10 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 invention discloses a cross-modal representation learning method based on a pre-trained multi-modal large model, and the method comprises the steps: carrying out the feature extraction of input data at different scales through a teacher model and a student model, and carrying out the knowledge extraction of the student model through the combination of comparison distillation, feature distillation, similarity distillation and hard negative sample distillation; and the weight is dynamically adjusted according to the loss of different distillation methods, and the distillation process is optimized. The invention provides an effective method for extracting the knowledge of the multi-modal large-scale pre-training model, so that the performance and accuracy of the lightweight student model are improved, and the method has the advantages of high efficiency, simplicity, convenience and the like, and can be widely applied to the fields of cross-modal representation learning and the like.

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 an effective method of basic input and output system options and computing equipment. The method comprises the following steps: acquiring a basic input / output system option modification instruction; the basic input and output system option modification instruction comprises an option identifier of at least one option to be modified and a target option value of each option to be modified; according to the option identifier of the at least one to-be-modified option and the target option value of each to-be-modified option, modifying the option value of each to-be-modified option in the memory; according to the option identifier of the at least one option to be modified and a cold restart table, determining a restart mode of the computing device; the restart mode is a cold restart mode or a hot restart mode; the cold restart table comprises option information of at least one cold restart option; according to the restarting mode, restarting the computing equipment to enable the option modification value of the option to be modified to take effect; the option modification value of the to-be-modified option is the modified option value of the to-be-modified option. And the effective efficiency of the options is improved.

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 provides a quality reward and punishment strategy selection method based on a cumulative foreground theory and a tripartite evolutionary game. The method comprises the following steps: step 1, determining a tripartite game theoretical model; 2, setting a strategy set and parameter composition: setting game model parameters based on game subject strategy behaviors and cost and income factors; 3, constructing a payment matrix and a revenue function considering the cumulative foreground; step 4, solving and discussing equilibrium points of the game model: calculating and copying a dynamic equation, discussing possible equilibrium points of the game model, and further obtaining an equilibrium strategy of each game main body; and 5, the data input system finds and draws a reward and punishment scheme: setting parameter assignment, and proposing the reward and punishment scheme and example reference verified by the model system by adjusting the reward and punishment of the production enterprise on employee behaviors. According to the method, the quality supervision data is used as input of quality reward and punishment scheme formulation, the bounded rationality of people is considered, and scientific and effective method guidance can be provided for enterprises to select quality reward and punishment strategies.

The invention relates to a distributed zero-latency multi-product batch process production scheduling method. The method comprises the following steps: establishing a distributed zero-latency multi-product batch process production scheduling model taking minimization of maximum completion time as a target function; the model is solved by adopting a discrete coding whale optimization algorithm to obtain a scheduling scheme, and in the solving process, the calculation process of the target function is accelerated by utilizing a time interval matrix. Compared with the prior art, an effective method is provided for distributed zero-latency multi-product intermittent process production scheduling, and rapid positioning of an optimal scheduling scheme is ensured; the invention provides a new objective function calculation method, which can save the time required for calculating the objective function, can quickly find the most suitable scheduling scheme in more candidate schemes through more efficient objective calculation, and is beneficial to optimizing resource allocation and improving production efficiency.

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).