20 results about "Python (programming language)" patented technology

The invention provides a legacy system-oriented large-model-driven API document automatic generation system, belongs to the crossing field of artificial intelligence and software development, and provides a multi-modal data fusion and closed-loop verification mechanism aiming at the defects of a traditional API document generation method in the aspects of semantic comprehension, dynamic context capture and multi-technology stack adaptation. A code static feature and a dynamic track during operation are analyzed through a multi-source data acquisition module, and an interface semantic feature is extracted in combination with a field self-adaptive large model of a semantic enhancement analysis module; deducing an implicit service rule by fusing static / dynamic characteristics through a graph neural network, and generating a standardized document conforming to an OpenAPI specification through a parameterized template generative adversarial network (PT-GAN); and finally, performing three-level verification and closed-loop optimization through a sandbox environment. The method supports a heterogeneous system of 16 programming languages such as Java / C + + / Python, interface version changes can be automatically recognized, document patches are generated, the problems of missing and outdated system documents and low maintenance efficiency are solved, and maintainability and integration efficiency of enterprise-level systems are remarkably improved.

The invention discloses a method and a system for quickly constructing a railway station structure engineering demand parameter matrix, which are characterized in that a time-history load function, a time-history load working condition and a ground acceleration of a railway station structure model are automatically created and set through a Python programming language on the basis of an application programming interface (API) opened by MIDAS Gen design software, and the time-history load function, the time-history load working condition and the ground acceleration of the railway station structure model are automatically established and set through a Python programming language. The method comprises the following steps of: firstly, carrying out time-history analysis on a plurality of key components, then carrying out elastic-plastic time-history analysis in batches, extracting a corner of a non-elastic hinge of the key components and interlayer acceleration of the floors, and finally, automatically assembling and forming an engineering demand parameter (EDP) matrix, so that the convenience and feasibility of shock resistance toughness evaluation of the railway station are remarkably improved.

The invention relates to a programming language migration operation method and device, an M language function can be recognized from M language script codes and converted into a Python function, in addition, for other codes with complex M language grammar forms, the other codes can be uniformly mapped and converted into the Python function, and through the functions of context and the like in the Python function, the programming language migration operation can be achieved. Different functions are implemented or different processing logics are executed for different types. The realization difficulty is greatly reduced, complex functions can be quickly realized, and the M language can be effectively operated without depending on a core library of Matlab.

The invention discloses a robot program verification and real-time data twinning method, relates to the field of robot technology and digital twinning technology, and aims to optimize task design and path planning of a robot in actual work. The method specifically comprises the steps that a script is written by utilizing basic programming languages such as C #, Python and Java, and offline programming software is developed for the robot according to a data interface matched with the corresponding robot; after a working program is written for the robot by using off-line programming software, deploying three-dimensional models of the robot and the production field environment into an unreal engine to form a visual three-dimensional scene, and adding colliders and physical attributes for all the models; robot posture data pre-generated by offline programming software are accessed to a three-dimensional scene, occurrence conditions of accidental collision, joint overrun and singular poses are detected, and after correctness of an offline program is verified, robot data are accessed to an entity robot and the visual three-dimensional scene at the same time. And real-time monitoring and data linkage collaborative display of real-time physical robot operation are realized.

Legacy program applications written in a SQL-like programming language such as FOCUS can be automatically converted to modern languages such as Python / PySpark. The FOCUS files are parsed and translated into SQL statements which can be stored, along with associated metadata, in a transformation configuration file. The configuration file can then be converted into Python / PySpark code.

This artificial intelligence-based pollen recognition and counting system is designed to recognize and count healthy, unhealthy, and dead pollen in microscopic images with high accuracy and efficiency. This system uses a convolutional artificial neural network method that can detect objects in images quickly, precisely, and lightly. This system also has interactive software created with the Python programming language and the streamlit framework, which can display and store the results of pollen recognition and counting. This system is created by collecting, labeling, and augmenting pollen data, training and evaluating an artificial intelligence model with accuracy and error metrics, and integrating the model into the software. This system is useful for helping plant breeders optimize the crossing process.By utilizing this system, plant breeders can determine the quality and quantity of pollen more accurately and effectively, this is an effort to reduce the number of rejected seeds, which affects the productivity and sustainability of plantations.

The application relates to a programming language migration running method and device, which can recognize M language functions from M language script codes, convert the M language functions into Python functions, and uniformly map and convert the remaining codes with complex M language syntax forms into Python functions, so that different functions or different processing logics for different types are realized through the context and other functions in the Python functions. The implementation difficulty is greatly reduced, complex functions can be quickly realized, and the M language can be effectively run without depending on the core library of Matlab.

The present invention provides a method for determining and drawing the vertex projection of a photovoltaic module string under any slope, which can provide a method for determining and drawing the vertex projection of a photovoltaic module string under any slope at multiple moments and in multiple seasons. By using programming languages such as Java, C++, C#, and Python and the AutoCAD ActiveX interface technology, the projection is automatically drawn on CAD, and this projection is used as the basis for processing the layout spacing of the components, improving the accuracy of the photovoltaic array layout and the design efficiency of mountain photovoltaic power generation.

The invention relates to a temporary horizontal cable force and tension optimization method in the arch rib lifting construction process, and the method is characterized in that optimization variables, objective functions and constraint conditions are defined, and a temporary horizontal cable and lifting cable grading tension scheme is formulated based on a differential evolution algorithm by jointly applying a Python programming language and an API interface of a Midaas / Cill NX finite element platform. According to the method, on the premise that it is guaranteed that closure section deformation at the arch springing is well controlled in the lifting process, the iterative calculation efficiency can be greatly improved, the requirement for hardware equipment is low, the temporary horizontal cable force suitable for the arch rib lifting construction process can be economically and rapidly obtained, and therefore it is guaranteed that follow-up closure construction is conducted smoothly, and the construction period is shortened. And meanwhile, graded tensioning of the temporary horizontal cable and the lifting cable is beneficial for guaranteeing the stress safety of the structure in the construction process.

The application is a kind of thermal wave detection method for internal hole defects of plate in complex environment, relates to a kind of section detection method, the method is edge detection algorithm and heat transfer, depth learning is fused to reach detection method;Heat transfer model is interacted by Python programming language through PyAnsys library and Ansys simulation software, material attribute data is established;Deep learning model is composed of yolov5 visual recognition algorithm, and the edge detection algorithm is Canny edge detection algorithm;First, the initial model of heat transfer is established, then the detection results of deep learning and edge detection algorithm are used to correct the simulation model reversely, finally the edge of internal hole defect of the plate is detected;The application detects the plate defect by the fusion of edge detection algorithm and finite element heat transfer model deep learning, avoids the inaccuracy of hole edge detection caused by low contrast, unclear boundary and noise area interference of thermal image in complex environment.

The application discloses a kind of based on free bulging thin-walled pipe flow stress measurement method, including establishing database, writing depth learning algorithm, bulging test;The database is established by using ABAQUS finite element software to establish thin-walled pipe free bulging model, different flow stress curves are constructed based on Swift flow stress mathematical model and input into model, gather pipe material bulging height curve, and establish the database of corresponding relationship;The depth learning algorithm is written by using Python programming language as model algorithm platform, and the neural network structure is established, the data is forwardly transmitted and the reverse transmission is carried out by using error back propagation method, and the depth learning algorithm is gradually improved by continuously learning data;The bulging test is carried out by bulging test to pipe material, and the flow stress of pipe material is obtained by using depth learning algorithm;The application can improve the simplicity of pipe material flow stress measurement method.

The invention provides an automatic accounting and completion settlement method and system based on a code mapping function point and a medium, the automatic accounting and completion settlement method based on the code mapping function point comprises six steps from A) to F), and the system comprises a code collector, an analysis engine, a class role classifier, an element meter, an evidence chain and audit body and a report generator. The method is a function point automatic accounting and completion settlement technology oriented to a software project completion stage and based on source code role mapping. The core application scenarios of the method comprise function point list generation after software project completion, budget and completion difference reconciliation, reuse / secondary development cost conversion and multi-environment deployment pricing, the method can adapt to software systems developed by multiple programming languages such as Java, C # / . NET, Go, TS / Node, Python and the like, and objective and traceable technical support is provided for software cost auditing.

The invention discloses a method for evaluating the interaction rock breaking efficiency of a PDC drill bit and a rock stratum. The evaluation method comprises the following steps: establishing a PDC drill bit three-dimensional model based on parameterization of a Python programming language; rock parameters are tested through a laboratory triaxial rock testing machine; the PDC drill bit three-dimensional model and rock parameters are utilized, and dynamic rock breaking analysis is conducted on interaction of the PDC drill bit and a rock stratum through a numerical simulation method; evaluating the rock breaking efficiency of the drill bit according to the overall mechanical specific energy MSE of the drill bit and the load of each cutting tooth by utilizing a dynamic rock breaking analysis result; the rock breaking efficiency of the PDC drill bit is evaluated through secondary development, and the structural design of the drill bit is optimized. The method has the beneficial effects that systematized and complete drill bit mechanical specific energy analysis is carried out by changing the geometric shape of the drill bit, rock properties, drilling parameters and the mineral content in heterogeneous rock, and the rock breaking law of the drill bit is evaluated, so that the drilling speed increasing effect is obvious, and the drilling cost is effectively reduced.

The invention discloses a cross-normal-form automatic conversion method and system for parameterized geometric modeling scripts and a medium, and provides a conversion architecture based on double-layer abstract syntax tree (AST) mapping and state stack management for a semantic gap between an implicit structure entity geometry (CSG) description language (such as OpenSCAD) and an explicit boundary representation (B-rep) programming language (such as CadQuery / Python). Deducing logic from an implicit functional list in the source script by adopting a recursive structure mapping mechanism, decoupling and reconstructing into an explicit implicit circulating control flow in a target language; a statement container stack is used for solving the isolation and recombination problems of hierarchical action domains; and identifying a specific geometric combination (such as Minkowski and cubes and spheres in operation) through semantic analysis, and reconstructing the specific geometric combination into efficient function call based on feature modeling (such as chamfer). According to the method, the parameterization design intention of the original script is reserved, and the calculation efficiency of the generative model in the B-rep kernel is remarkably improved.

The application discloses a kind of railway station structure engineering demand parameter matrix fast construction method and system, based on the Application Programming Interface (Application Programming Interface, API) opened by MIDAS Gen design software, through Python programming language, automatically create and set the time load function of railway station structure model, time load working condition, ground acceleration, then batch execution elastoplastic time history analysis, extract the rotation angle of key component inelastic hinge and floor interlayer acceleration, finally automatically assemble and form engineering demand parameter (Engineering Demand Parameter, EDP) matrix, significantly improve the convenience and feasibility of railway station seismic resilience evaluation.

The invention relates to the technical field of vehicle design and development simulation analysis, in particular to a misuse load rapid determination method and system based on a virtual test site. According to the method, on the basis of a virtual test field technology in combination with a multi-body dynamic model, through multi-body dynamic simulation analysis, the wheel center longitudinal X load and the tire grounding point YZ-direction load of the whole vehicle can be obtained in the design stage. According to the method, a Python programming language is used for writing a load processing program based on a virtual test field, load signal data obtained through simulation analysis of different road surface types of the virtual test field is processed, a load limit value of tire force is obtained, and the multiple relation between the load limit value and the tire load in the static state is calculated to serve as load coefficients in the X direction, the Y direction and the Z direction. According to the method, the load coefficient is applied to calculation of the external load decomposed by the static load of the multi-body dynamic suspension in the development process of the same platform type vehicle model, and the efficiency and accuracy of multi-body dynamic load decomposition are improved.

The invention relates to the technical field of program design and aerogel production application, in particular to an aerogel formula automatic calculation system based on Python, a GUI application program based on Tkinter, a whole set of aerogel original formula completed in a laboratory, a basic theoretical calculation mode from laboratory amplification to production and raw materials used in the aerogel production process. The invention discloses rapid production conversion application of an automatic calculation formula. According to the Python-based aerogel formula automatic calculation system, the problems that when aerogel formula calculation is used for production, a manual calculation mode is generally adopted, consumed labor cost and time cost are high, and error loss caused by human factors is large are considered, and the purposes of improving accuracy and facilitating operation are achieved; according to the aerogel formula automatic calculation system designed through the Python programming language, different formulas in a laboratory can be more conveniently amplified for production and use, the manual calculation amount is greatly reduced, and the manpower and time cost is reduced.

The invention belongs to a hydrometeorological data processing technology, and discloses a python-based drainage basin rainfall data fusion analysis and display method, which comprises the following steps of: dividing a drainage basin grid to obtain a sampling point set; the rainfall data and the elevation data in a preset time period are preprocessed, and an effective rainfall data set and effective elevation data of the site are obtained; constructing a rainfall-elevation gradient function, calculating the area rainfall of the watershed in combination with various methods, constructing a fusion analysis model through an analytic hierarchy process, obtaining rainfall data at a sampling point set, and calculating the average area rainfall by adopting an averaging method; and generating a surface rainfall spatial distribution thermodynamic diagram time sequence and a surface average rainfall time trend chart. According to the method, drainage basin rainfall data are automatically fused and analyzed through the python programming language, the influence of the terrain on rainfall spatial distribution is comprehensively considered, rainfall spatial and temporal change characteristics are presented in a multi-dimensional visualization mode, and accurate core input elements are provided for drainage basin flood forecasting.

The present application belongs to the field of engineering technology and environmental protection, and discloses a sustainable tunnel construction water inflow prediction method based on machine learning technology, which comprises the following steps: data collection and preprocessing; using multivariate statistics and machine learning methods to identify the main control factors of water inflow; using Python programming language, according to the key influencing factors of water inflow, based on random forest, XGBoost and deep neural network model algorithm, respectively establishing efficient water inflow prediction model, and according to the evaluation parameters of the output performance of the initial water inflow prediction model, evaluating and comparing the performance of different efficient water inflow prediction models. The efficient water inflow prediction model with the closest output performance of the training set and test set of each efficient water inflow prediction model is determined as the best water inflow prediction model; and a sustainable tunnel construction water inflow intelligent prediction system is constructed. The present application optimizes the traditional water inflow prediction method by combining various machine learning technologies.

The invention relates to a cross-language vehicle cloud collaborative algorithm cloud integrated deployment method, which comprises the following steps of: packaging an SDK (Software Development Kit) by algorithms, respectively packaging the algorithms written by Python, C or C + + and Java programming languages into the SDK, and packaging a third-party library or a configuration file on which the algorithms depend into the SDK; algorithm SDK integration, algorithm SDK deployment, algorithm SDK calling and algorithm result issuing: the cloud control platform further processes the algorithm result, converts the result into a corresponding instruction or information according to a communication protocol between vehicles and cloud, and issues the instruction or information to the vehicle-mounted terminal through a communication network; algorithm reusability and integration efficiency are improved: by packaging algorithms written by different programming languages into a unified SDK form, differences between languages and environments are shielded, so that the algorithms can be conveniently reused in different projects, and the workload of repeated development is greatly reduced.