Effective Resource Utilization Research Articles

An intelligent Hybrid‐Q Learning clustering approach and resource management within heterogeneous cluster networks based on reinforcement learning

AbstractRecently, heterogeneous cluster networks (HCNs) have been the subject of significant research. The nature of the next‐generation HCN environment is decentralized and highly dynamic; optimization techniques cannot quite express the dynamic characteristics of node resource utilization and communication of HCN networks. In this article, we present an intelligent Hybrid‐Q Learning approach (Hybrid QL)‐based clustering approach for IoT and WSN. Using the self‐learning abilities of (HCNs), we propose a model for dynamic accessing systems on nodes and agents that identify the best possible paths and communication over heterogeneous cluster networks using reinforcement learning. In addition to reducing energy consumption, it creates efficient and effective resource utilization and node communication performance. Through increased throughput and link management, the HCN aims to reduce energy consumption. The proposed model is compared to existing approaches based on various scenarios. Finally, the results of the evaluation tasks demonstrate high accuracy, low‐level complexity, fast dynamic response times, and scalability for heterogeneous cluster networks. Our model showed exceptional node allocation efficiency for dynamic IOT environments and WSNs.

Constructing highly stable and biomimetic supercapacitor materials of 2D MoS2 coupled with NiCoLDH @chitosan carbon microsphere

Due to the growing demand for clean and renewable energy, the effective utilization of renewable resources in energy storage and has been concerned. And high specific capacitance and stable electrochemistry for energy storage devices are challenging to develop. Learning from unique structure of goniastrea coral, we design to construct three-dimensional layered conductive electrodes material made of MoS2@chitosan carbon spheres (MNCCS) composite. The NiCoLDH nanosheets construct on the surface of conductive chitosan carbon spheres to effectively form open permeable channels and rich conductive networks, improves the efficient energy storage and electrochemical stability. MoS2 is strongly coupled with NiCoLDH nanosheets through heterointerface, which resembles tentacles on the surface of coral, exposing more active sites to efficiently trap ions. Besides, the obtained material possesses favorable heterojunction interface and defects, which provide sufficient available electroactive sites and numerous storage charge area. Hence, The MNCCS presents an outstanding specific capacitance of 2605.9 F g−1 at 1 A g−1. Notably, a supercapacitor fitted with MNCCS electrode and CCS electrode achieves high specific capacitance (234.4 F g−1 at 1 A g−1), the maximum energy density of 77.0W h kg−1 at highest power density of 667.4W kg−1, along with the excellent cycling stability of 90.8% retention over 10,000 cycles. This work provides a new perspective for fabricating high-performance and stable supercapacitors electrode material for clean energy storage.

Intelligent Task Scheduling Approach for IoT Integrated Healthcare Cyber Physical Systems

Cyber-physical systems (CPS) based on cloud computing provides resources over the Internet and allow a variety of applications to be deployed to provide services for various industries. We proposed IoT-based healthcare cyber-physical system that provides effective resource utilization at fog and cloud levels with minimum execution cost. In addition, we also consider data from social media networking and drug review for the analysis. Furthermore, two different feature extraction approaches were applied based on data collection. Homogeneity score-based K-means clustering is used as a feature extraction and selection method for sensor data features, while text mining and sentiment analysis approach is used for social media networking and drug review data feature extraction. We proposed efficient resource utilization and cost-effective task scheduling at the Fog level and multi-objective heuristic approach Ant colony optimization task scheduling (MOHACO-TS) at cloud level. Both task scheduling algorithms focus on executing maximum task tasks in minimum time with effective resource utilization. We consider five different datasets and existing task scheduling and classification approaches for performance evaluation of the proposed IoT-HCPS framework. From the results, it is evident that the proposed work IoT-HCPS outperformed the exisitng techniques and algorithms.

Distributed Joint Resource Optimization for Federated Learning Task Distribution

The effective utilization of computation and communication resources is one of the primary challenges for Artificial Intelligence (AI)-based applications. The complexity of the edge computation environment and the diversity of communication interference in the wireless environment make it particularly difficult to quickly and accurately complete multiple federated learning tasks. This paper examines energy cost, communication capacity, computation capacity, and other factors in a network-slicing environment. The virtual queue vector and the associated Lyapunov drift are presented to analyze queue backlog performance and optimize each slice’s Key Performance Indicator (KPI) performance, including energy and other Quality of Experience (QoE) factors. In response, we design a Federated Learning (FL) Service-Oriented Allocation Policy Calculation Algorithm to address the allocation policy calculation problem in a time-varying environment. Finally, our simulation experiments demonstrate that our proposed algorithm outperforms other benchmarks.

Effective utilization of attentional resources in postural control in athletes of skill-oriented sports: an event-related potential study.

Postural control plays a key role in skill-oriented sports. Athletes of skill-oriented sports (hereinafter referred to as "skilled athletes") usually showed better control ability compared with non-athletes. However, research focused on the single postural task, rarely considering the actual situation in skill-oriented sports in which other processes, such as cognitive control, frequently accompany postural control. This study aims to explore how skilled athletes control their posture under the dual-task situation and use limited attentional resources. A total of 26 skilled athletes and 26 non-athletes were required to perform the postural control and N-back tasks simultaneously. Center of pressure (COP) trajectory, reaction times (RTs), and discriminability (d') of N-back tasks were recorded and evaluated, along with event-related potentials, including N1 (Oz, PO7, and PO8), P2 (Fz, FCz, Cz, and Pz) components, and the spectral power of alpha band. Skilled athletes demonstrated more postural control stability and a higher d' than non-athletes in all dual tasks. Besides, they showed enhanced N1, P2 amplitudes and reduced alpha band power during dual-tasking. Notably, in skilled athletes, a significant negative correlation between N1 amplitude and d' was observed, while significant positive correlations between alpha band power and postural control performance were also identified. This study investigates the potential advantages of skilled athletes in postural control from the view of neuroscience. Compared to non-athletes, skilled athletes could decrease the consumption of attentional resources in postural control and recruit more attentional resources in stimulus discrimination and evaluation in cognitive tasks. Since the allocation of attentional resources plays a crucial part in postural control in skilled athletes, optimizing the postural control training program and the selection of skilled athletes from a dual-task perspective is important.

Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors

The increasing penetration of renewable energy sources (RESs) in multi-microgrids (MMGs) poses significant challenges to stable operation of the systems, and exploring grid-interactive functionalities of electric vehicles (EVs) is receiving increasing attention. However, current distributionally robust energy management models suffer from convergence inefficiencies when exposed to large amounts of historical data, and typically neglect the multi-period coupling effect of EV user behaviors, which hinder the effective utilization of the highly-potential EV resources. In this paper, a novel distributionally robust energy management model for MMGs is proposed to accommodate the uncertainties of RESs and loads, with the grid-interactive EVs operating in an efficient vehicle-to-grid (V2G) mode. Firstly, a multi-period dynamic EV-connection matrix is formulated to determine the connection and dwell times for EVs interacting with the power systems, which enables the cross-cycle continuity of SOCs. Further, the multi-period coupling uncertainties of accidental EVs disconnections are taken into account. Secondly, the Kohonen neural network-based ambiguity set is constructed without including the entire historical scenarios, where the ambiguous distribution is characterized by the representative scenarios with weights. On this basis, a two-stage distributionally robust optimization model is finally developed, which can be solved iteratively by the extended column-and-constraint generation method until the worst-case cost expectation is obtained. The proposed model was evaluated through simulations on a system comprising four interconnected microgrids from the Hainan provincial power grid. The results demonstrate that the proposed model achieves superior cost efficiency, convergence performance and robustness compared to alternative approaches.

Construction and Empirical Study on the Value Assessment System of Traditional Cultural Resources: Taking Shandong Province in China as an Example

Accurate value assessment is the cornerstone and key to ensuring the effective protection, inheritance, and utilization of traditional cultural resources. Based on existing study and relevant literature on cultural resource assessment, combined with the characteristics of traditional cultural resources themselves, this study has constructed a general index system for evaluating traditional cultural resources from four dimensions: resource quality, development conditions, resource value and resource utility. The analytic hierarchy process(AHP) was used to determine the weight of each factor of the criteria layer and each evaluating index of the index layer, and a traditional cultural resource value assessment model was constructed by combining the scoring method of the ultimate indicator. Subsequently, based on the empirical evaluation of traditional cultural resources in Shandong Province, this study puts forward relevant suggestions for effective protection, inheritance and utilization of traditional cultural resources from the perspectives of government, enterprises and society.

Structural and psychological empowerment and burnout among nurses: A systematic review and meta-analysis.

This study aims to review available evidence about the relationship between structural and psychological empowerment and burnout among nurses. Nurses are key healthcare providers, who experience higher levels of burnout due to uncertainty and role conflicts about nursing roles and responsibilities and poor management. Nurse empowerment is an effective approach to reduce nurse burnout and enhance patient care quality. Positive working conditions along with positive attitudes and perceptions for nurses are crucial in the workplace. Nurse empowerment in the workplace results in quality improvements in work life and the provision of healthcare. We conducted a systematic review in accordance with the recommendations of the Joanna Briggs Institute and the PRISMA guideline. Relevant studies published between 2007 and 2022 were identified via PubMed, CINAHL, Web of Science, Scopus, Cochrane Library, Ovid MEDLINE (R), Science Direct and Turkish scientific literature databases. Studies that reported correlation coefficients were pooled to conduct a meta-analysis. Random-effects meta-analyses showed a negative association between structural and psychological empowerment and emotional exhaustion. The overall findings showed a moderate and negative association between the six dimensions of structural empowerment and depersonalization. There was a positive association between structural empowerment and personal accomplishment. There is a relationship between decreased burnout levels and nurse empowerment. The causal relationship between empowerment and burnout levels needs to be investigated in various healthcare settings in several countries. The relationship between structural and psychological empowerment and burnout levels emphasizes that empowerment can reduce nurse burnout. Nurse empowerment is a critical management strategy for improving the quality of life for nurses, increasing the quality and effectiveness of patient care and achieving positive outcomes. The encouragement and empowerment of nurses for prompt decision-making and effective resource utilization, reducesnurse burnout, enhancing nurses' job commitment, productivity, satisfaction and competence along with increased quality of care. The encouragement of nurses as empowered managers at the macro, meso and institutional levels not only improves the overall quality of health services but also helps to find solutions for the issues concerning healthcare service users and the health system environment.

Precipitation prediction based on CEEMDAN–VMD–BILSTM combined quadratic decomposition model

Abstract Accurate prediction of monthly precipitation is crucial for effective regional water resources management and utilization. However, precipitation series are influenced by multiple factors, exhibiting significant ambiguity, chance, and uncertainty. In this research, we propose a combined model that integrates adaptive noise-complete ensemble empirical mode decomposition (CEEMDAN), variational modal decomposition method (VMD), and bidirectional long- and short-term memory (BILSTM) to enhance precipitation prediction. We apply this model to forecast precipitation in Fuzhou City and compare its performance with existing models, including CEEMD–long and short-term memory (LSTM), CEEMD–BILSTM, and CEEMDAN–BILSTM. Our findings demonstrate that the combined CEEMDAN–VMD–BILSTM quadratic decomposition model yields more accurate predictions and captures the real variation in precipitation series with greater fidelity. The model achieves an average relative error of 1.69%, at a lower level, and an average absolute error of 1.32 m, with a Nash–Sutcliffe efficiency coefficient of 0.92. Overall, the proposed quadratic decomposition model exhibits excellent applicability, stability, and superior predictive capabilities in monthly precipitation forecasting.

Suitability Evaluation and Engineering Matching for Agricultural Development of Barren Grassland in Mountainous Area: A Case Study of County Scale

Barren grasslands are an important reserve resource of cultivated land in mountainous areas. The exploitation of barren grassland for agricultural use has played a role in ensuring food security and people’s livelihood in many countries and regions. The suitability evaluation of agricultural-use development, based on the conditions of barren grassland itself and the engineering matching, can not only ensure the sustainable development of agriculture but also avoid the ecological negative effects caused by excessive engineering. According to the research, the agricultural development of barren grassland needs to be evaluated from the two angles of natural suitability and engineering suitability, and an innovative “index level serial number summation method” is proposed. The suitability of barren grassland for agricultural use development was divided into three categories: barren grassland suitable for cultivation, barren grassland suitable for forestry, and barren grassland suitable for prataculture. The barren grassland suitable for cultivation was selected for type division and engineering-accurate matching. Taking Tang County as a research area, an example was provided through a combination of theoretical research. According to the characteristics of barren grassland in Tang County, the evaluation indexes of natural suitability and engineering suitability were selected, and the suitability of barren grassland for agricultural development was evaluated and graded. A classification system for the utilization of barren grassland suitable for cultivation is constructed, the required engineering types are explored to eliminate the main limiting factors, and the utilization types are matched with the engineering combination types. The barren grassland suitable for cultivation in the study area can be divided into 37 types, which can match eight engineering combinations. This study proposes a systematic method for the identification, classification, rating, and engineering matching for arable land reserve resources. The study can provide the basis for the effective utilization and accurate development of land resources.

Unraveling the role of polymeric ferric sulfate in promoting propionic acid in anaerobic sludge hydrogen production fermentation

Polymerized ferric sulfate (PFS) is widely employed in the pretreatment of industrial wastewater and sludge dewatering. Its widespread use results in its accumulation in waste activated sludge (WAS) and may impact anaerobic fermentation (AF). This work fills the gap in the effects and mechanisms of PFS on hydrogen (H2) production from AF under mesophilic and thermophilic conditions. PFS (40 g/kg total solids, TS) reduced cumulative H2 production by 59.2% (mesophilic conditions) and 52.6% (thermophilic conditions) (p < 0.001), whereas propionic acid production increased by 299.2% and 192.0%, respectively. Model-based analysis showed that PFS inhibited solubilization, hydrolysis, and acidification below 10 g/kg TS but promoted them above 10 g/kg TS. Changes in the ratio of the two forms of nicotinamide adenine dinucleotide (NADH/NAD+) and hydrogenase activity led to unfavorable pathways for H2 production. Thermophilic fermentation exhibited strong tolerance to PFS due to the increased release of extracellular polymeric substances (EPSs). By analyzing 16S-rRNA gene operational taxonomic units (OTUs), the effects of environmental factors on microbial communities and microbial interactions under the combined effects of PFS and temperature were identified, revealing the important factors affecting H2 production. Sludge containing PFS was not conducive to anaerobic H2 production; however, mesophilic fermentation with PFS may be a potential way to recover propionic acid with low energy consumption. This study provided insights into the effects of potential contaminants in WAS on AF for H2 production and information for the effective disposal and resource utilization of WAS.

Does Natural resource dependency impede sustainable development? Exploring the non-linear consequence of economic complexity

Environmental sustainability is paramount for developed and developing countries to accomplish their global commitment for climate actions. Particularity, emerging countries must prioritize endeavors to curtail the factors that create hurdles for the practices align with the Paris climate change agreement. In the same purists, this study unveil the influence of natural resource rent, economic complexity, and economic growth on the ecological footprint in emerging seven (E7) countries from 1995 to 2020. It applies Non-linear ARDL model to integrate non-linearity and address structure changes in data. The findings reveal that the positive shocks in natural resource rent, economic growth, and economic complexity positively influence the ecological footprint in both the long and short runs. Whereas the negative shocks of these variables significantly enhance the environmental sustainability in E7 countries. However, the short-term influence of negative shocks of economic complexity and economic growth are insignificant. These finding implied sustainable transformation of industries and effective utilization of mineral resources.

Regulation of soil enzyme activity and bacterial communities by food waste compost application during field tobacco cultivation cycle

Food waste (FW) composting is an effective waste resource utilization method; however, only limited approaches of FW compost application in agriculture have been investigated. This study reports changes in soil chemical properties, enzyme activities, and bacterial communities at different tillage stages and its effects on tobacco biomass after FW compost application in tobacco cultivation field experiments. The results showed that FW compost increased tobacco biomass. Also, FW compost functioned as a fertilizer that acted mainly during the maturity (September) and fallow (December) periods of tobacco, and its effect was enhanced with increasing application rates. FW compost significantly increased soil pH, organic carbon, total nitrogen, total phosphorus, alkaliolytic nitrogen, and available phosphorus content. It enhanced soil urease, acid phosphatase, fluorescein diacetate hydrolase, and polyphenol oxidase activities. FW compost application increased soil bacterial diversity, increasing the relative abundance of dominant bacteria such as Proteobacteria and Bacteroidetes while decreasing the relative abundance of Acidobacteria and Chloroflexi. Bacterial chemoheterotrophy, aerobic_chemoheterotrophy, ureolysis, chloroplasts, nitrogen_fixation, and fermentation functions were enhanced, and the complexity of ecological networks among bacterial species increased. A combined ranking analysis showed that the soil's available phosphorus, pH, alkaline nitrogen, and total phosphorus content were the main factors influencing bacterial population dynamics. In conclusion, FW composts can be used as an alternative to fertilizers. They exerts their beneficial properties by regulating bacterial communities mainly through soil chemistry, indirectly enhancing soil enzyme activity, and increasing tobacco yield.

A methodology for part family formation in reconfigurable manufacturing systems using Shannon's entropy method

For effective utilization of resources, Operation Management plays an important role in businesses and organizations. It basically coordinates the efforts of people to fulfil the goals and objectives decided by the organization. Operation Management is a supporting system to carry out businesses. From the point of generation of raw material to the point of consumption of goods, it directs and controls the organization. It makes a path to a manufacturing system to fulfil its goal. For full utilization of resources in an uncertain environment, the manufacturing system faces various challenges in this century. The manufacturing systems must be responding rapidly to market changes. For effective management, a new paradigm in the manufacturing system is the Reconfigurable manufacturing systems, which are built around a part family to face such challenges in an uncertain environment. The authors have developed a methodology for part family formation using attributes modularity, reconfiguration effort, longest common subsequence, etc. in the present work. The developed methodology has been discussed using an example.

Fermentation Regulation and Ethanol Production of Total Mixed Ration Containing Apple Pomace

To effectively utilize local fruit residue resources and regulate ethanol production in fermented feed, the impact of moisture adjustment, lactic acid bacteria (LAB) inoculant, and chemical additive on the fermentation characteristics and ethanol production of total mixed ration (TMR) containing apple pomace was studied. The TMR was prepared with apple pomace, corn, wheat bran, soybean meal, timothy, and alfalfa hay. The mixing proportion of apple pomace was 15% based on dry matter (DM). In experiment 1, the moisture in TMR was unadjusted (control) or adjusted to 45, 50, and 55%, respectively. TMR containing 55% moisture was used in experiment 2, and the treatments were control, homo-fermentative LAB (Lactobacillus plantarum, LP), hetero-fermentative LAB (Lactobacillus buchneri, LB), and calcium propionate (CaP). The laboratory-scale fermentation system was used to prepare TMR, and their fermentation characteristics were analyzed after 60 days of ensiling. In experiment 1, the pH of the various TMRs was around 4.1. As the moisture decreased, lactic acid increased (p &lt; 0.05) and ammonia-N decreased (p &lt; 0.05). The ethanol decreased significantly with moisture adjustment compared to the control and the TMR with 50% moisture had the lowest ethanol content (p &lt; 0.05). In experiment 2, LP treatment increased lactic acid, and decreased acetic acid and ammonia-N significantly (p &lt; 0.05), while LB treatment had no effect on fermentation. LP and LB each had no effect on the ethanol content. TMR treated with CaP significantly decreased the ethanol and acetic acid content (p &lt; 0.05), but did not inhibit lactic acid production compared to control. The results confirmed that adjusting the moisture of TMR to 50% and adding CaP could effectively inhibit the excessive production of ethanol in TMR of apple pomace. Homofermentative LAB can better improve the fermentation quality of TMR than heterofermentative LAB, but neither can inhibit the production of ethanol. This is of great significance to the effective utilization of apple residue resources and the promotion of livestock production.

Improving the Performance of Solar Thermal Energy Storage Systems

In recent times, renewable energy resources have been greatly researched because of the increasing concern to minimize global warming and meet energy demands. Energy storage systems have become useful tools for sustainability and meeting energy needs. Solar energy has proven in recent times to be the primary and most prevalent option due to its environmental friendliness, availability, and minimal pollution. Effective utilization of available energy resources has led to developing new alternative energy devices like the solar thermal energy storage system (STESS) with a solar energy source. Solar thermal energy systems are efficient systems that utilize solar energy to produce thermal and electrical energy. This review aims to give a detailed overview of solar TESS, different TES application systems, and effective methods of increasing the system performance to provide energy during deficient times. The various classifications, basic components, the principle of operation, application areas of STESSs, prospects, and extensive reviews on these aspects have also been discussed in this review. The different factors to be considered geared towards meeting energy demands and increasing the efficiency of solar TES systems have been duly detailed. This review is a single manuscript with a detailed overview of STESS, the principle of operation and components of STESS, thermal energy storage materials, a description of different application systems, and a discussion of factors responsible for improving the system efficiency.

Peningkatan Kinerja Karyawan Melalui Motivasi dan Lingkungan Kerja Pada BMT Amanah Mandiri Purwantoro

Effective utilization of human resources is a way for a company to maintain its survival and growth in the future. One way that can improve employee performance is by providing work motivation and conditioning the work environment. However, in reality, employees still lack discipline and their work productivity decreases. The problems studied in this study are how the motivation techniques and conditioning of the work environment are carried out, the obstacles faced, and the impact on improving employee performance at BMT Amanah Mandiri Purwantoro. In this study, the author used a type of field research with a qualitative approach. This study uses data collection methods carried out at the place where the phenomenon occurs, ranging from observation, interviews, and documentation. From the results of the research that the author conducted, it can be concluded that the motivation and conditioning techniques of the work environment provided are in accordance with the theory used starting from providing salaries, providing health guarantees, and also respecting and respecting each other. However, there are still some things that are still lacking, namely emotional changes in themselves, the condition of the building is less extensive, the provision of incentives received by employees is still not in accordance with their needs, and also the level of discipline that is still low both in the accuracy of entering work and completing work in accordance with the predetermined time.

Deriving hydropower reservoir operation policy using data-driven artificial intelligence model based on pattern recognition and metaheuristic optimizer

Robust reservoir operation policies are crucial in ensuring the effective utilization of water resources. However, owing to multiple complicated and changeable factors in practice, it is difficult for standalone approaches to derive reasonable reservoir operation policy. To address the practical requirement, this research proposes a novel artificial intelligence method for deriving reservoir operation policy. The proposed method uses the fuzzy clustering iteration method to identify multiple typical operation patterns from the influencing factors; secondary, for all the samples within each pattern, the novel twin support vector regression (TSVR) is utilized to model the nonlinear mapping relationship between the influence inputs and the target outputs, while the emerging equilibrium optimizer is chosen to determine suitable computation parameters for the TSVR model. The feasibility of the proposed method is fully evaluated on two real-world huge hydropower reservoirs in China. The simulations demonstrate that the developed method can yield better comprehensive benefits than several control methods in deriving reservoir operation policy under uncertain environments. Hence, the experiments confirm that metaheuristic algorithms and pattern recognition techniques can enhance the performance of a standalone artificial intelligence methods in deriving reservoir operation policy.

A framework of genealogy knowledge reasoning and visualization based on a knowledge graph

PurposeWith the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.Design/methodology/approachBased on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.FindingsThe case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.Originality/valueThis study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

Smart Building Skin Design with Dynamic Climate Adaptability of Smart Cities Based on Artificial Intelligence

As the separation and carrier of indoor and outdoor energy and climate conditions, building skin plays an important role in indoor environment regulation and effective utilization of outdoor environmental resources. The traditional fixed skin of residential buildings in cold regions lacks the ability to respond to the external climate, so it is difficult to meet the dual requirements of building energy efficiency and indoor comfort. In the long river of architectural development, the most important thing of architectural design is how to meet the climate adaptability. Traditional architectural forms have long been unable to meet the current social development, climate conditions, and user needs. Based on the basic theory, this paper establishes a systematic understanding of inlay, studies the design method of complex skin with geometric algorithm as the operating tool, discusses the application of this method in architectural design in combination with practice, more systematically and comprehensively studies the building skin with dynamic climate adaptability, and makes a physical model of building skin with dynamic climate adaptability. The contrast experiments under different control modes were carried out using the climate chamber experimental system. This research focuses on taking geometric principles as the prototype, trying to break the common design idea of generating skin by overlapping cells, and providing a systematic skin design method with strong operability and modular structure, hoping to help expand creative thinking.