17 results about "Effective method" patented technology

The invention relates to the technical field of flexible job shop scheduling in intelligent manufacturing, in particular to an optimization method for a flexible job shop scheduling problem considering adjustment time, and the method comprises the steps: initializing parameters of a collaborative variable neighborhood search algorithm; initializing a population; jittering operation is executed, and two new solutions are generated through the jittering operation; executing a variable neighborhood descent operation; updating the current algebra; judging whether a restart strategy is executed or not; and selecting a solution with shorter maximum completion time in the improved solutions generated by the collaborative variable neighborhood search algorithm as an initial solution of the constrained programming model, and further optimizing the quality of the solution through the constrained programming model. The invention provides an effective method for solving the problem of flexible job shop scheduling considering the adjustment time of sequence dependence, and makes substantial progress in reducing the maximum completion time of the shop.

The invention belongs to the technical field of computer aided design (CAD), and particularly relates to an automatic shaft part size mark generation method applying Ontology. The method specifically comprises the following steps: (1) constructing a shaft part dimension mark generation body; (2) establishing a size mark automatic generation SWRL inference rule; (3) extracting the surface structure and related constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, reasoning the size marks and outputting a generation result. Constructing an ontology reasoning knowledge framework for the dimensioning domain knowledge by using the ontology, and reasoning implicit knowledge according to dominant domain knowledge; and in combination with a semantic network rule language rule base, performing reasoning on the specific structure and constraint conditions of the shaft part to generate size marking information. According to the method, a consistent knowledge description framework generated by the size marks of the shaft parts can be provided, so that a computer can automatically generate related size mark information according to structure information and constraint conditions of the shaft parts, and a quick and effective method is provided for intelligent generation of the size marks of the shaft parts.

The invention discloses a DO385 test case set-based ACAS X software standard conformity verification method, which comprises the following steps of: generating a test sequence table according to 7 groups of 422 test cases specified by DO385 through software programming, and supporting realization of various programming languages; aCAS X software is operated, and TRM, STM and COST result files are generated by taking the test sequence table cases as input; comparing a result file with a standard result file provided by DO385 through computer software programming, recording variables and errors which do not meet the tolerance requirement, and updating the test sequence table according to a comparison result; correcting the ACAS X software for the failed use case, and iteratively testing until all 422 test cases pass; the standard conformity can also be compared in batches and visually by using automatic folder comparison software. By setting and updating the test sequence table, a systematic and optimized test process is provided, iteration is achieved, the implementation mode has flexibility and universality, and an effective method is provided for standard conformity verification of ACAS X software.

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 relates to a method for improving the precision of an encoder, relates to the technical field of circuit systems, and aims to solve the technical problem that an effective method which is simple and reliable in operation and high in real-time performance does not exist in phase calibration of two paths of sine and cosine signals in the prior art. The method comprises the following steps: collecting the amplitude of an analog signal; gains of sine / cosine signals are calculated and amplified to ideal amplitudes; revising the sinusoidal signal again; equally dividing the fine code signal into a specific number of parts; and respectively calibrating a long period and a short period, and carrying out software algorithm compensation on an error fitting curve. According to the method for improving the precision of the encoder, on the basis of an MR3 + DSP28335 technology, two paths of sine / cosine original analog signals are subjected to hardware adjustment, and then the amplitude, the direct current and the phase are revised again by adopting a software algorithm, so that the two paths of analog signals entering a subdivision program have good orthogonality and symmetry, and the subdivision precision is improved.

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 invention discloses a metamorphic test group generation method based on an isolated forest and an adaptive random test, which comprises the following steps of: firstly, generating a group of initial test cases, and judging whether defects of a to-be-tested program can be detected or not; if no program defect is detected, putting the program defect into a selected test case set; constructing an isolated forest model by using the selected test case set; thirdly, constructing a candidate test case set, judging whether a program defect is detected by the candidate original test case, and if the program defect is not detected, generating a candidate subsequent test case according to the metamorphic relationship; and calculating abnormal scores of the candidate test cases by using an isolated forest model, putting the test cases of which the abnormal scores are higher than an abnormal threshold value into a selected test case group set, and finally judging whether program defects are detected or not. Through the method provided by the invention, an effective method is provided for generating the test case of the high-dimensional input scientific calculation program in the metamorphic test, and the generation efficiency is improved while the boundary effect problem in the generation of the test case is relieved.

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 provides a method for effectively solving the problem of insufficient available memory of a 32-bit program process space, which comprises the following steps of: analyzing a source code of a main program, and splitting a functional module to obtain a plurality of sub-functional modules; developing a new program matched with the sub-function module, adding a user-defined message processing function into a sub-program of the sub-function module, unifying a scheduling interface of the sub-function module, and completing program packaging of the sub-function module; creating a shared memory area, and fusing the windows of the main program and the subprogram in the shared memory area to obtain a unified interface; performing data interaction and program function scheduling between the windows, and developing a fusion program to complete cross-process communication of window messages; wherein a socket technology is introduced to carry out network communication between programs. According to the method, the memory occupation can be reduced, an application group with complete and operable functions and a complete, continuous and uniform man-machine interaction interface is provided for a user, and the user experience is greatly optimized.

The present invention is directed to a method which enables a device to be configured particularly efficiently and control commands to be selected particularly efficiently. The present invention allows for the selection options to have to be assigned only once in the two-dimensional storage structure, and thus an efficient method is provided which consumes less processing power and memory. The advantage is that parameters do not have to be repeatedly read out or selected and redundantly stored. As a result, errors are avoided and far less memory is required than in the prior art. The invention is also directed to a correspondingly designed system arrangement, which is designed to carry out the method. A computer program is also proposed, which provides commands suitable for implementing the method and operating the system arrangement.

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).