6 results about "Effective method" patented technology

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The application discloses a kind of based on knowledge contrast enhancement prompt small sample text classification method, belong to natural language processing technical field, including the following steps: S1: initialization continuous prompt;S2: knowledge template generation;S3: joint training optimization;S4: mask prediction.The application first utilizes BiLSTM to initialize a continuous prompt vector that can be learned and has relevance, then pre-training language model is used as knowledge base, automatically generates a set of positive and negative prompt templates, on this basis, combined with contrast learning and mask language model are jointly trained, finally obtain effective continuous prompt embedding for accurate mask prediction, and provide an effective method that can automatically construct continuous prompt template, and extensive experiments are carried out on 14 data sets, the results show that the accuracy of the method is improved by more than 3.5% than the optimal contrast model, solve the two major problems that continuous prompt is sensitive to initial parameters and easy to overfit in small sample environment.

The invention discloses a code development system and method with self-debugging capability, and relates to the technical field of artificial intelligence and software engineering. The system comprises a code defect acquisition module, a self-debugging model module, a verifiable evaluation module and a strategy updating module. The self-debugging model module generates a candidate code repair scheme based on a large language model; and the strategy updating module adopts an adaptive entropy guide reinforcement learning method, and dynamically adjusts a dominant function for training by calculating the variable quantity of the model strategy entropy so as to excite the model to realize intelligent balance between exploration of a new repair path and utilization of a known effective method. The corresponding method performs model training and code repair based on the system. According to the method, the problems that an existing code debugging method based on reinforcement learning is insufficient in exploration capability and a repair strategy is converged too early are solved, and the diversity and robustness of a model generation repair scheme can be improved.

The application provides a test method and system for embedded Python, and the method comprises the following steps: S1, starting a timer and taking a timer count value; S2, calculating the value of the nth item of the Fibonacci sequence; S3, taking the timer count value again; S4, calculating the difference between the two timer count values; and S5, outputting the difference to a PC through a serial port. The test method effectively verifies the method of translating Python into C++, can greatly reduce the time consumed by Python type judgment, reduce the Flash space occupied by unused functions, and is an effective method for improving the real-time performance of Python, reducing the Flash occupation and enhancing the portability.

The application discloses a code development system and method with self-debugging capability, and relates to the technical field of artificial intelligence and software engineering. The system comprises a code defect acquisition module, a self-debugging model module, a verifiable evaluation module and a strategy updating module. The self-debuging model module generates a candidate code repair scheme based on a large language model. The strategy updating module adopts an adaptive entropy-guided reinforcement learning method, calculates the change amount of the model strategy entropy, dynamically adjusts the advantage function used for training, and stimulates the model to intelligently balance between exploring new repair paths and utilizing known effective methods. The corresponding method performs model training and code repair based on the system. The application solves the problems of insufficient exploration capability and premature convergence of repair strategies in the existing code debugging method based on reinforcement learning, and can improve the diversity and robustness of the repair scheme generated by the model.

The invention relates to a method for operating a safety controller. In order to provide an efficient method for operating a safety controller (3) of a safety-related engineering system (1), a set of safety functions (SF1, SF2) defining logical dependencies between sensor signals (32S) and actuator signals (33S) is provided and grouped into a first class (C1) and a second class (C2) of safety functions (SF1, SF2), a first class (C1) of security functions (SF1) is compiled and linked to obtain a first executable program code, a second class (C2) of security functions (SF2) is compiled and linked to obtain a second executable program code, the first executable program code and the second executable program code being transmitted to a memory (ROM) of the security controller (3), the first executable program code is executed by a first processor (31a) of the security controller (3) and the second executable program code is executed by a second processor (31b) of the security controller (3), generating an actuator signal (33S) from the sensor signal (32S).