67 results about "Python language" patented technology

According to the rapid modeling method for the finite element of the bridge rod system unit disclosed by the invention, a clearer and more reasonable modeling method is constructed by combining rod system structure modeling and BIM modeling logic, and a user can obtain the finite element of the bridge rod system unit by modifying simple underlying data or utilizing a result formed in the method. And analysis and modeling are realized by effectively combining with other software at low cost. The method uses a Python language, and comprises the following steps of: 1, realizing attributes of editing classes by utilizing the Python language; 2, creating a material and a section instance; 3, the bridge structure is divided into different component instances corresponding to the bridge types, boundary information between different components is bound through the boundary class, and loads acting on the different components are determined through the load class; 4, packaging the axis class, and binding the component instance with the axis instance; 5, adding a calculation module, and carrying out mesh generation; 6, obtaining each subdivision point on the axis, and connecting the subdivision points to generate a corresponding unit set; and 7, establishing boundary and load examples.

The invention discloses an automatic administrative boundary fusion charting method and a meteorological charting method based on a python language. Belongs to the technical field of meteorological charting, and particularly relates to the cross technical field of meteorological data processing and geographic information system visualization. The method starts with the construction of a multi-scale adaptive boundary fusion map, so as to solve the problems of cross-scale (such as province-to-village) precision faults and large interpolation errors of complex terrains, and achieve a cross-level administrative division boundary seamless aggregation technology (village level-town level-county level-province level). Therefore, the problem that precision is easy to lose when cross-scale conversion (such as from province to village) is carried out during meteorological mapping is solved.

The present invention belongs to the technical field of engineering design and optimization, specifically, a method for parameterizing and generating surfaces of propeller blades based on B-splines: first, based on the B-spline curve method, the least squares method is adopted to fit the blade section value points at different radii, and the parameterization of the blade section and the outer contour is realized by using methods such as proportion, offset, and coordinate transformation; secondly, based on the B-spline surface method, the propeller blade surface, blade back, blade root and blade top surfaces are efficiently generated; finally, the relevant program is written based on the Python language, and a UML class diagram of the program is given; the present invention programs the entire process through the language Python to realize the parameterization of propeller blades and the automatic generation of surfaces, effectively improving the propeller modeling efficiency and the flexibility of parameterization variable selection in the optimization process.

The invention belongs to the technical field of single-cell multi-omics, and discloses a single-cell multi-omics data analysis system, method and device and a storage medium, a data import module is used for reading sequencing data from different sequencing platforms and storing the sequencing data as S4 objects; the cross-language analysis module is used for calling a single-cell multi-omics analysis tool based on an R language and a Python language to analyze an S4 object according to an analysis process in a unified framework; the deep learning optimization module is used for training a deep learning model based on the cross-language interaction interface and integrating an analysis result of the deep learning model to an object S4; and the interaction and extension module is used for obtaining an analysis process and visualizing an analysis result, constructing a user-defined single-cell multi-omics analysis tool in a plug-in form and loading the tool to the S4 object for analysis. Cross-sequencing platform data compatibility, multi-language tool seamless integration and deep learning framework efficient access are achieved, and the problems of data isomerism, language ecological splitting and algorithm expansibility in the prior art are solved.

The invention provides a hardware health monitoring method, system and equipment of a test machine and a medium. The method comprises the steps that temperature data of to-be-monitored hardware of a test machine and the health state of a disk are obtained according to a first detection rule, and the to-be-monitored hardware comprises a controller, a case and the disk; judging whether the to-be-monitored hardware of the test machine is abnormal or not based on whether the temperature data of the to-be-monitored hardware and / or the health state of the disk meet preset conditions or not; and if the target hardware of the test machine is abnormal, writing alarm information into a log file and executing an alarm operation. In the mode, the python language can be used for coding, and the monitoring of the temperature and health state of each hardware of different types of test machines in a cross-platform manner is supported, so that the production efficiency is improved, the maintenance cost is reduced, and the stable operation of the test machines is ensured.

The invention discloses a grid optimization generation method and device of a one-dimensional and two-dimensional coupling hydrodynamic model, a medium and a product, and relates to the field of data processing.The method comprises the steps that after a grid river network digital elevation model is generated based on original terrain data, a river channel center line and boundary lines of the two banks of a river channel are determined; a hydrodynamic simulation system is used for obtaining an unstructured grid unit information file based on a riverway center line and riverway two-bank boundary lines, a coupling point processing module is written through a python language, and unstructured grid unit information is received and preprocessed; and a python script is operated, a cross product function and a straddling condition are utilized, the verification model is calibrated based on the preprocessed unstructured grid unit information, and grid optimization of the one-two-dimensional coupling hydrodynamic model is completed. According to the method and the device, the grid optimization of the one-two-dimensional coupled hydrodynamic model can be completed under the condition of not depending on third-party hydrodynamic modeling software, so that the accuracy of generating the coupling point of the one-two-dimensional coupled hydrodynamic model and the grid optimization generation speed are improved.

The invention discloses an MJS reinforcement parameter optimization method based on finite element-neural network integration. The method comprises the following steps: firstly, constructing a finite element automatic modeling script system based on an ABAQUS platform, taking geological parameters and reinforcement preliminary design parameters of a construction area as input initial codes of automatic modeling, and then modifying different corresponding reinforcement parameters in the codes to realize parameterized modeling and batch model generation; reading and formatting strength and deformation results in a. Odb file output by a calculation result of the ABAQUS platform by utilizing Python, and fusing the strength and deformation results with on-site deformation monitoring data to form a unified database; then, constructing a forward modeling / inversion model based on PINNs by utilizing a Python language; performing preliminary prediction on site deformation by using the forward modeling model; continuously optimizing and updating the reinforcement design target parameters through the inversion model; and finally, comprehensively deciding and determining a foundation reinforcement parameter combination according to the calculation result of the forward / inversion model.

The invention relates to a fishing boat meteorological route planning system, and belongs to the field of meteorological route planning system construction. The system takes a layered structure as a main framework and is sequentially divided into a data source layer, a data processing layer, a supporting layer, an interaction layer, a front-end UI layer and an access layer from bottom to top; in the design of a front-end page, a Vue framework is adopted, Jave and Python languages are adopted for back-end codes, and a PostgreSQL database is adopted for a database end. And finally, the sea condition meteorological data and the course planning process and result are displayed on a front-end page of the system, and the data can also be transmitted to an Internet of Things platform to be displayed.

The application provides a method for calculating the abundance of a fission product nuclear decay process. According to the characteristics of the decay differential equation, only the decay process is considered for n fission product nuclei, the differential equation set for the abundance change is represented by a matrix, a program is written by using Python language to solve it, and the result of the abundance change of the fission product nuclei with time is obtained. The method has simple program design, less input data types, and fast speed, and can quickly obtain the abundance data of the fission product nuclear decay process. In the case of a processor i5-1155G7@2.50GHz, the time consumption is less than 1 second, which is much faster than similar programs.

The application discloses a scientific paper influence evaluation method and system based on adaptive exceeding index. The application sets four modules, namely, a paper theme determination module, a real-time retrieval module, an adaptive exceeding index calculation module and a visual output module. The paper theme determination module analyzes a given paper title and abstract based on a large language model to obtain one or more paper themes. The real-time retrieval module obtains relevant data of the paper from a publicly available database according to the obtained paper theme. The adaptive exceeding index calculation module calculates the adaptive exceeding index of the specified paper according to the returned relevant data. The visual output module uses Python language to real-time render a PDF file with pictures and texts and a neat interface according to the previously obtained and calculated paper relevant data.

The invention discloses a railway tunnel portal parametric modeling method based on an IFC file, and the method specifically comprises the following steps: 1, extracting three types of core parameter sets of an inner contour section, an open cut tunnel lining section and a portal member based on a railway tunnel general reference graph; 2, on the basis of the IFC file structure features and an IfCpenShell toolkit, entity elements of an inner contour, an open cut tunnel lining and a tunnel portal component are constructed in the IFC file through the python language; 3, based on the spatial assembly constraint condition of the railway tunnel portal, establishing a geometric association relationship among the three parts of entity elements, and forming an IFC file of the whole railway tunnel portal; and 4, importing the IFC file formed in the step 3 into BIM software for parametric modeling. According to the method, the problems of poor compatibility of different software and insufficient data interoperability and expansibility in the existing parametric modeling are solved.

The invention belongs to the technical field of filling type field-induced phase-change materials, and discloses a method for regulating and controlling the performance of a filling type field-induced phase-change material. The method comprises the following steps: (1) data set acquisition: acquiring previous experimental data from publicly published literatures, tables and graphs and forming a data set; (2) data preprocessing: executing all preprocessing by using a Python language in the Juputer Notebook, wherein the Python language is used in the Juputer Notebook; standard Scaler is used to carry out standardization on the data set; (3) constructing a machine learning model: constructing a prediction model of the nonlinear conductivity of the filled polymer composite material by adopting support vector regression, kernel ridge regression, random forest regression, XGBoost regression and gradient enhanced regression; and (4) integrating prediction results by adopting a Stacking integration algorithm. According to the method, the SEL strategy is adopted, the performance of the Eb and alpha models is improved by integrating the five basic models, the prediction performance of the models can be improved, the generalization ability of the models can be enhanced, and the models can well express new and unseen data.

The application discloses an integrated visual intelligent platform of WisdomStore local deployment under a brain-like technology, and the platform main body is composed of a user interface layer, a basic service layer, a language framework layer and a bottom service layer; the user interface layer comprises four modules, namely, a data labeling interface, a model training interface, a model reasoning section and a result display interface; the basic service layer comprises two modules, namely, data processing and model management; the language framework layer is based on a Python language and an ecological system thereof; and the bottom service layer combines public data sets and internal customized data sets in the data source part. The application improves the intelligent level of the industry, improves market competitiveness, promotes digital transformation and promotes scientific and technological innovation.

The application discloses a kind of batch fast creation pile foundation model and batch parameter assignment method and system programmed using Python language, and its method includes: S1: extracting the parameter data required for modeling from pile foundation CAD design drawing, and collating to form standardized data table;S2: using Python language and its pandas data processing library, write the data matching and structured script of pile foundation;S3: in the Dynamo visual programming environment of Revit platform, construct Dynamo node package, and node package is used to define the creation rule and parameter assignment logic of pile foundation model;S4: run Dynamo node package in Revit project file, use intermediate file as the input data of Dynamo node package, automatically batch create pile foundation model and give each pile foundation model corresponding parameter.The application innovatively uses Python space index grid algorithm, realizes the automatic accurate matching of pile foundation center circle graphics and numbered text in CAD drawing, solves the problem of artificial checking of massive pile foundation data.

The invention relates to a driver monitoring system test evaluation method based on a simulation robot. Comprising the following steps: step 1, constructing a simulation robot for a DMS test; 2, designing a DMS test simulation robot control system; step 3, establishing a DMS system; step 4, generating and evaluating a test case; according to the method, firstly, a simulation robot used for DMS testing is constructed, then a steering engine is selected and arranged, and fine control over fatigue actions of the simulation robot is achieved through control of a microcontroller; a python language is used as a development basis, modular design is adopted, eye detection, head posture estimation, attention evaluation, parameter configuration, a camera interface and the like are included, and a detected DMS system is deployed; and finally, based on the national standard and the actual driving behavior, generating an efficient and high-coverage test case set by adopting a combined test theory, and executing through a control system, so that the problems of low efficiency, high cost, uncontrollable action, insufficient scene coverage, lack of a standardized evaluation system and the like of the existing DMS test method are solved.

The present invention relates to the technical field of chip verification, and in particular to a chip system-level verification system based on the Python language, including: a preset Python script set in a first verification module, and a design under test set in a second verification module; the preset Python script sets the first verification module as the master device party and the second verification module as the slave device party; the first verification module generates a main thread of the Python script, sends a command to start the verification platform simulation thread to the second verification module, starts the verification platform simulation thread, and establishes a connection between the main thread of the Python script and the verification platform simulation thread; the main thread of the Python script controls the first verification module to send a verification request command to the second verification module, and the verification platform simulation thread controls the second verification module to process the verification request command. The present invention simplifies the chip system-level verification process, improves the verification efficiency, and reduces the verification cost.

The application discloses a flood evolution analysis method based on instant compiling technology, mainly comprising the following steps: setting a hardware acceleration environment, specifying a calculation time and an iteration step length, loading a grid topological structure, an initial water level and an initial flow velocity, a grid roughness field and model boundary conditions, optimizing codes of a finite volume method and a HLLC shallow water equation solving algorithm based on instant compiling technology, and realizing parallel acceleration simulation of flood evolution. The method is written in Python language with strong interpretability to compile shallow water equation solving codes, and machine codes are generated by instant compiling technology during runtime to greatly improve code running efficiency. Different hardware platforms such as CPU, GPU or CUDA can be selected to perform parallel acceleration solving on the shallow water equation, simple and efficient simulation of flood evolution is realized, and the method has the advantages of strong code readability and high performance.

The present invention discloses a parametric modeling method of an Abaqus model of a bolted ECC-RC beam based on Python language, which belongs to the field of image recognition and data processing technology, and includes the following steps: creating a concrete beam model; assembling the concrete beam according to axisymmetry; digging holes and splitting; creating hinge supports and loading support steel plates; creating bolts and placing them in holes, creating a steel cage for the beam and a steel cage for the plate; creating an analysis step; setting a contact relationship; dividing the grid; and applying a load. In the above manner, compared with the traditional GUI modeling method, the present invention uses Python language to perform secondary development on Abaqus, realizes intelligent parametric modeling of finite element models, and only takes tens of seconds to complete the modeling process compared to the original modeling process of several hours, saving a lot of time for finite element modeling and greatly improving the efficiency of finite element modeling.

The invention discloses a method for optimizing event trigger test efficiency, which relates to the technical field of software testing, and comprises the following steps: S1, arranging an event library by using a notebook or Excel, collecting and storing information related to events, and carrying out classified management on the events; s2, according to a test requirement, setting a sending frequency and a data type of an event, writing a sending related code, and appointing a sending target server; and S3, after network communication is debugged to be normal, executing a sending program, sending event information to a platform, and checking the condition of a rule triggered after the data is sent at the platform side. According to the method for optimizing the event trigger test efficiency, the technology is used for simulating the operation behavior of a person, generating events, collecting the events into an event library, compiling an automatic script by using a Python language, and sending the events which are pre-stored in the event library in advance to a platform in batches, so that the effect generated by manual operation is achieved, manual operation is replaced, and the efficiency is improved by more than 20 times.

The application discloses a tooth surface modification optimization method of a two-stage reduction gear system based on an NSGA-II algorithm, and comprises the following steps: establishing a two-stage reducer gear system model according to an actual structure; determining multiple typical working conditions of the two-stage reducer gear system; determining a modification method; establishing a multi-objective optimization mathematical model with the minimum peak-peak value of load transmission error under multiple working conditions as an objective function; based on the NSGA-II algorithm, using a secondary development interface of professional gear analysis software, using a Python language to compile an optimization program, respectively optimizing tooth surface modification parameters of a first-stage reduction gear pair and a second-stage reduction gear pair, and obtaining respective Pareto optimal solution sets; selecting a tooth surface modification optimization scheme in the Pareto optimal solution set, and completing tooth surface modification optimization work. The application combines the NSGA-II multi-objective genetic algorithm with professional gear analysis software, and provides an optimization method with stronger optimization ability and more rapid and accurate optimization for tooth surface modification optimization work under multiple working conditions.

The application relates to a target detection algorithm test method and system based on a python language, and the method comprises the following steps: S1, a step of preparing test data, preparing a test picture needing target detection, and labeling the test picture to generate an xml file of corresponding coordinate points; S2, a step of uploading the test picture to an algorithm interface to obtain response data; S3, a step of reading xml labeling file data, drawing a green rectangular frame in the test picture by reading rectangular frame coordinate point data; writing the rectangular frame coordinate point data into a txt file and saving the txt file to a groundtruths expected result directory; S4, a step of saving test picture classification; S5, a step of counting test results; S6, a step of automatically sending an email, generating a test report after the test is completed, and the test report showing test case execution results; and automatically sending an email after the test is completed, wherein the email content comprises test description information and a test report, and the email is sent to a test person in charge.

The invention belongs to the technical field of computer software products, and discloses a system for converting a rule into a Python language, which comprises an API (Application Program Interface) module and a conversion component, the API module is used for receiving a rule input by a user and forwarding the rule to the forwarding component; and the conversion component receives the rule input by the user and forwarded by the API module, and realizes conversion of the rule into the Python language. Based on an Antlr4 tool, lexical and grammatical analysis programs needed by a language compiler can be automatically generated only by configuring lexical and grammatical rules, the programming workload of programmers is greatly reduced, meanwhile, in combination with working requirements, self-defined lexical and grammar can be added, the whole conversion method is strengthened, and the conversion efficiency is improved. And mutual conversion of codes of various common languages and custom languages is realized.

The application discloses an industrial system mechanism modeling simulation platform, a unified system modeling simulation code system is constructed based on a Python language, a system identification model function library is constructed based on AI technology, and a dynamic model of a complex industrial system or equipment is identified through industrial big data; a general system modeling simulation environment is provided, a linear dynamic system and an artificial intelligence nonlinear dynamic model are incorporated into a unified modeling simulation system, visual combination modeling and industrial system simulation are carried out, and identification modeling simulation integration is supported; and the application is used for industrial system identification modeling simulation technology required by industrial internet and industrial intelligent construction, and provides a reliable system model for design of a high-end controller.

The present invention provides a service calling method, device, and electronic device, relating to the field of artificial intelligence technology. The service calling method includes: receiving an inference service calling request encapsulated in a first programming language and sent by a microservice gateway; modifying the recipient address in the inference service calling request to an address corresponding to an inference service module encapsulated and generated in a second programming language; sending the modified inference service calling request to the inference service module; receiving an inference result of the modified inference service calling request sent by the inference service module; and sending the inference result to the microservice gateway. In an embodiment of the present invention, by using a proxy service module to proxy the inference service of the inference service module encapsulated and generated in a second programming language under a microservice architecture of a first programming language system, it is possible to implement an inference service corresponding to an inference model encapsulated in the Python language under a microservice architecture of a JAVA system.

The present invention discloses a parameterized modeling method for the in-bore motion of rifled guns. First, within the Python language framework, the idea of ​​fragmentation and splicing is employed, combined with the structural parameters of the rifled gun barrel, to calculate the three-dimensional spatial coordinates of each node. The nodes are then connected in numerical order to generate a finite element model file for the rifled gun barrel. Then, based on the body-of-revolution characteristics of the projectile belt, a three-dimensional geometric model of the projectile and belt is established and meshed in ABAQUS software. The generated finite element model file for the rifled gun barrel is imported, and materials are assigned to each component involved in the calculation of the in-bore motion of the gun. Assembly, analysis step creation, contact definition, load and constraint setting are sequentially performed to create a job. The execution log file is retrieved, the key operation step codes are retained, and parameters related to the projectile belt structure are replaced with variables. Finally, the RSG dialog box builder is used to create a GUI plug-in for generating the rifled gun barrel and projectile belt, respectively, and performing pre-processing. The present invention greatly improves the efficiency of modeling and pre-processing by enabling one-click generation of a rifled artillery barrel finite element model, one-click completion of parametric modeling of projectile belts, and one-click completion of finite element analysis pre-processing of the motion within the rifled artillery firing chamber, thus providing a fast and efficient method for finite element simulation analysis of the artillery firing process.

The invention provides a planar additive manufacturing and remanufacturing finite element modeling method based on a linear path, and aims to solve the problems that a traditional additive manufacturing finite element modeling method based on GUI operation is complex in process, low in efficiency, poor in reliability and inconvenient to digitize and intelligentize, and the model precision is insufficient when a preset weld bead is adopted to simulate an additive process. Parameterized analysis steps are written based on a Python language to create program codes, and the problem that GUI operation is extremely complicated under the large-model and high-precision requirements is solved; based on a Fortran language, DFLUX user subprogram secondary development and a unit life and death technology, the problem of precision deviation caused by neglecting of a welding bead gradual accumulation process in a preset welding bead is solved. By adopting the method, the modeling precision and reliability requirements can be guaranteed to the greatest extent while the finite element modeling efficiency in the additive manufacturing process is improved. Meanwhile, parameterized driving is adopted, the method is applied to additive manufacturing technology development and product full-life-cycle management, and automation and intelligentization are achieved conveniently.

The present invention discloses a Python-based LDO circuit design method, which is applied to a terminal and implemented by executing code, and includes the following steps: Step 10, determining an initial circuit structure module based on design indicators; Step 20, searching for optimal size information for each transistor in a transistor parameter database; Step 30, generating multiple test circuit modules; Step 40, calling a simulator to perform simulation testing to obtain pre-simulation results; Step 50, generating a circuit layout; Step 60, calling a simulator to perform simulation testing to obtain post-simulation results; Step 70, if the post-simulation results do not meet the design indicators, executing Step 30. The present invention codifies and automates the LDO design process, and the code can be run to complete the complete design process from circuit description, size generation, multi-index simulation, and automatic layout and routing of the layout, thereby shortening the LDO layout design cycle, improving design efficiency, and achieving full automation of the LDO circuit to layout design process.

The present application relates to the technical field of wind power steel-concrete structure parameterization calculation, in particular to a Python language-based parameterized automatic modeling method for a tower structure Abaqus finite element model, which solves the problems of low efficiency, easy errors and difficult parameter adjustment of traditional manual modeling. The method comprises the following steps: reading geometric, material, modeling and load information in an Excel parameter file; automatically generating a concrete component, a steel wire body and a steel reinforcement cage; completing prestressed anchor cable system modeling; assembling each component and automatically assigning material and cross-section properties; finally performing mesh division, contact definition, boundary condition application and load loading, and saving a complete finite element model. The present application significantly improves modeling efficiency and consistency through full-process parameter driving and modular script architecture, supports flexible customization of a whole tower structure, and provides a high-reliability pre-processing scheme for complex structure simulation.

The invention discloses a floating board-to-board connector signal terminal pin structure design optimization method, and belongs to the technical field of connectors. According to the method, transient dynamic finite element full-automatic parametric modeling of terminal and plastic shell assembly is achieved through a Python language, associated data of clamping thorn structure parameters and insertion and extraction force characteristics are obtained through orthogonal experimental design, and a multivariate nonlinear prediction model is established. A sub-target multiplication and division method is innovatively adopted to construct an insertion and extraction force comprehensive performance target function, a genetic algorithm is applied to carry out multi-target optimization, and finally an optimal parameter combination meeting working condition requirements is obtained. According to the invention, the intelligent design of the terminal bayonet structure is realized, the mechanical stability, the assembly reliability and the production economy of the connector are obviously improved, and an efficient solution is provided for the design of a high-precision connector.

The invention discloses a high-quality data set construction method and device. The method comprises the steps of obtaining a first data set of a vulnerability list; screening the first data set through a screening function to obtain a second data set of the vulnerability list; based on the second data set, determining a first target data set for constructing a high-quality data set; based on the first target data set, determining a second target data set for constructing a high-quality data set; and based on the second target data set, determining a high-quality data set in the target format. According to the method, the high-quality data set for the Python language is obtained through the high-quality data set construction method, so that powerful support is provided for fine adjustment of a large model, code vulnerability pre-detection and repair are performed on the Python language, and development efficiency and project quality are guaranteed.