Intelligent Decision Method Research Articles

An intelligent decision method of quality risk in electromagnet assembly process based on digital twin and q-rung dual hesitant fuzzy number

In order to solve the problem of online risk analysis of assembly process, an intelligent decision-making method of electromagnet assembly risk based on Digital Twin (DT) was proposed. Firstly, a DT model of electromagnet assembly process is constructed, and the potential failure modes are collected in real time at the physical layer. Secondly, in the virtual layer, real-time potential failure modes are obtained through information synchronization and transmitted to the application layer. Then, in the application layer, the optimized Failure Mode and Effects Analysis (FMEA) method is used to analyze the risk of failure mode, and the risk value of failure mode is fed back to the virtual layer. In the improved FMEA method, the q-Rung Dual Hesitant Fuzzy Number (q-RDHFN) is used as the evaluation language. The weight of experts is calculated by Bidirectional Projection Method (BPM), and the comprehensive weight of risk factors is calculated by Best and Worst Method (BWM) method, entropy weight method and Criteria Importance Through Intercriteria Correlation (CRITIC) method. Finally, the physical layer is adjusted to reduce the probability and risk of failure mode occurrence. An example of risk intelligent decision making is given to verify the objectivity and practicability of the new method.

Application of the artificial intelligence system based on graphics and vision in ethnic tourism of subtropical grasslands

This study aims to optimize the evaluation and decision-making of ethnic tourism resources through the utilization of deep learning algorithms and Internet of Things (IoT) technology. Specifically, emphasis is placed on the recognition and feature extraction of Mongolian decorative patterns, providing new insights for the deep application of cultural heritage and visual design. In this study, the existing DL algorithm is improved, integrating the feature extraction algorithm of ResNet + Canny + Local Binary Pattern (LBP), and utilizing an intelligent decision method to analyze the intelligent development of indigenous tourism resources. Simultaneously, the DL algorithm and IoT technology are combined with visual design and convolutional neural networks to perform feature extraction and technology recognition. Visual design offers an intuitive representation of tourism resources, while fuzzy decision-making provides a more accurate evaluation in the face of uncertainty. By implementing an intelligent decision-making system, this study achieves a multiplier effect. The integration of intelligent methods not only enhances the accuracy of tourism resource evaluation and decision-making but also elevates the quality and efficiency of the tourism experience. This multiplier effect is evident in the system's capacity to manage substantial datasets and deliver prompt, precise decision support, thus playing a pivotal role in tourism resource management and planning. The findings of this study demonstrate that optimizing intelligent development technology for rural tourism through IoT can enhance the efficacy of intelligent solutions. In terms of pattern recognition accuracy, AlexNet, VGGNet, and ResNet achieve accuracies of 90.8 %, 94.5 %, and 96.9 %, respectively, while the proposed fusion algorithm attains an accuracy of 98.8 %. These results offer practical insights for rural tourism brand strategy and underscore the utility of applying fuzzy decision systems in urban tourism and visual design. Moreover, the research outcomes hold significant practical implications for the advancement of Mongolian cultural tourism and provide valuable lessons for exploring novel paradigms in image analysis and pattern recognition. This study contributes beneficial insights for future research endeavors in related domains.

Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

AbstractTo verify the effectiveness of the intelligent decision method for fertigation, an automatic control system for fertigation in greenhouses was designed, and three intelligent decision methods based on evapotranspiration (T1), soil moisture (T2) and accumulated temperature (T3) were tested. Intelligent decisions included monitoring meteorological information, automatically monitoring soil moisture, utilizing fertigation application systems and using automated control modules. The system was stable and accurately controlled according to the decision scheme. The results showed that the average errors of the automated control system for decision‐making and irrigation were 1.1 and 0.8%, respectively. The study findings serve as a reference for the integration of intelligent irrigation decision‐making and control systems and for further improving the efficiency of water and fertilizer utilized. Compared with those of the control, the three intelligent decision‐making methods increased the tomato yield by 8, 12 and 7%, respectively. In addition, the irrigation water and fertilizer levels decreased significantly compared with those in the control treatment. Although the accuracy of the soil water content (SWC) estimated based on ET and temperature in irrigation decision‐making is low, the general trend is consistent with practice. In addition, the irrigation water use efficiency (IWUE) and partial factor productivity of fertilizer (PFP) were significantly improved. Similarly, the IWUE in T1 was the highest (60 kg m⁻3), and the PFP in T3 was the highest (669 kg kg⁻¹).

Fusion-based intelligent decision method with factor code and factor pedigree in the system fault evolution process

The key to information fusion lies in the clarification and integration of the information domains comprising all information and using factor space is an effective way to achieve the above goals. We studied the intelligent representation and logical reasoning of the system fault evolution process (SFEP), determined the relationship between factor influence and fault state and proposed an SFEP intelligent decision method based on factor space theory. The focus and innovation of this study lies in the combination of factor code and factor pedigree with space fault network to represent the topology of SFEP to extract and analyse the fault concept and realise intelligent analysis and decision of the fault process. The results show that the minimum unit can be expressed as a factor code, and the factor pedigree can be formed by knowledge increase expression. The frequency and the probability of the four phases of the fault concept under the action of a single- factor and multiple factors, as well as the proportion of certainty and uncertainty, were obtained. This study provides the basic theory and intelligent decision method for the formation of the fault concept in the SFEP considering information fusion.

Intelligent temperature control framework of lithium-ion battery for electric vehicles

The heat generated during battery charging and discharging induces rapid temperature rise, potentially affecting battery performance and safety. Coolant flow rate control has been used to regulate battery temperature to address this. However, traditional battery temperature control strategies have difficulty balancing temperature control accuracy and system response speed. Thus, an intelligent temperature control framework employing two control strategies: Fuzzy Logic Control (FLC) and Reinforcement Learning Control (RLC), is proposed in this paper. Meanwhile, a single-valve temperature control loop based on FLC and a double-valve temperature control loop based on RLC is designed in the framework. Moreover, an intelligent decision method is proposed to select the appropriate control strategy for each operation stage to achieve intelligent control. The results indicate that, compared with the traditional PID control strategy, the response time decreased from 361 s to 225 s by FLC, and the temperature difference decreased from 5.33 K to 2.36 K by RLC. The performance of the temperature control strategy for liquid cooling has been significantly improved.

Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining.

Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10-4 is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

Research on intelligent decision method of computer-aided manufacturing numerical control parameters based on model-based definition and back propagation neural networks

Aiming at the low efficiency and intelligence degree in the parameter setting of computer-aided manufacturing (CAM) numerical control (NC) programming caused by manual work, an intelligent decision-making method of CAM NC programming parameters based on model-based definition (MBD) is proposed. The hierarchical process model uses MBD technology to express the process flow and manage the process information. The tightness between process and NC machining is enhanced by sorting out NC machining steps and extracting their process information automatically. Then a back propagation (BP) neural network is used to mine the experience of NC programming in historical cases. Sparrow search algorithm (SSA) and other methods are used to optimize BP neural network’s initial weight and threshold, learning rate, and feedback process. A CAM parameter decision model based on BP neural network is constructed. Taking the connecting rod of a certain type of diesel engine as the experimental object, the method increases programming efficiency by 47%, with an accuracy rate of 80%. It is proved that the intelligent decision-making method proposed in this paper has practicability and feasibility.

Research on Intelligent Decision Method of Optimal Production Planning and AGV In-time Delivery in Mixed-Model Assembly Line

An automated guided vehicle (AGV) system is an indispensable part of a mixed-model assembly line (MMAL) for flexible and efficient production, which is utilized for the material delivery. With the urgent needs of industry digitalization and market customization, along with the trend of corporate smart decision management, MMAL has been widely adopted in the manufacturing process of modularized products. To address the parameter matching and energy consumption optimization of the AGV system in MMAL, the overall architecture of an intelligent MMAL (IMMAL) is firstly proposed. And the model of this problem is constructed. Then, the research methodology is proposed, which comprehensively adopts the dispatching rule, genetic algorithm, design of experiment, BP neural network, and simulated annealing. Eventually, a real-life case is used to validate the practicality of the proposed solutions. The results show that the optimal solutions of MMAL production and AGV system delivery at the minimum energy consumption are obtained successfully. Furthermore, three factors: the quantity, capacity, and velocity of AGVs greatly impact the energy consumption of the AGV system, where the quantity of AGVs has the most negligible significance, while the capacity of AGVs has the most significance.

Decision criteria and intelligent decision method for tunnel excavation scheme selection considering carbon emissions

The tunnel construction process is accompanied by high resource consumption and non-negligible greenhouse gas emissions. Reducing the carbon emissions from this process is an issue that should be considered in the decision-making stage. For tunnel construction using the drill-and-blast process, selecting a reasonable excavation scheme is a feasible method for reducing carbon emissions. This paper proposes an evaluation index system that takes into account the stability of the tunnel construction process and the reasonable assessment of carbon emission levels. For various scenarios with different focus on stability requirements, theoretical deductions are used to filter out the key indicators that should be used as assessment items. For the determination of carbon emission assessment indicators, this study is guided by the life cycle theory, and in the determined calculation boundary, the reference of previous projects and expert opinions are selected as the item source to fill the unfavorable situation in which the carbon emissions of different excavation schemes cannot be accurately calculated because of the lack of bills of quantities in the engineering decision stage. This paper also proposes an intelligent decision method based on a support vector machine to better complete the task of calculating and ranking the utility of excavation schemes to adapt the proposed multi-factor parallel evaluation system. Finally, the proposed decision indicator system and decision method were applied to the selection of the excavation scheme in an engineering case, and reasonable and realistic scoring results were obtained.

Intelligent decision method for the position and attitude self-adjustment of hydraulic support groups driven by a digital twin system

The hydraulic support group is often skewed during the propulsion process, but the existing technology cannot autonomously adjust its position and attitude according to the actual working conditions. This paper proposes an intelligent decision method for the position and attitude self-adjustment of hydraulic support groups driven by a digital twin system. The method reconstructs the position and attitude of the hydraulic support, completes virtual decision-making, virtual execution and strategy optimization for the adjustment behavior in the virtual space, and finally derives the optimal control command to reversely control the real support to achieve the self-adjustment of the position and attitude of the support. First, the full position and attitude parameter matrix and the 3D workspace are defined to describe the position and attitude of the hydraulic support, and a pose analysis method of the hydraulic support group is proposed based on the lengths of the column and balance jack. Second, the method of predicting the ideal pose of the support, the synergistic method of the adjustment device, and the method for the calculation of its elongation are studied. Finally, a digital twin system facing physical support prototypes was built in a laboratory environment, and three key experiments were carried out for the virtual reconstruction of the support pose, the intelligent decision-making and virtual execution of the adjustment behavior, and the autonomous adjustment of the physical prototype based on virtual decision-making results. The experimental results show that virtual decision-making can effectively guide the real adjustment behavior, and the applicability and influencing factors of the adjustment effect are further studied. The proposed method compensates for the vacancy of hydraulic support self-adjustment technology, and can provide a reference for the intelligent control of fully-mechanized mining equipment.

Intelligent Decision Method of Multi-Agricultural Commodity Model Based on Machine Learning

China Agricultural Outlook publishes the outlook report on major agricultural commodities and the estimates of the supply–demand balance sheet of agricultural commodities in the next decade, so as to release agricultural information and guide the development of modern agriculture. A large amount of data has been accumulated in the work associated with agriculture outlook in recent years. This paper will optimize the agriculture commodity model through deep learning and create an analysis tool based on long short-term memory (LSTM) deep learning by comprehensively considering the key factors of supply and demand of agricultural commodities including the output, consumption and price and combining the impact of complex natural, social and economic factors. In this way, the close relevance of different varieties and multi-variable strong coupling of the analysis and prediction model of major agricultural commodities can be solved. The “random sampling” and “stress on causality” of traditional complex agriculture analysis models are replaced by the “whole data” and “emphasis on relevance over causality”. The intelligent decision method of agricultural commodity model based on deep learning proposed in this study can effectively improve the analysis efficiency and accuracy of multi-variety coupling model of agricultural commodities (at least by 15%), and enhance the intelligence of the supply–demand analysis and prediction, especially with the accumulation of future data, the prediction accuracy will continue to improve. Machine learning has been regarded as an effective method to provide forecast and early-warning of future agricultural development in a timely manner based on real-time monitoring of agricultural data.

Intelligent decision method of sludge bulking using recursive kernel principal component analysis and Bayesian network

Efficient and accurate decision is essential to avoid sludge bulking and ensure the stable operation of wastewater treatment process (WWTP). However, due to the strong nonlinear characteristic, time-varying characteristic and changeable triggering causes of sludge bulking, the decision performance is not optimistic. In this paper, an intelligent decision method combining recursive kernel principal component analysis (RKPCA) and Bayesian network (BN) is proposed to accurately detect the occurrence, identify the root causes and severity level, and provide the restraining measures for sludge bulking. The proposed intelligent decision method consists of three main parts. First, a RKPCA model based on the sliding window mechanism is proposed to extract the nonlinear and time-varying characteristics of WWTP. Then, the proposed RKPCA can assist the decision method to detect the sludge bulking with high accuracy. Second, a mean reconstruction contribution of RKPCA (MRC-RKPCA) method is designed to preliminarily isolate all candidate faulty relevant variables of sludge bulking. Then, the MRC-RKPCA method is able to provide the relationship between the sludge bulking and the corresponding faulty variables for the decision method. Third, a decision model using BN is developed to determine the root causes and the severity of sludge bulking through reverse and forward inference, and provide corresponding restraining measures. Then, the proposed decision method can obtain accurate strategies to suppress the sludge bulking. Finally, the proposed intelligent decision method was applied for a real WWTP. The results demonstrate the proposed intelligent decision method can obtain better decision performance than some existing methods.

An efficient intelligent decision method for bionic motion unmanned system

In this article, we propose an efficient intelligent decision method for a bionic motion unmanned system to simulate the formation change during the hunting process of the wolves. Path planning is a burning research focus for the unmanned system to realize the formation change, and some traditional techniques are designed to solve it. The intelligent decision based on evolutionary algorithms is one of the famous path planning approaches. However, time consumption remains to be a problem in the intelligent decisions of the unmanned system. To solve the time-consuming problem, we simplify the multi-objective optimization as the single-objective optimization, which was regarded as a multiple traveling salesman problem in the traditional methods. Besides, we present the improved genetic algorithm instead of evolutionary algorithms to solve the intelligent decision problem. As the unmanned system’s intelligent decision is solved, the bionic motion control, especially collision avoidance when the system moves, should be guaranteed. Accordingly, we project a novel unmanned system bionic motion control of complex nonlinear dynamics. The control method can effectively avoid collision in the process of system motion. Simulation results show that the proposed simplification, improved genetic algorithm, and bionic motion control method are stable and effective.

Intelligent decision method for main control parameters of tunnel boring machine based on multi-objective optimization of excavation efficiency and cost

Timely and reasonable matching of the control parameters and geological conditions of the rock mass in tunnel excavation is crucial for hard rock tunnel boring machines (TBMs). Therefore, this paper proposes an intelligent decision method for the main control parameters of the TBM based on the multi-objective optimization of excavation efficiency and cost. The main objectives of this method are to obtain the most important parameters of the rock mass and machine, determine the optimization objective, and establish the objective function. In this study, muck information was included as an important parameter in the traditional rock mass and machine parameter database. The rock–machine interaction model was established through an improved neural network algorithm. Using 250 sets of data collected in the field, the validity of the rock–machine interaction relationship model was verified. Then, taking the cost as the optimization objective, the cost calculation model related to tunneling and the cutter was obtained. Subsequently, combined with rock–machine interaction model, the objective function of control parameter optimization based on cost was established. Finally, a tunneling test was carried out at the engineering site, and the main TBM control parameters (thrust and torque) after the optimization decision were used to excavate the test section. Compared with the values in the section where the TBM operators relied on experience, the average penetration rate of the TBM increased by 11.10%, and the average cutter life increased by 15.62%. The results indicate that this method can play an effective role in TBM tunneling in the test section.

Application of Machine Learning and CoVaR Model on Intelligent Decision Method

The traditional asset allocation model does not take into account the spread of systemic risk in the process of portfolio optimization, which will lead to great losses in the portfolio when facing financial risks, especially extreme risks. In order to solve this problem, this paper improves the efficiency frontier of Markowitz, takes the factors that cause changes in the rate of return on individual underlying assets into the consideration of systemic risk, applies CoVaR model to measure the spread of systemic risk, and constructs a new asset allocation model based on Mean-CoVaR. The results show that when considering systemic risk shocks, the impact of systemic risk diffusion on Mean-CoVaR portfolio is significantly lower than that of traditional Mean-Variance portfolio. And Mean-CoVaR model is more efficient for portfolio allocation.

Intelligent decision method for supplier selection

In the process of supply chain risk management, selecting the right supplier quickly and accurately is one of the most critical issues for the company. Due to the influence of various risks, improper selection or time-consuming evaluation may lead to the company losing the best time, cost and market share. For the multi-criteria decision-making problem of supplier selection, this study proposes an intelligent method combining the combination of triangular fuzzy number and network analysis method, evaluates the alternative solutions, obtains the supplier ranking result and proves the effectiveness of the method by case studies of the electronics industry. A scientific reference is provided by this method for the development of supplier-selected intelligent systems.

Research on Intelligent Decision Method Based on Grey System Theory in the Prediction of Grain Yield in Jilin Province

In order to improve the prediction ability of grain yield, the grain yield data of Jilin province is taken as the research object, GM(1,1) model and GM (1,N) model is established. According to the correlation analysis results, some key correlation factors of grain yield are selected into the prediction model, including the amount of chemical fertilizer, the end head of livestock, the grain sown area etc, and carries on the forecast to the grain yield of 2010-2012. The predicted results show that the average prediction error of GM(1,1) model is 6.6705% and the average prediction error of GM (1,N) model is 5.2020%. Through the comparative analysis, GM (1,N) model has higher prediction accuracy for the multiple attribute intelligent decision problem, it can be used for the prediction of grain yield in Jilin province.

Research on Intelligent Decision Method Based on Grey System Theory in the Prediction of Grain Yield in Jilin Province

In order to improve the prediction ability of grain yield, the grain yield data of Jilin province is taken as the research object, GM(1,1) model and GM (1,N) model is established. According to the correlation analysis results, some key correlation factors of grain yield are selected into the prediction model, including the amount of chemical fertilizer, the end head of livestock, the grain sown area etc, and carries on the forecast to the grain yield of 2010-2012. The predicted results show that the average prediction error of GM(1,1) model is 6.6705% and the average prediction error of GM (1,N) model is 5.2020%. Through the comparative analysis, GM (1,N) model has higher prediction accuracy for the multiple attribute intelligent decision problem, it can be used for the prediction of grain yield in Jilin province.