The current acupoint automatic positioning technology for massage robots is faced with issues such as dress restrictions, limited application scope, and poor positioning accuracy. A new method for human back acupoint positioning, based on back morphology classification and a multilayer perceptron network morphology classification-multilayer perceptron-based accurate point location(BMC-MPAPL) has been proposed. A substantial collection of diverse human back images, along with skeletal key point positioning techniques, kernel density estimation, and the maximum interclass variance approach, was used to investigate the statistical distribution and effective categorization of back morphologies. To counteract the restrictions imposed by clothing on positioning, a dataset encompassing key back points and the Dazhui acupoint was developed based on classification outcomes, and the automatic positioning of the Dazhui acupoint was accomplished through a deep learning model of the multi-layer perceptron network. Utilizing the Dazhui acupoint's positioning results, a human coordinate system was established, and the automatic positioning of 60 common back acupoints was achieved with the integration of ancient Chinese bone measurement methods. The results show that the Dazhui acupoint positioning model, tailored to various back morphologies, has realized high-precision positioning, with an average accuracy of 94.87% at an allowable error of 20 mm, marking a 13.37% enhancement over models without back classification. For other common back acupoints, the positioning accuracy stands at 91.58% within an allowable error of 20 mm, irrespective of patient attire or background constraints. It is concluded that the method presented effectively enhances the accuracy and applicability of acupoint automatic positioning.

Forest fires pose a significant threat to human lives and property. Accurate prediction of forest fire risk is crucial for disaster mitigation and prevention. Influenced by factors such as terrain, meteorology, vegetation cover, and human activities, the causes of forest fires exhibit regional differences. This study uses historical forest fire events in Muli County, Sichuan Province as the response variable, with terrain, meteorological data, vegetation cover, and human activity data as explanatory variables. Leveraging CatBoost's strengths in handling high-dimensional sparse data and classification problems, a high-precision forest fire prediction model was constructed based on CatBoost. The experimental results indicate that, compared to random forest (RF), extreme gradient boosting(XGBoost), and gradient boosting decision trees(GBDT) models, the CatBoost model achieves higher modeling accuracy and significantly improves forest fire prediction accuracy, with a prediction accuracy rate of 91.36% and an area under curve(AUC) value of 0.970. Predictions made using this model can provide valuable references for the early prevention of forest fires in Muli County.

In order to meet the needs of fast and efficient transportation for coal mine drilling construction operations with trackless transportation conditions, a trackless rubber tyre vehicle integrated drilling rig was used to travel directly from the ground to the drilling site without the need for secondary transportation. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. By comparing the advantages and disadvantages of integral and articulated chassis structures, the narrow body rubber tyre chassis structure was determined based on actual underground tunnel conditions, and key performance parameters for the vehicle design were provided. Based on the overall technical requirements, multiple key technologies of the trackless rubber tyre vehicle were designed and analyzed, and the static load capacity and dynamic characteristics of the frame under two working conditions, namely the driving process and the drilling process, were simulated and analyzed. The simulation results show that the stress and displacement meet the design requirements of the frame load capacity during the walking and drilling processes, and it has good stability. After the installation and adjustment of the rubber wheel chassis, a climbing test and comprehensive performance test were carried out. The test shows that the whole vehicle meets the 15 ° large angle climbing condition, all data meet the standard requirements, and the performance meets the transportation needs of the coal mine drilling rig. It can adapt to the complex working conditions underground in coal mines.

Improper tunnel blasting parameters will seriously affect the safety and quality of tunnel construction. Therefore, the determination of appropriate blasting parameters is an important work in tunnel construction. In order to solve this problem, based on deep learning model-whale optimization deep belief network (WO-DBN) and multi-objective optimization algorithm-non-dominated sorting genetic algorithm II (NSGA-II), an intelligent algorithm for tunnel blasting parameters optimization was proposed. Firstly, using the developed deep learning model WO-DBN, an intelligent model for predicting the safety and quality of tunnel blasting construction based on geological parameters and blasting parameters was constructed. The tunnel crown subsidence and overbreak and underbreak area were taken as the index of construction safety and quality evaluation. Secondly, based on the established tunnel blasting construction safety and quality evaluation model, an intelligent algorithm for tunnel blasting parameter optimization was proposed by using NSGA-II to control crown subsidence, overbreak and underbreak area. Finally, taking the blasting construction of Panlongshan highway tunnel as an example, the proposed new algorithm was verified by engineering application. The results show that the construction parameters obtained by the new algorithm can reduce the tunnel crown subsidence and the overbreak and underbreak area by 27.05% and 60.30%, respectively, and the construction effect is greatly improved. Therefore, the proposed intelligent algorithm can provide technical support for the real-time optimization control of tunnel blasting parameters and provide a strong guarantee for the smooth progress of tunnel construction.

In deep heavy oil reservoirs, substantial heat losses during steam injection are often associated with suboptimal steam chamber development, significantly reducing the efficiency of steam flooding. A novel steam chamber expansion model was introduced, incorporating a wellbore heat loss coefficient derived from vapor-liquid interface theory and heat transfer principles. Compared to existing models, the modified model was shown to predict a more pronounced steam override and a larger steam-swept area at the reservoir top. Validation against field monitoring data reveals a deviation of only 7.61%, demonstrating strong agreement with actual development conditions. Further analysis of the wellbore heat loss rate and steam chamber morphology shows that the heat loss rate peaks early in the injection process and subsequently decreases over time. It is observed that the wellbore heat loss rate increases with greater reservoir depth. Additionally, the mobility ratio is found to be negatively correlated with steam chamber development, while the shape factor is positively correlated, with larger shape factors resulting in a wider steam-swept area and a reduced impact of steam override. The research is closely integrated with theoretical concepts and practical applications, enabling rapid and accurate predictions of steam chamber front positions, optimizing steam injection parameters, and informing the design of development strategies for medium to deep heavy oil reservoirs.

The complex diagenetic facies of the tight sandstone reservoir in the Shaximiao Formation, located in the Jinqiu gas field to Tianfu gas area in the central Sichuan region, pose significant challenges to reservoir evaluation and natural gas exploration and development. Traditional diagenetic facies identification methods are often low in accuracy, heavily reliant on specialized personnel, and time-consuming. There is an urgent need for a diagenetic facies identification method that is highly accurate, cost-effective, and fast. Firstly, based on cast thin section identification data, the lithology of the tight sandstone was determined using a ternary plot of components. Image processing techniques were then used to identify the types and proportions of pores and cements, and the diagenetic facies of the tight sandstone were classified. Secondly, the corresponding 1 019 depth-based well log data for core-divided diagenetic facies were analyzed in terms of distribution range, median, uniformity, and skewness. These 6 types of well log data were standardized to a 0-1 range, and data imbalance was addressed using synthetic minority over-sampling technique (SMOTE). Finally, 10 traditional machine learning algorithms and ensemble learning algorithms were selected for model training and performance comparison. The study found that ensemble learning algorithms, especially the extreme randomized trees (ET) algorithm, performs best in diagenetic facies identification, achieving higher accuracy and F₁ scores than traditional machine learning algorithms. This significantly improved identification accuracy and stability. The ET model was then used to predict the diagenetic facies of the JQ8 well, validating the feasibility of the method. This study provides effective technical methods and references for diagenetic facies research in tight sandstones.

As large-scale fracturing in the development of deep shale gas results in rapid production decline, the accurate understanding of gas-liquid flow patterns is considered essential for stabilizing gas well production. Gas well models with two different wellbore trajectory structures were established, and OLGA software was applied to conduct transient calculations on models with varying tubing depths. The results indicate that in deep shale gas well A₁, slug flow only occurs in the build-up section and above, while in well B₁, slug flow appears in the horizontal section and near the build-up section. Considering cumulative gas production and liquid loading, the optimal tubing depth for deep shale gas wells A₁ and B₁ is at the heel of the horizontal section, while for conventional shale gas wells A and B, the optimal tubing depths are at the heel of the horizontal section and one-third of the horizontal section. Deep shale gas wells are more favorable for drainage and production compared to conventional shale gas wells.The optimal tubing setting depths for conventional shale gas wells with two deep formation wellbore configurations are at the horizontal section heel and the one-third point of the lateral, respectively. It is determined that deep shale gas wells are more advantageous for drainage and production compared to conventional shale gas wells. As the tubing size decreases, both the gas production and the corresponding critical gas flow rate for liquid carryover are reduced. It is also found that the greater the light hydrocarbon content in the shale gas composition, the higher the gas production. This study is intended to provide a reference for determining the rational tubing placement in the drainage and gas production processes of deep shale gas wells.

The offshore heavy oil thermal recovery platform has the characteristics of small space, high steam injection temperature, and high steam injection pressure, with temperatures up to 300 ℃. Once high-temperature and high-pressure steam leaks, it will cause serious consequences and pose a huge threat to equipment and inspection personnel. An effective steam leakage monitoring method was urgently needed. In order to solve these problems, the influence of thermodynamics, fluid mechanics and other factors were considered comprehensively to study the mechanism of steam leakage monitoring in offshore heavy oil thermal recovery. A virtual sensing monitoring method based on mechanism and inference was proposed, and for the first time, the indirect measurement method of steam leakage was applied to steam leakage monitoring in offshore heavy oil thermal recovery. A steam leakage monitoring model was built, and a hybrid sensing technology suitable for steam leakage monitoring in offshore heavy oil thermal recovery was formed for real-time online monitoring of steam leakage. The results show that this method can achieve leak discrimination and leak estimation based on operational data, and directly characterize the failure state of steam leaks online. The minimum detectable leak rate can reach 0.5%, and the accuracy of leak discrimination is above 96.49%. Compared with traditional methods, the minimum detectable leakage rate has increased by 90%, and the leakage discrimination rate has increased by at least 1.6%. This method solves the problems of limited installation of physical sensors on site, difficulty in obtaining effective monitoring data, and limited accuracy due to personnel experience, making up for the shortcomings of on-site monitoring methods for thermal recovery platforms and providing safety guarantees for offshore heavy oil development.

The plunger pump is one of the important power conversion components of the hydraulic system, and its performance directly affects the safety and stability of the hydraulic system. In order to accurately evaluate the operating status of the plunger pump, a plunger pump health status assessment method based on a combination of convolutional neural network(CNN) and long short-term memory network(LSTM) was proposed, and a genetic algorithm was introduced to optimize the parameters of the neural network. The vibration signals of the plunger pump at different operating moments were collected. The energy characteristics of the vibration signals were extracted by using wavelet packets. Combined with the time-frequency domain characteristics of the signals, a dataset of the health status characteristics of the plunger pump was constructed. The health status was identified and classified by the CNN-LSTM method, and the classification results were evaluated by sample entropy. To verify the effectiveness of this health assessment method, it was applied to the experimental test of the plunger pump. The results show that the recognition accuracy of this method reaches 99%, which can effectively improve the accuracy of the health status assessment of the plunger pump.

In order to guide the optimal design and daily maintenance of explosive capacity bombs, the response characteristics of 20 L explosive capacity bombs under the explosion action of explosives were studied. Internal explosion tests of different masses of trinitrotoluene (TNT) explosive capacity bombs were designed. The strain distribution and variation law of the outer wall of the explosive capacity bomb were measured, and the internal explosion impact overpressure of the explosive capacity bomb was obtained. According to the test results, the equal-scale simulation calculation model was calibrated, and the propagation law of shock wave inside the explosive capacitor bomb after the explosive explosion was calculated, and the pressure distribution in the explosion field was obtained. The results show that the simulation results of internal pressure of explosive charge are basically consistent with the experimental results. Under the action of explosive explosion, the internal pressure of explosive capacitor presents a complex structure of multi-peaks, the maximum pressure appears at the corner of explosive capacitor, and the maximum strain of the outer wall of explosive capacitor appears at the lower position of the explosion center plane.