The research of landslide monitoring technology plays a key role in preventing landslide disasters, which is not only reflected in the pre-disaster early warning, but also provides a theoretical basis in the post-disaster reconstruction. A clear understanding of the history of landslide monitoring technology can not only understand the development situation of landslide monitoring, but also contribute to the improvement and update of the monitoring technology in the future. Through summarizing the work of a large number of domestic and foreign scholars, the research progress of landslide monitoring technology was expounded from three historical stages: manual monitoring stage, semi-automatic and manual monitoring stage, and semi-intelligent and automatic monitoring stage. The main instruments and methods of common monitoring technology in each period are reviewed. Combining the latest landslide monitoring technology research status, some problems necessary for further research and discussion were proposed from the four perspectives of data inversion, technology comprehensive, intelligent and low-cost technology of landslide monitoring technology.

The prevailing techniques for seismic phase analysis encompass waveform classification, seismic attribute feature mapping, and seismic geomorphological delineation. The waveform classification method is a well-established and extensively utilized technique for lithological, sand body, and oil and gas reservoir prediction. Nevertheless, the conventional approach relies on equal-length time window waveform similarity, which is only pertinent to the stable zone of formation thickness. As a consequence of changes in formation thickness, equal-length seismic waveforms cannot reflect the complete lithological information, or conversely, may lead to the phenomenon of ‘time-warp’, which in turn affects the accurate revelation of the relationship between reservoirs and waveforms. A seismic waveform classification method for unequally thick layers, intending proposes to reduce complexity and enhance classification efficacy. In comparison to the traditional classification method, this approach transfers unequal seismic signals from the time domain to the Hilbert domain with constant bandwidth, thereby ensuring the completeness of the waveform extraction and simplifying the traditional two-dimensional self-organized feature mapping network into a structure with fewer neurons and a one-dimensional output layer. This adaptation is better suited to the resolution of the seismic data and the need for classification efficiency. The enhanced network retains the capacity to modify the field and value of weight correction by with the responsiveness of the output neurons to the input neurons, thereby facilitating effective control of the network size, reducing the complexity of classification calculations, and enhancing classification efficacy. The practical results confirm the effectiveness of the method and significantly improve the accuracy of waveform classification.

As the forefront of technological innovation, industry occupies a central position in promoting the development of productive forces. Accelerating new industrialization is an inevitable choice for generating new productivity, shaping new competitive advantages and stimulating new economic momentum. From the perspective of qualitative reconstruction, new industrialization leads the improvement of the efficiency of labor materials, promotes the expansion of the scope of labor objects, stimulates the ability of workers to leap, reshapes the basic elements of productivity, and promotes the leap and qualitative change of its optimized combination, giving birth to new quality productivity. From the perspective of base support, new industrialization builds a solid base for the development of new quality productivity in five dimensions, namely, market, industry, numerical intelligence, safety and greenness, and empowers the emergence of new quality productivity in an omni-directional way.The process of new industrialization can be pushed forward from the network layer, innovation layer, application layer and linkage layer to accelerate the formation and development of new productivity.

Many types of defects are produced during the drilling of holes in carbon fiber reinforced composites, and of all the defects delamination has the most serious effect on the material. Therefore, it is crucial to develop an effective model that can accurately predict delamination in laminated materials. However, materials domain data is characterized by small samples, high latitude and complex relationships, which makes it necessary and feasible to use empirical knowledge to enhance the effectiveness of machine learning modeling. A knowledge-guided machine learning(KGML) model that integrates empirical knowledge and data-driven modeling is used to predict laminated material delamination, the fact that empirical knowledge is incorporated into the loss function as an adaptive weighting in order to enforce physical constraints during the training process. Finally, by comparing the prediction performance of the model without knowledge and the model with knowledge, the R² of the model with knowledge was improved from 0.79 to 0.91, which successfully demonstrated the advantages of empirical knowledge-based machine learning, and provide a generalized approach for delamination prediction to reduce the experimentation time and cost for researchers.

In order to reduce the floor area of ground equipment for sand control operations, meet the fast and flexible installation and disassembly of subsequent sand control operations, as well as the post-phase inspection, maintenance, inspection and repair, the layout of sand control equipment and process in deepwater gas field was studied. The modular adaptability analysis, modular equipment design, selection and module key connection design were carried out, the modular combination of surface process equipment was realized to ensure the safety and smooth completion of development Wells. Finally, the finite element analysis software SACS was used to model and simulate the module, and the load condition of the module was analyzed and calculated. The modular related design satisfies the design index and safety analysis of the operation, and has certain reference significance for the popularization of the relevant modular technology and scheme in the self-operated deepwater development projects.

In response to the technical difficulties of the deep exploration and evaluation well FG119 in the Xujiahe Formation of the Sichuan Basin, an “S” shaped wellbore trajectory was designed, with limited ground conditions, resulting in increased difficulty in trajectory controlling and high safety risks of drilling tools. The implementation plan of the ϕ165.1 mm slimming well was adopted, and the development of fractures in the Xujiahe Formation and the high and low pressure interlayers led to high well controlling risks. Therefore, optimization technology for slimming well wellbore structure, complex trajectory optimization design, establishment of four pressure profiles in fractured formations, and supporting technologies such as pre bending dynamic drilling tool combination, fine pressure control, and friction reduction were carried out. This ensured the smooth completion of the first deep “S” shaped slim well in the work area, providing technical reference for the subsequent construction of complex wellbore track wells in the block.

In response to the demand for reservoir protection during well maintenance of low-pressure gas wells in the South China Sea L gas field, the optimal evaluation of the system types and concentrations of foaming agents and foam stabilizers in the laboratory was carried out. A set of foam workover fluid system and preparation process suitable for L gas field were constructed. The system has a half-life of 60~72 h under reservoir conditions of 80~90 ℃ and a maximum temperature resistance of 100 ℃, demonstrating good temperature resistance and stability. The foam density can be as low as 0.5 g/cm³, matching with the pressure coefficient of the target reservoir, and effectively reducing the leakage. Through core damage test, the recovery rate of permeability of high permeability core after being polluted by foam workover fluid can reach 95.1%, indicating that the system has excellent reservoir protection performance.

Technological innovation serves as a significant driving force for economic growth. Taking the nine provinces in the Yangtze River Basin as the research object and based on panel data from 2011 to 2022, the impact of technological innovation factors on industrial economic development was studied. Empirical findings reveal that the level of technological innovation factors has an overall positive influence on regional industrial economic development. Technological innovation significantly impacts industrial economic development through various means, such as optimizing industrial structure, enhancing production efficiency, driving consumption structure upgrading, and creating new economic growth points. After conducting numerous robustness tests, this conclusion remains valid. Finally, based on the research findings, policy suggestions are proposed to promote the coordinated development of science, technology, and the economy in the region.

Promoting design driven product innovation is currently an important measure for enterprises to enhance product competitiveness. Therefore, based on the theory of cue utilization, the intrinsic mechanism and boundary conditions of design driven product innovation on consumer purchase intention were explored. The research results indicate that aesthetics, functionality, and symbolism all have a significant positive impact on consumer purchase intention, and psychological ownership plays a mediating role between the aesthetics, functionality, and symbolism dimensions of design driven product innovation and purchase intention. In addition, brand trust can positively regulate the relationship between aesthetic and functional dimensions and psychological ownership.

Drawing on the coupling theory in physics, the coupling and coordinative mechanism of higher education agglomeration and regional innovation was analyzed, an evaluation index system of the two was established. Using the coupling coordination model to measure the coupling coordination degrees of the 11 cities in Zhejiang Province from 2005 to 2019, the spatial-temporal evolution process was analyzed by using spatial statistical tools. The results show that from 2005 to 2019, the comprehensive development index of higher education agglomeration in Zhejiang Province presented a fluctuating upward trend, and the comprehensive development index of regional innovation showed a spiral upward trend, higher education and regional innovation ability in Zhejiang Province has undergone an overall development, but there is an imbalance in the 11 cities. The coupling coordination degree of higher education agglomeration and regional innovation in Zhejiang Province is generally on the rise, the overall coupling coordination types was “near maladjustment and decline” in 2005, while the types became “grudging coordinated development” in 2019. The coupling coordination development level of higher education agglomeration and regional innovation in Zhejiang Province presented the spatial pattern of “coexistence of polarization-balance” and “obvious gradient differentiation of center-periphery”, the single-polarization pattern with Hangzhou as the pole transferred to the multi-center pattern with Hangzhou, Ningbo, Jiaxing as the centers, presents a gradient difference between northeast Zhejiang and southwest Zhejiang. Based on this, some countermeasures and suggestions are put forward for the coordinated development of higher education agglomeration and regional innovation in Zhejiang Province.