1001 results about "Conversion method" patented technology

The present application relates to a waveform file conversion method and apparatus for chip testing, and a computer readable storage medium. The method comprises: performing content analysis on a waveform file to be converted, to acquire period information, pin information, and state information of pins in each period of said waveform file; generating a PAT-format digital signal waveform file; and on the basis of a mapping relationship between pins and ATE channels, converting the PAT-format digital signal waveform file into a binary vector file for ATE testing. A waveform file in any format is converted into a PAT-format digital signal waveform file which uses periods as rows and pins as columns, so that each row vector not only contains current time information but also contains a logical state of each pin; in addition, to facilitate understanding, the PAT-format digital signal waveform file is further converted into a binary vector for ATE testing, so that efficient processing of waveform files in a plurality of different formats can be realized, thereby improving the universality of conversion.

The invention provides an execution feedback and iterative optimization-based natural language to SQL (Structured Query Language) conversion method. The method comprises the following steps of: generating a plurality of candidate SQL statements; performing parallel execution on the candidate SQL statements, and capturing grammar error information in execution results and line number classification numbers of different candidate SQL return results; generating an error pattern abstract based on the grammar error information, converting the error pattern abstract into a natural language constraint instruction, and injecting the natural language constraint instruction into a prompt word generated in the next round; when the line number classification number is smaller than or equal to a preset threshold value, a self-consistency voting mechanism is adopted, a candidate SQL set with the largest number of returned same lines is selected, and the SQL with the shortest execution time is selected from the set to serve as final output. According to the method, the accuracy, robustness and execution efficiency of conversion from the natural language to the SQL can be remarkably improved by executing a feedback-driven multi-round iterative optimization mechanism.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.

The invention discloses an equipment agent processing program conversion method and device based on cooperation of a large model and a small model. An equipment agent is constructed by analyzing control systems, kinematics structures and process constraint information of a source machine tool and a target machine tool, and line-level instructions are abstracted into system-independent unified semantic representation based on a multi-system numerical control corpus. And performing grammar analysis and semantic mapping on the source program, and generating a conversion task in combination with grammar rules, kinematics accessibility and process security constraints of a target machine tool. A small model is finely adjusted through supervised learning to realize basic generation capability, a large model is introduced as a patch evaluator, candidate results are jointly scored from different dimensions, knowledge migration is realized based on improved near-end strategy optimization, and a small model generation strategy is continuously optimized. And finally, high-quality results are screened through confidence degree sorting, cross-system G / M instruction and multi-axis track generation and verification are completed, and high-precision automatic conversion of numerical control programs is achieved.

The invention belongs to the technical field of data processing, and provides a scale detection and scale conversion method and system based on an ultrasonic image, and the method comprises the steps: constructing a discrimination function fusing multi-modal features according to the spectral basis functions of different scales, and determining the scale contour of the scales in the ultrasonic image; the design can dynamically adapt to the spectral characteristics and structural characteristics of the scale, can effectively cope with the interference of scale diversity, ultrasonic speckle noise and the like of equipment of different manufacturers, and ensures the stability of scale positioning and subsequent conversion; the method comprises the following steps of: establishing a biomechanical-image deformation coupling model by taking a scale contour of a scale in an ultrasonic image as a geometric reference and combining biomechanical characteristics of an ultrasonic probe and tissues to obtain a corrected pixel distance between adjacent scale points of the scale; and curvature compensation for eliminating refraction errors of ultrasonic waves in curved tissues is introduced to correct a scale pixel-millimeter conversion coefficient, so that the pixel-millimeter conversion error rate is reduced to 1.2%, which is far lower than the level of a traditional method.

The invention provides a document adaptive conversion method and device based on a multi-modal large model and a medium, and belongs to the technical field of data processing. The method comprises the following steps: inputting a multi-modal document into a pre-constructed multi-modal large model, wherein the multi-modal large model comprises a multi-modal joint framework fusing a ViT visual model and an LLM model; using a ViT visual model and an LLM model to respectively extract multi-scale visual features corresponding to non-text content and semantic features corresponding to text content, and using a cross-modal attention mechanism to bidirectionally align the multi-scale visual features and the semantic features to obtain a multi-modal document; carrying out self-adaptive blocking on the multi-modal document by using a self-adaptive blocking strategy, and modeling a relative position relationship between blocks; establishing a dynamic mapping rule from a document element to an HTML tag, and mapping the multi-modal document into a front-end interaction component in combination with a natural language instruction of a user; and dynamically rendering the front-end interaction component in an on-demand mounting and resource isolation mode. The problem of insufficient document restoration capability in the prior art can be solved.

The invention relates to an unmanned end-to-end sensing method and system for an unstructured road scene, and belongs to the technical field of automatic driving. A point cloud supervision view angle conversion method is adopted, point cloud densification is realized by constructing a local subgraph, and multi-view angle depth estimation is optimized in combination with an optical flow consistency constraint; according to the method, long and short focus image features are fused through a cross attention mechanism, camera internal reference information is embedded, image BEV features are enhanced, and the long-distance perception capability is improved; according to the method, dynamic interference such as flying dust is effectively recognized and filtered through a space-time attention mechanism, a unified deep learning framework is adopted, the problems of information loss and error accumulation in a traditional modular framework are avoided, and unstructured scene perception is effectively improved. The problems of long-distance sensing, blocking of dust raising and the like, inaccurate space conversion and the like in an unstructured road scene are solved.

The invention provides an efficiency optimization control method of a permanent magnet synchronous motor and related equipment. The method comprises the following steps: acquiring an output current of an inverter; constructing an inverter loss model based on the output current of the inverter and the inverter loss coefficient; constructing a total loss model based on the electromagnetic loss model, the inverter loss model and an equivalent conversion method; constructing a current population by taking the plurality of d-axis current values as individuals; and performing optimization solution based on the total loss model, the current population, a PID search algorithm and an artemisinin optimization algorithm to obtain a global optimal solution and control the permanent magnet synchronous motor. According to the scheme, the driver loss is added into the total loss model, and efficiency optimization can be carried out on the whole motor driving system; in order to solve the problem that a traditional method is not suitable for an IPMSM, a PID search algorithm and an artemisinin optimization algorithm are combined, parameter search is carried out on a motor efficiency optimal point by simulating an incremental PID controller adjusting mechanism, a global optimal solution is obtained, and therefore optimal control over the click efficiency is achieved.

The invention provides a Text-to-SQL (Structured Query Language) conversion method based on semantic modeling, which relates to the technical field of natural language processing and data analysis, and comprises the following steps: designing a structured semantic model comprising a fact table and a dimension table by adopting a dimension modeling method, and defining business field division, data model definition and model dictionary construction; performing word segmentation processing on the natural language question input by the user, matching the natural language question with dimensions, indexes and dimension values in the structured semantic model based on the model dictionary library, and generating Schema information containing database types, table names, field definitions and filtering conditions; calling the large model, and converting the natural language problem into a preliminary semantic SQL in combination with Schema information and a preset rule; calling the large model again, performing logic verification and grammar optimization on the preliminary semantic SQL, and generating a corrected semantic SQL; based on Schema information and data model definition, the corrected semantic SQL is converted into an executable database SQL statement.

According to the model conversion method provided by the invention, the height map of the terrain and the weight distribution maps of the plurality of material layers are acquired, the target three-dimensional grid model is generated according to the height map, the weight distribution maps of the plurality of material layers are combined to generate the weight map, and based on the position of the target three-dimensional grid model in the world coordinate system, the target three-dimensional grid model is converted into the target three-dimensional grid model. Calculating texture mapping coordinates; and generating a material instance corresponding to the target three-dimensional grid model according to a preset material template, the weight map and the texture mapping coordinate. Through effective integration and conversion processing of terrain height information and multilayer material weight information, automatic conversion from two-dimensional terrain data to a three-dimensional grid model with complete material information is realized, and it is ensured that the finally generated three-dimensional model can accurately express a complex material mixing effect. And the visual authenticity and rendering efficiency of the terrain model are obviously improved.

The invention discloses a multi-protocol conversion method, system and device between fire alarm devices and a medium, and belongs to the technical field of communication. The method comprises the following steps: S1, loading a protocol template; s2, establishing communication with a source device; s3, analyzing and converting the original data; s4, writing the data into a database in a standardized manner; s5, standardized data mapping and frame construction; s6, establishing communication with the target; the system part comprises the following modules: a protocol configuration module used for storing and managing template information of all converted protocols; the data acquisition and analysis engine is used for establishing communication with source equipment; the data standardization and database module is used as a data transit hub of the system; the data mapping and packaging engine is used for preparing communication data for target equipment; the data sending engine is used for communicating with target equipment; the problems that in the prior art, protocols between new fire alarm equipment and old fire alarm equipment are not compatible, a flexible conversion mechanism is lacked, and the new fire alarm equipment and the old fire alarm equipment are difficult to uniformly access an Internet of Things platform are solved.

The invention relates to the technical field of spiking neural networks, in particular to a spiking neural network conversion method and system based on differentiable adaptive optimization, and the method comprises the steps: constructing and pre-training a target ANN model; in a pre-trained ANN model, performing simulation processing on the activation value of each layer by using a global differential value representation operator; on the basis of a loss function, an original weight parameter in the target ANN model and a trainable excitation threshold in a global differentiable value representation operator are placed in the same optimization framework for joint fine tuning, and an optimal weight and an optimal threshold learned by each layer are obtained; and an equivalent SNN model is constructed. According to the method, precise correspondence between continuous activation and pulse sequences can be optimized, the method is suitable for multiple pulse coding modes, quantization errors are reduced to the maximum extent, the deployment requirements of neuromorphic hardware and an edge intelligent computing platform can be deeply adapted, and an efficient and universal model conversion solution is provided for the neuromorphic computing industry.

The embodiment of the invention provides a DCS man-machine picture conversion method and system, electronic equipment and a medium, and the method comprises the steps: firstly, responding to a conversion request, locking a target DCS system and a required target picture conversion parameter, and then carrying out the locking of the target picture conversion parameter based on a preset general mapping relation and a unique preset extension mapping relation of the target system, and converting the parameters into an intermediate execution file compatible with the target system. Wherein the problem of cross-system common compatibility is solved through general mapping, and it is ensured that icon layout parameters do not fail due to system differences; extended mapping gives consideration to uniqueness of a target system, so that personalized functions of icons are accurately converted. The generated intermediate file can be directly driven to generate a corresponding picture after being imported into a target system, and a design process does not need to be repeated. Therefore, only one conversion request is needed, and the target system and the expansion mapping relation are adjusted, so that the same set of picture requirements can adapt to different systems, the phenomenon that pictures with the same function are redrawn for each system is avoided, and mutual conversion of man-machine pictures among different DCS systems is effectively realized.

The invention relates to the technical field of ground feature image processing, and discloses a ground feature automatic identification vector conversion method based on deep learning, which realizes pixel-level ground feature accurate classification based on a CBAM attention mechanism and multi-scale feature fusion, extracts a smooth boundary by improving Canny detection and polynomial fitting, and improves the accuracy of ground feature recognition. And combining Shapley and Geopandas libraries to realize automatic vector generation and topology processing, and finally completing attribute assignment through intelligent matching of an attribute feature database. Therefore, the ground object data processing period is shortened from several days to several hours, the data processing efficiency is remarkably improved, and rapid and reliable data support is provided for land planning, disaster emergency and the like. And meanwhile, through mixed loss function optimization, probability calibration and topological iteration optimization mechanisms, the ground feature recognition accuracy and the vector data geometric precision are remarkably improved, the data deviation is effectively reduced, and a more scientific and reasonable data guarantee is provided for land space development and ecological protection.

The invention relates to the technical field of research and development management, and discloses an SQL grammar conversion method and device based on a large model, equipment and a storage medium. The method comprises the steps that a semantic structure is determined according to an SQL statement and a large language model; according to the grammar specification and the semantic structure, generating at least one SQL statement to be determined; and performing grammar verification on each to-be-determined SQL statement through the grammar database to generate a verification result, and determining a target SQL statement according to the verification result. Through the above mode, the semantic structure of the original SQL is deeply analyzed through the large language model, the accuracy of conversion service logic is improved, multiple to-be-determined SQL statements are automatically generated through the preset grammar specification and the semantic structure, rapid verification is performed in combination with the preset grammar database, and the to-be-determined SQL statement passing verification is determined as the target SQL statement. The method can be applied to the business fields of financial science and technology, medical health, old-age care and the like, and the SQL grammar conversion reliability of a database management system is improved.

The invention discloses a method and system for converting a C / S architecture HMI configuration file into a B / S architecture JSON format, and the method comprises the steps: initializing a conversion service, constructing and loading a distributed configuration center file, so as to obtain a multi-dimensional configuration parameter; performing configuration file monitoring and increment identification, monitoring file change of a target path in real time, and performing legality verification on the file change; the primitive data in the configuration file are analyzed, and the analyzed primitive data are obtained; primitive attribute mapping conversion is conducted on the analyzed primitive data, a JSON file obtained through conversion is verified, and a verified JSON file is obtained; and respectively storing the verified JSON file to an intelligent operation and maintenance platform and a local disk. According to the invention, automation, standardization and high reliability of configuration conversion from C / S to B / S architecture are realized.

The invention relates to the technical field of instruction set conversion, and discloses an instruction set conversion method of a GPU heterogeneous environment based on simulation software. According to the method, the key instruction stream in the simulation task is accurately captured through the dynamic instrumentation method, and a solid foundation is provided for subsequent processing; the method comprises the following steps: dividing an original instruction stream into a plurality of instruction blocks to be converted through a classification mechanism according to instruction semantic features and hardware suitability, and creating conditions for parallel conversion; a lightweight front-end translator is adopted to efficiently convert classified instruction blocks into architecture-independent intermediate representations, the degree of parallelism and the data dependency relationship between instructions are reserved, the intermediate representation instructions are deeply optimized, and the method comprises the key steps of instruction selection, register allocation, instruction recombination, SIMT mapping, instruction coding and the like. And generating a native instruction of the target GPU architecture through accelerated translation of the target GPU thread block. According to the method, the instruction conversion period is shortened, and the correctness and the execution efficiency of the conversion result are improved.

The invention relates to a multi-resolution geological data conversion method and system based on ensemble learning, and belongs to the technical field of geological information processing, and the conversion method comprises the steps: obtaining a low-resolution geological data set containing a target element, and a high-resolution geological data set not containing the target element; performing spatial grid aggregation processing on the high-resolution geological data set to generate a predictive variable matrix which is spatially aligned with the low-resolution geological data set; training a Stacking integrated regression model by taking the predictive variable matrix as input and a target element value in the low-resolution geological data set as an output target; inputting the high-resolution geological data set into the fusion prediction model, and outputting a preliminary prediction value of a target element; and performing spatial error correction on the target element preliminary prediction value based on the target element actual value of the low-resolution geological data set to generate corrected high-resolution target element data. According to the invention, multi-scale and multi-source heterogeneous geological data can be effectively integrated.

The invention discloses a protocol conversion method, which comprises the steps of receiving an original data frame from a charging power supply, extracting a protocol feature vector of the original data frame through a pre-trained machine learning model, and matching a first protocol type of the original data frame from a protocol feature library based on the protocol feature vector; calling an analysis rule base corresponding to the first protocol type to deconstruct an original data frame into intermediate layer data irrelevant to the protocol; and calling a field conversion rule from a protocol mapping table associated with the second protocol type, dynamically recombining the standardized field label in the intermediate layer data into a target data frame structure, and injecting a protocol control character originating from a target protocol specification to generate a target data frame conforming to the second protocol type. The method has the capability of adaptively learning a new protocol, reduces the manual intervention and maintenance cost, and improves the compatibility, communication reliability and overall operation efficiency of a battery replacement system for vehicles of different brands.

The embodiment of the invention provides a program conversion method and a compiling device, and relates to the technical field of computers, in the method, an IR file of a GPU program is obtained to serve as a to-be-converted file, for a function parameter of any function in the to-be-converted file, the function parameter is converted into a function parameter matched with a CPU, and the converted parameter is obtained. And determining the IR statement having data dependence with the converted parameter as a statement to be adapted. And for any statement to be adapted, adjusting the statement to be adapted based on the parameter type of the converted parameter and the thread execution mask of the CPU. And compiling the adjusted to-be-converted file to obtain an executable file adaptive to the CPU. The GPU programs written by different high-level languages can be converted into the IR file, so that the conversion is carried out from the dimension of the IR file, and the support for different high-level languages can be realized. Therefore, the limitation is lower.

The invention discloses a low-altitude field multi-source heterogeneous data unified conversion method and system based on Beidou grid codes, and relates to the technical field of data processing.The method comprises the steps that a constructed multi-source heterogeneous data set is analyzed, and key information is extracted; on the basis of Beidou grid codes, coding precision is dynamically adjusted according to data types, and time-space stamps of the analyzed data are converted into unified Beidou grid time-space coordinates; establishing a mapping relationship between the multi-source heterogeneous data set and a preset uniform data format, and converting the aligned data into data conforming to the uniform data format according to the mapping relationship; and performing quality verification on the converted data conforming to the unified data format to obtain high-quality data conforming to the unified data format, and performing associated storage on the high-quality data and the corresponding Beidou grid codes. According to the method, a data barrier can be broken, efficient fusion and accurate expression of data under a unified framework are realized, and high-quality data support is provided for intelligent management and decision-making in the low-altitude field.

The invention provides a method and a system for accurately converting a large-model natural language into an SQL (Structured Query Language) based on multi-modal fusion. The method comprises the following steps: S1, performing named entity recognition and dependency syntax analysis on a natural language text, performing vision-text dual-path processing on image data, and constructing a bipartite graph structure for table data so as to obtain a vision text composite feature and a table feature; s2, designing a modal attention controller and calculating the weight of each modal through a gating network; s3, constructing a knowledge graph, clearly recording all legal table names and column names in a database, and retrieving the knowledge graph in real time when a decoder generates the table names or the column names; s4, generating an SQL draft; s5, performing bidirectional verification correction on the SQL statement; and S6, outputting the legal SQL statement. According to the method, multi-modal information can be integrated, information loss and semantic deviation are reduced, illegal statements are avoided through grammar and semantic dual verification, and the overall reliability of SQL statement generation is improved.

The invention relates to the technical field of SQL (Structured Query Language) grammar conversion, and provides a general SQL grammar conversion method, device and equipment and a medium, the method comprises the following steps: performing lexical analysis on a source end SQL statement to obtain a lexical unit set; based on the type features and semantic attributes of the lexical unit set, determining parser combiners, and constructing a specific parser by infinitely combining the parser combiners; the specific resolver is used for conducting grammar analysis on the lexical unit set, a general abstract syntax tree is constructed through a mapping assembly, and nodes of the general abstract syntax tree correspond to the specific resolver one to one; and traversing the general abstract syntax tree, and generating a target end SQL statement according to an SQL syntax rule of a target end database. Through the technical scheme, any complex source end SQL can be accurately converted into the target end SQL.

The invention discloses a header identification and conversion method based on a large model and RAG enhanced retrieval, and relates to the technical field of data processing and information intelligent management. According to the method, header recognition and format mapping are completed in an automatic mode, the workload of manual checking is greatly reduced, the overall processing efficiency is improved, meanwhile, intelligent adaptation of complex naming differences and unknown tables is achieved in combination with large models and rule knowledge, and the analysis risk caused by format inconsistency is remarkably reduced; the method can prompt in time when facing a low-confidence result, further improves the reliability and accuracy of data processing, has good expansion capability, and can perform continuous optimization and incremental learning along with service changes, thereby adapting to a continuously evolved header structure.

The invention relates to a multi-stage AI cooperation and closed-loop self-healing video-to-script conversion method, and relates to the digital content creation cross technical field, the method comprises the following steps: obtaining a resource identifier ordered set of a series of videos, the resource identifier ordered set comprises a plurality of identifiers arranged in order, and each identifier corresponds to a video resource; based on the resource identifier ordered set, a preset first AI service model and a preset second AI service model, a first version script set is generated, the first AI service model represents a model with multi-mode video understanding and script generation capabilities, and the second AI service model represents a model with logic induction and information integration capabilities; creating a script verification object for each script in the first version script set, and performing closed-loop intelligent proofreading on each script verification object to generate a target script set. Through multi-stage AI cooperation and closed-loop self-healing, the technical effect of accurate, efficient and logically coherent video-to-script conversion is realized.

The invention discloses a satellite-ground enhanced signal conversion method and a satellite-ground enhanced signal conversion model based on PPP-RTK, which are used for solving the problems that conventional RTK in a ground-free network area fails, and satellite-based PPP-RTK depends on a special terminal and is slow in convergence. Generating an RTCM-OSR correction number by analyzing the SSR correction number, fusing the orbital clock correction, ionosphere and troposphere correction and performing weighted interpolation; the ambiguity is solved in combination with the observation value of the base station, dual-path verification and weighted fusion are carried out in combination with M-W and LAMBDA algorithms, and the wide lane ambiguity fixing reliability is improved; and when the ground-based signal is interrupted for more than 1 second, inheriting and maintaining Kalman filtering continuity by using the state vector and generating a switching identifier. The model integrates a signal processing module, a correction generation module, an ambiguity verification module and a switching control module, so that a conventional RTK terminal obtains centimeter-level positioning (level 2.3 cm and elevation 4.1 cm) without a public network, the interruption recovery is less than or equal to 1.5 seconds, and the scene upgrading cost of power inspection, power transmission monitoring, wind power plant monitoring and the like is reduced.

The invention provides an SSE-based HTTP to MCP protocol conversion method, and relates to the technical field of network communication and artificial intelligence engineering integration, and the method comprises the steps: receiving a request sent by a client through an SSE protocol, and extracting key parameters in the request; according to a preset rule mapping configuration, converting the key parameter into a parameter conforming to an original HTTP service interface specification, and constructing an HTTP request; sending the HTTP request to a target HTTP service, and obtaining HTTP response data; analyzing the HTTP response data, and mapping and packaging the HTTP response data according to the upper and lower field specifications of the MCP protocol; and segmenting the packaged MCP protocol data according to an SSE protocol format to generate a streaming response, and pushing the streaming response to a client in real time.

The invention discloses a digital regularization and graph conversion method for large model training, which belongs to the technical field of image processing, and comprises the following steps: acquiring a target document, carrying out preprocessing and layout analysis on the target document, and extracting title text block information, body text block information and document picture data; based on multi-scale density analysis, determining a column layout of each page in the target document and a column to which each text block belongs, and establishing a chapter structure; judging the chapter attribution of the text block through two-stage decision, and judging the reference attribution of the document picture based on the reference relationship and the document picture data; reconstructing a document structure, and sequentially generating graphic structured data; and constructing a multi-modal knowledge graph. According to the method, the logic structure of the complex typesetting document is identified and reconstructed, the structured data is extracted, the data utilization rate and the information depth of the complex document are improved, and a high-quality training basis is provided for a large model.

The invention provides an edge computing and protocol unified conversion method based on an Internet of Things intelligent gateway. The method comprises the following steps: collecting data of the Internet of Things intelligent gateway; inputting the extracted protocol features into the constructed protocol recognition model for recognition; the network state and load of the Internet of Things gateway are collected in real time, a conversion strategy is dynamically adjusted based on a load balancing mechanism, equipment data characteristics are predicted through an algorithm, and resources are allocated in combination with the priority level of a conversion task; performing protocol analysis on the collected data based on the identified protocol type, extracting key fields in the original data, mapping the analyzed data into a standardized format by constructing a unified data model, and packaging the standardized data into a target protocol format according to downlink requirements for outputting; and performing encryption uplink storage on the data based on the block chain in combination with an encryption technology. A conversion strategy can be dynamically adjusted, and resource allocation is optimized; the conversion from a multi-source protocol to a standardized format is realized, and multiple target protocols can be packaged as required.

The invention relates to an NL2SQL conversion method oriented to a large language model, and belongs to the technical field of database query. According to the method, complex queries are analyzed into sequentially executed sub-task chains through a task decomposition module, an example optimization prompt module dynamically selects guide examples based on semantic matching, a user feedback self-correction module carries out double confidence evaluation and interactive correction, and an end-to-end SQL generation optimization system is formed. According to the method, the technical threshold problem of operating the database by a non-professional user is effectively solved, the SQL generation accuracy, the system adaptability and the interaction safety are remarkably improved, and high-reliability conversion from a natural language to database query is realized.