8results about How to "Improve the ability to solve" patented technology

The utility model relates to the field of diesel engine crankshaft parts, in particular to a crankshaft of a diesel engine for teaching. The crankshaft of the diesel engine for teaching comprises a support, mounting grooves and clamping jaws, the top of the support is rotationally connected with a crankshaft body, the middle of the support is fixedly connected with a cross rod, a tank is mounted on the cross rod, the mounting grooves are symmetrically formed in the crankshaft body, balancing weights are mounted in the mounting grooves, and the two clamping jaws are symmetrically mounted at the bottom of the support. According to the utility model, the mounting seat and the balancing weight are arranged on the crankshaft body, and the motor is combined to drive the crankshaft to rotate, so that the state change of the crankshaft in the movement process can be intuitively displayed; meanwhile, the transparent tank body on the cross beam and the shaking condition of clear water in the transparent tank body can clearly reflect the uniformity of the crankshaft counterweight and whether defects exist or not, so that students are helped to better understand the importance of the crankshaft balance.

The invention relates to the technical field of artificial intelligence and evolutionary computing, and particularly discloses a memory annual ring learning-based evolutionary method, which comprises the following steps of: locking elite groups, extracting core declusters, coding task constraint fingerprints, backtracking efficient operators, drawing performance operator portraits, constructing memory annual ring units and storing the memory annual ring units in a memory annual ring library. When facing a new task, analyzing and generating a to-be-matched task fingerprint, matching a historical memory annual ring unit, extracting a core declustering and efficiency operator portrait, remodeling a population basis, generating a dynamic operator weight table, and executing dual-channel collaborative guide evolution; according to the method, through collaborative optimization of the solution space and the strategy space, the convergence speed and the solving performance of the algorithm in a new environment are remarkably improved, conversion from blind exploration to experience guidance is achieved, and the challenges of knowledge migration and experience reuse in dynamic change or staged tasks of a traditional evolutionary algorithm are solved.

This application discloses a method, apparatus, and equipment for optimizing the frequency conversion of fans in a multi-level station ventilation system. The method includes: acquiring a ventilation network model of the multi-level station ventilation system; calculating airflow distribution in the ventilation network model based on a ventilation network solution method, and adjusting the ventilation network model based on the airflow distribution calculation results until the simulated operating conditions of the fans in the ventilation network model match the actual operating conditions; generating at least one candidate fan frequency conversion control scheme based on the underground ventilation airflow demand and the corresponding set optimization model of the multi-level station ventilation system; and determining the optimal fan frequency conversion control scheme based on the at least one candidate fan frequency conversion control scheme and the underground ventilation airflow demand; wherein the set optimization model is a multi-objective mixed integer linear programming model. This enables intelligent frequency conversion control of the multi-level station ventilation system and significantly improves the solution performance of the fan frequency conversion control scheme.

This invention provides a method for the continuous evolution of code agents based on structured memory. Addressing the problem that existing code agents cannot adapt to complex repository-level tasks, this invention proposes a human-machine co-evolution framework. The method mainly includes three stages: First, the memory construction stage, which uses a large language model to extract historical code submission records into standardized structured memories and constructs a vector database; Second, the dynamic execution and self-correction stage, where, when handling new tasks, a two-stage retrieval mechanism is used to obtain the most relevant historical experience to the current problem, and a refined sub-agent is introduced to generate test cases and verification lists, providing real-time guidance and correction to the main agent's generation results; Third, the experience internalization stage, where manually verified task solutions are again converted into structured memories and stored in the database. This invention, by constructing a closed-loop evolution mechanism, significantly improves the accuracy and automation level of code generation and defect repair.

The utility model discloses an electric power system whole process operation control experiment platform comprising an electric power system power plant operation control experiment system, an electric power transmission line operation control and protection experiment system, a transformer station experiment system, a factory power supply and power distribution operation control and protection experiment system and an electric power system whole process operation control system. The power system power plant operation control experiment system comprises a generator set and a power plant control cabinet, the power plant control cabinet is connected with the generator set, the generator set is connected with the power transmission line operation control and protection experiment system, and the power transmission line operation control and protection experiment system is connected with the transformer substation experiment system. The substation experiment system is connected with the factory power supply and power distribution operation control and protection experiment system, has an integrated function of power generation, power transmission, power distribution and power utilization, and is more reasonable and advanced in software and hardware design, so that students can perform whole-process experiment operation of the power system on the same platform and know operation and control of the power system better; and the safety and stability of the experiment process are ensured.

The application discloses a reinforcement learning driven heuristic solving method for multi-ship co-dock maintenance scheduling, and three global search operators are designed for the multi-ship co-dock maintenance scheduling problem. One is a ship crossover operator, which is used to adjust and exchange the maintenance order of the ship. The second is a maintenance task destruction operator, which breaks the original task structure by extracting part of the task from the current maintenance task sequence, providing a larger optimization space for subsequent search. The third is a maintenance task recombination operator, which inserts the extracted maintenance task into the existing maintenance task list to form a new maintenance task order, thereby improving the possibility of obtaining a better scheduling scheme. The execution process of the global search operator is adaptively guided, and a more suitable search operation is dynamically selected according to the state information in the solving process, so as to reduce the invalid search overhead in the meta-heuristic algorithm, improve the utilization efficiency of the computing resources, and further improve the solving efficiency and solving effect of the multi-ship co-dock maintenance scheduling problem.

The utility model discloses an inclined avoiding type mechanical arm, and relates to the technical field of mechanical automation. Comprising a bottom plate, a supporting plate, a first driving lead screw, a first driving seat, a driving plate, a guide rail, a guide chute, a sliding block, a connecting seat, a sliding chute, a second driving lead screw, a second driving seat, a mounting plate, a connecting plate, a connecting disc, a connecting block, a reset spring, a lifting rod, a connecting frame and a mechanical arm main body. The bottom plate serves as a basic supporting part of the whole mechanical arm and provides a stability and installation platform for the whole structure. And the supporting plate is fixed on the bottom plate. According to the inclined avoiding type mechanical arm, through the multi-shaft linkage design, guiding and sliding groove structure optimization, introduction of the reset spring and modular design, the operation flexibility, the movement precision and the stability of the mechanical arm are remarkably improved. The device can effectively avoid collision and damage, adapts to complex working environments, and has wide application prospects and market value.

The invention relates to an intelligent question-driven personalized thinking training system. The system comprises a data acquisition module, a guide strategy modeling module, a dynamic progressive question interaction module and a learning condition analysis and optimization suggestion module. The data acquisition module is used for acquiring guide data; a guidance strategy modeling module performs adaptability verification on the guidance data, and obtains a question style based on a verification result and a student background; vocabulary disassembling is carried out based on the teaching target, a mapping relation is established, and guide configuration data is acquired; the dynamic progressive question interaction module is combined with student input viewpoints, a question sub-module is used for generating a migration question of the teaching target, progressive question data and interaction state data are obtained, and the question data and the interaction state data are stored; the learning condition analysis and optimization suggestion module is used for analyzing the stored data and generating a structured evaluation report for guiding optimization suggestion; and personalized thinking training of students is realized.