7 results about "Type conversion" patented technology

The application relates to the technical field of deep learning compilation, and provides a method, device, equipment and medium for converting an MLIR file into a PyTorch file. The method comprises the following steps: obtaining an MLIR file, extracting data information and operation information in the MLIR file; retaining original identifiers of the data information and converting data types of the data information into Tensor data types in a PyTorch file; constructing a dependency relationship between operations according to a front-back order relationship between the operations; converting operation types in the MLIR file into operation types in the PyTorch file according to a conversion relationship between the PyTorch file and the MLIR file; and forming the PyTorch file according to the converted data types, the dependency relationship between the operations and the converted operation types. The method can equivalently convert the MLIR file into the corresponding PyTorch file.

This application discloses an automatic code generation method and system for SOME / IP data types, relating to the field of vehicle system development. The method includes: S1, data dictionary reading: obtaining the data dictionary for SOME / IP protocol communication; S2, data type classification, identification, and processing: opening the Excel file from step S1, classifying each row of data in the worksheet, and performing specific parsing processing for different types; S3, multi-round iterative dependency resolution: using a multi-round scanning strategy to resolve nested dependencies between various data types; S4, naming standardization processing: standardizing the names of each parsed data type according to preset naming rules; S5, standard code generation and output: converting all data types that have undergone naming standardization processing into type definition code conforming to C language specifications; simultaneously converting the value range, precision, unit, and Chinese description metadata in the data dictionary of the Excel file into code comments, generating a C language header file that can be included in the compilation project.

This invention discloses a method for implementing a multi-source heterogeneous data transformation pipeline for time-series databases, comprising: parsing, transforming, and modeling the original data according to configuration rules to obtain a streaming transformation pipeline including a parsing stage, a transformation stage, and a modeling stage connected in sequence; in the parsing stage, parsing various data formats of the original data and converting the fields extracted after parsing into data in a unified columnar storage format; in the transformation stage, filtering, mapping, calculating, and type conversion operations are performed on the data processed in the parsing stage; in the modeling stage, the data processed in the transformation stage is mapped to the super table and sub-table structure of the time-series database according to the configuration rules; the modeling process includes: dynamic table name generation based on template expressions, extraction of label fields and data fields, and batch data assembly; the configuration rules specifically include loading, validating, and hot-updating the configuration file of the streaming transformation pipeline.

The application discloses a model conversion method based on Flowable workflow nested JSON, and relates to the technical field of computer software; JSON structure analysis is carried out: the hierarchical structure of the nested JSON is recursively traversed, the paths of all fields are extracted, the JSON fields are corresponded with the attributes of the target data model based on predefined field mapping rules, Flowable node configuration is carried out: the JSON conversion node is embedded in the Flowable process, which is used for carrying out the field mapping rule configuration and performing the conditional judgment of the dynamic script for the data type conversion, the nested JSON is converted into the target data model through the JSON conversion node according to the mapping rule and the script logic, and the conversion result is transmitted to the subsequent node through the Flowable process variable or is persisted to the database.

The application discloses a technology consultation entity portrait online analysis processing analysis and device, the method comprises the following steps: obtaining an entity attribute label based on a foreground attribute label and a background attribute label; wherein the background attribute label is obtained based on processing of entity original data in a technology consultation scene; corresponding data label conversion is performed according to the entity attribute label and a preset data type conversion relationship table, the converted data label is stored to obtain a data storage result; the preset user rule is converted to JSON format data based on the data storage result, the JSON format data is parsed to generate a corresponding SQL statement query result; data rendering is performed based on the SQL statement query result, and entity portrait analysis in the technology consultation scene is completed according to the rendering result. The application eliminates the differences between technology consultation entity attributes, realizes flexible data query and millisecond-level response, and promotes the realization of industrial digitization and data value.

This application relates to the field of data processing technology, specifically to a data processing method, device, medium, and program product. The method includes: acquiring input data of a model, and based on the input data, acquiring first input data of the i-th computational layer among the M computational layers of the model, wherein the data type of the first input data is a first data type, and i is an integer from 1 to M; when the first data type does not satisfy N preset data types, determining a target data type from the N preset data types based on the matching degree between the first data type and the N preset data types, wherein the N preset data types are the data types of input data supported by the operators in the i-th computational layer, and N is a positive integer; converting the data type of the first input data to the target data type to obtain second input data; and inputting the second input data into the i-th computational layer to obtain the output result of the i-th computational layer. In this way, the model can support input of more types of initial data.

The application discloses a vehicle network simulation test environment construction method and system based on protocol definition driving, relates to the field of automobile electronic test technology, and loads new and old protocol definition files to extract physical semantic tuples containing signal physical meaning and data type conversion functions to generate a normalized intermediate representation layer, constructs an abstract syntax dependency graph, performs semantic level difference calculation, marks semantic change source nodes and layout change source nodes in the abstract syntax dependency graph, calculates an influence radius through a graph traversal algorithm according to the out-degree and in-degree of the change source nodes, identifies affected function clusters affected, extracts test cases covering the changed signals from a historical test sequence library based on the affected function cluster identification to generate a minimum disturbance regression test set, and outputs an incremental configuration description file used for indicating a virtual electronic control unit identification list that needs to be locally reloaded in a simulation environment, so that intelligent pruning of regression test cases and local reloading of a simulation model are realized.