. Through the survey of medical staff, we can also find that at least 40% of the people feel that the work distribution is more reasonable, and 45% of the people feel that the equipment failure rate and the time required for maintenance have been greatly reduced.Discussion: We can see that the network platform for medical equipment maintenance management built through big data is very feasible, which can help us work more effectively and improve work efficiency.

A Multi-Equipment Task Assignment Model for the Horizontal Stripe Pre-Cut Mining Method

This paper proposes a multi-equipment task assignment model for the horizontal stripe pre-cut mining method to address the problem of cooperative scheduling operation of multi-equipment in underground metal mines under complex constraints. The model is constructed with multiple objectives, including operation time, operational efficiency, equipment utilization rate, and ore grade fluctuation by considering the constraints of time, space, equipment, and processes. The NSGA-III algorithm is used to obtain the solution. The effectiveness of the algorithm is tested based on the actual data from the Chambishi Copper Mine. The results show that the average equipment utilization rate is 51.25%, and the average ore output efficiency is 278.71 tons/hour. The NSGA-III algorithm can quickly generate the optimal multi-equipment task assignment solution. The solution reduces the interference of manual experience and theoretically improves the actual operation of the mine.

Intelligent Blanking of Silicon Steel Coil in a Transformer Core Oriented to Green Manufacturing

For transformer enterprises, energy consumption and environmental pollution mainly occur in the manufacturing process. The conventional manufacturing mode does not conform to the current green manufacturing mode. The green manufacturing mode requires enterprises to improve the transformer production technology, the utilization rate of materials and equipment, and the production efficiency and to achieve clean production through energy conservation and consumption reduction. The main objective of this research is to schedule the blanking of multiple transformer cores together rather than the conventional calculation conducted one by one. An optimization model of the silicon steel coil blanking is established, an evaluation method for blanking schemes is proposed, algorithms to solve the optimization model are analyzed in detail, and the solving processes and results are compared through a case study. Compared with the current manual calculation, this paper is of practical significance for transformer manufacturers to improve the production efficiency, reduce material waste, meet the personalized market demand characterized by multiple varieties and small batches, and achieve the green manufacturing of transformers.

Developing a Decision Support System for Evaluating an Investment in Fare Collection Systems in Transit

This article presents the initial development of a decision support system (DSS) to assess cost impacts of upgrading or replacing a transit ticketing and fare collection (TFC) system. Assessment of these costs, typically, requires extensive calculations and involves estimation of unknown parameters such as future ridership growth rate, equipment utilization rate, and interest rate on debt financing. This DSS is developed with two categories of policy- and decision-makers in mind—transit agency managers and transit industry researchers and policy-makers.With the aid of this DSS, a transit manager or analyst is able to specify current TFC system characteristics, select desirable options for a new TFC system, and in a matter of minutes estimate capital costs, forecast operating costs, perform net present value and payback period analyses for alternative TFC systems. This article also presents a prototype TFC DSS including a model base, database, knowledge base, and the Microsoft Excel-based graphical user interface.

A Heuristic Integrated Scheduling Algorithm via Processing Characteristics of Various Machines

Complex products with a tree-like structure usually have an asymmetric structure. Therefore, in order to avoid the separation of equipment and operation correlation during the scheduling, the structural attributes of products and the use of equipment resources should be fully considered. However, this feature is ignored in the current research works on the scheduling of multi-variety and small batch products. This leads to increased idle time for equipment and an extended makespan for products. To avoid this situation, a heuristic integrated scheduling algorithm via processing characteristics of various machines (HIS-PCVM) is proposed. In the integrated scheduling, the algorithm first divides the equipment into two categories: the special equipment and the general equipment according to the resources of the production scheduling system. Then, different scheduling methods are designed according to the equipment categories. The makespan of the product is further optimized through various combination methods. Moreover, the constraint audit strategy to guarantee the constraint relationship between the operations is optimized. The earliest scheduling time strategy is proposed to improve the parallelism and serial tightness of the operations. These strategies reduce the idle time of equipment effectively. Experimental results show that the proposed algorithm has a better application effect in reducing the makespan of complex products, both with asymmetric structures and symmetric structures. This also shows that the algorithm is effective in improving the utilization rate of equipment.

Assisting Laboratory Management Based on Network Big Data Mining Technology

In today’s big data context, the composition of virtual networks is becoming increasingly complex, so it is neither easy nor difficult to strengthen the control of network security issues. This paper studies the important content of network big data mining-assisted laboratory management. On the basis of discussing the connotation of laboratory safety, combined with management practice, this paper puts forward some suggestions on laboratory safety management. This paper puts forward some views and thoughts on some problems existing in the management and experimental teaching of chemistry laboratory. By judging the stability of instruments and equipment, we know whether the calibration status of instruments and equipment has changed during this period, so as to ensure the sustainable use of instruments and equipment and the accuracy and traceability of laboratory test results. The use of laboratory management system can greatly improve the utilization rate of laboratory equipment, promote students’ autonomous learning, and promote the standardization of laboratory construction. Improve the network monitoring method of big data mining technology and the application mechanism of big data network monitoring, in one type of laboratory. Teachers and students in the laboratory can reasonably arrange their own time according to their own interests to guide and operate experiments. Through continuous operation and practice, students’ practical ability and experimental knowledge level should be improved as much as possible.

Integrated Scheduling Algorithm of Two Workshops Based on Process End Time Driven and Processing Area Priority

In order to effectively allocate processes of Aa single complex product to two workshops with the same equipment, an integrated scheduling algorithm of two workshops based on process end time driven and processing area priority is proposed. To optimize the total processing time, a process scheduling strategy based on process end time driven is proposed. By scheduling multiple devices at the same time, the parallelism of process execution is improved, in order to reduce the total processing time and improve the utilization rate of equipment. In order to optimize the process migration times between two workshops, a workshop assignment strategy based on process processing area priority is proposed, which comprehensively considers the influence of the immediate preceding process, neighbor process and friend process of the schedulable process on workshop assignment and makes the process in the same processing area be processed in the same workshop as much as possible, so as to reduce the process migration times. The example analysis shows that the proposed algorithm is superior to the existing two workshops integrated scheduling algorithms in terms of reducing the total processing time, reducing the process migration times, and improving the utilization rate of equipment.

Automatic monitoring system of power equipment based on Internet of Things technology

Abstract With the rapid development of the social economy, the power industry is also developing rapidly, the electricity load is increasing, and the power grid structure is becoming increasingly complex, people put forward higher requirements for improving the reliability of power supply, the emergence of Internet of Things technology can meet people’s urgent needs in a timely manner. How to use the advanced Internet of Things technology to realize the automation of power equipment is an inevitable trend in the development of power systems, and it is also the technical driving force for the development of power equipment. Due to the problems of information islanding, simplification, and security in traditional power equipment monitoring systems, this paper studies the automation of power equipment monitoring systems based on the Internet of Things. The experimental results show that the introduction of the Internet of Things technology into the automatic monitoring system of power equipment increases the utilization rate of power equipment by 4.33%. In addition, the safety of power grid operation and the monitoring of power equipment are improved. Once the power equipment is abnormal, it will be detected in time to ensure the continuity of the power supply.

THE DESIGN PROFILE OF THE DIRECTIONAL WELL ON THE NORTHERN GOTURDEPE FIELD

When deciding to use the method of dual completion, the degree of depletion of reserves, the proximity of the oil-bearing contour to wells, the presence of resins and paraffin in the extracted oils, the thickness of productive layers and the non-permeable layers separating them, the state of the production column of wells, etc. are taken into account. The positive effect of applying the technology dual completion is expressed in a reduction in capital investments for the construction of wells for each of the operational facilities, in reducing operating costs and the development period of a multi-layer field, in increasing the production of hydrocarbons and the term of final oil recovery with cost-effective operation of wells. In addition, the use of this technology contributes to an increase in the utilization rate of downhole equipment and the reliability of the downhole installation. When two horizons are operated separately at the same time, the layers are separated from each other by a packer. One or two rows of pumping and compressor pipes descend into the well, which are lowered in parallel or concentrically. With dual completion of two or more horizons, reservoir development can be carried out according to the following schemes: fountain-fountain; fountain-pump; fountain-gas lift; fountain-injection; gas lift-pump; gas lift-gas lift, gas lift-injection; pump-pump; pump-injection; pump-injection. The article considers the experience of drilling a directional exploration well in Turkmenistan in order to trace and clarify the expansion of the area of productive horizons in the coastal zones of the coastal waters of the Caspian Sea. This work can be used for the development of fields in difficult-to-develop shallow waters and to reduce costs during their drilling, as well as to increase the volume of oil produced in order to develop the field using the method of dual completion (accelerated method), without increasing the oil recovery coefficient.

Survey and analysis on digital construction of primary hospitals in Guizhou Province

To investigate the utilization status and awareness of digital hospital construction among medical staff in critical care department of primary hospitals, so as to promote the process of digital medical health. One to two doctors and nurses (in the department on that day) from public hospitals in 88 counties and urban areas in 9 cities of Guizhou Province were enrolled of field investigation. The questionnaires form were filled in on-site and sorted out and analyzed by special personnel. A total of 297 medical staff from the department of critical care medicine of 146 hospitals were included. All the questionnaires were filled in with their real names, including 152 doctors and 145 nurses. There were 24 class III Grade A hospitals and 122 class II and all the hospitals had implemented digital information management. The awareness of hospital digital information management system was generally low among the surveyed medical staff, and the awareness of hospital information system (HIS) was the highest (86.5%), followed by laboratory information management system (LIS, 41.4%) and image archiving and communication system (PACS, 40.7%). The awareness of hospital management system (HERP) was the lowest (7.7%). The total number of remote consultations conducted by hospitals using big data Internet was 25 428 times in 2020, with a median of 24.5 (88.0, 240.0) times in each hospital. From 2018 to 2020, the total number of patients admitted to the intensive care unit of the hospital was 50 473, 57 565 and 57 907, respectively, of which the number of patients over 65 years old accounted for 37.47%, 41.26% and 43.31%, respectively (all P > 0.05). There were 4 242 cases of remote consultation using big data Internet in the department every year, with a median of 257.50 (96.50, 958.25) cases. 12.12% of the departments had independent critical monitoring systems, and 8.75% of them could capture data automatically to form tables. 96.30% of the medical staff participated in systematic and professional training on basic knowledge, basic theory and basic medical care skills through the Internet platform, and the number of meetings, studies and training in the provinces and prefectures were 282 and 357 times per year, respectively. More than 90% of the departments initiated remote consultation, arranged referral or admitted patients who had improved status after treatment in superior hospitals through the Internet platform. Most of the patients (69.02%) were from the lower level of the hospital. The total number of out-patient consultations was 2 959 times per year, with a median of 296 (185 473) times. 54.79% of the departments had fixed service villages, and 28.08% of the departments had fixed service population. The median furthest visit distance was 52.5 (30.0, 80.0) kilometers, and the median average visit distance was 30.0 (20.0, 50.0) kilometers. 54.88% of medical staff believed that the biggest difficulties encountered during house visits were insufficient energy and too large service groups or regions. More than 90% of medical staff had been exposed to cloud learning and cloud training, and most of the surveyed medical staff believed that cloud learning and cloud training greatly improved medical service capacity and service efficiency of medical institutions (71.04% and 67.01%, respectively). Meanwhile, they believe that "Internet+health big data" projects from various aspects brought advantage to medical institutions, but there are also low utilization rate of Internet medical equipment by village doctors, low acceptance for telemedicine and mobile hospitals by farmers. Guizhou public hospitals have implementation of digital information management at the grass-roots level, the surveyed health care workers have a relatively low awareness of the digital information management system, hospital use big data Internet for remote consultation is uneven, intensive care medicine is a clinical discipline used in most remote consultation information system, and can complete two-way referrals. In the past three years, the discipline operation showed an upward trend year by year. Medical staff use artificial intelligence devices such as cloud learning and training to improve medical service capacity and efficiency. The digital transformation of primary hospitals is being continuously improved.