506results about How to "Realize automatic generation" patented technology

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention relates to the technical field of computer processing, in particular to a software architecture code generation method and system based on a large language model, and provides the following scheme: obtaining demand information input by a user, analyzing demands by using a natural language processing technology, and extracting functional elements; performing classification processing on the functional elements through a clustering algorithm to generate a functional module group; mapping the function module group to a system architecture layer, and generating an architecture diagram represented by UML (Unified Modeling Language); and generating a corresponding source code according to the UML graph, and optimizing the source code. The optimization strategy comprises static analysis of format problems and potential vulnerabilities in identification codes and performance optimization of complex functions. Through combination of the large language model and the automatic code generation and optimization technology, the software development efficiency is improved, manual intervention is reduced, and the correctness and maintainability of the generated code are ensured.

The invention discloses an AIGC multi-mode audio-visual content creation method and system, and belongs to the technical field of multi-mode audio-visual content generation and automatic creation, the AIGC multi-mode audio-visual content creation method comprises the following steps: an input module for inputting text description and materials required by creation by a user; the AIGC generation module is used for converting the text description and the material input by the user into a video sequence and a music clip; the multi-modal fusion and alignment module fuses and aligns the generated video sequence and the music clip to obtain an audio / video; the video intelligent editing module is used for splicing the segments of the audios and the videos and keeping visual coherence and smooth connection between the two audios and the videos; and the output module is used for outputting the generated audio and video as the media format required by the user. The method solves the problems that in the prior art, multi-modal audio-visual content creation lacks the ability of cross-modal cooperative work, the content generated during multi-modal information fusion and alignment is inconsistent in rhythm, semantics and emotion, the support of a later editing process is lacked, the content connection is not natural enough, and the creation speed is low.

The invention discloses an online reinforcement learning data increasing and expanding method based on a diffusion model. The method comprises the following steps: processing original trajectory data to generate enhanced feature data; extracting global structure representation and local detail representation; carrying out fusion by adopting a hierarchical integration method to obtain unified hierarchical representation data; generating constraint coding data based on pre-stored physical constraint parameters and task constraint parameters, and fusing to generate constraint perception representation data; generating an evaluation result based on the current strategy parameter; performing adaptive adjustment on the constraint perception representation data to obtain adjusted representation data; modal analysis is carried out, diversified trajectory data is generated, and a candidate trajectory set is obtained; and carrying out quality evaluation, carrying out trajectory screening through a multi-objective optimization method, and updating strategy network parameters to obtain updated strategy parameters. According to the invention, high-quality training data can be automatically generated, and the training efficiency and performance of a reinforcement learning system are improved.

The invention discloses a combinatorial optimization automatic modeling-oriented training data generation method and system, and the method comprises the steps: receiving a problem description text, extracting key information through semantic analysis and knowledge reasoning, and constructing a structured optimization problem representation; reading the structured representation, extracting a target function component from the binary label matrix, extracting a constraint function component from the constraint relation adjacent matrix, extracting a variable definition component from the variable dependency matrix, and integrating the components to form a model component set; a value interval parameter is generated for the variable, a coefficient parameter is generated for the constraint matrix, a weight parameter is generated for the target function, and a parameterized model instance is obtained; and applying corresponding templates to scene elements, variables and constraint descriptions to generate a problem description text. Through the systematic information extraction and parameter generation method, automatic generation of high-quality training data is realized, and the diversity and practicability of the data are improved.

The invention relates to the technical field of artificial intelligence, and provides an enhanced RAG-based automatic test case generation method, which comprises the following steps of: extracting characters from a system document, segmenting the extracted characters into text fragments, forming text vectors, and storing the text vectors into a vector database; a local first large language model generates a corresponding triple, and the triple is stored in a knowledge graph database; directly delivering the system document to a local first large language model, extracting an article outline, and storing the article outline into a relational database; according to a test case demand input by the user, obtaining a text fragment, a triad and an article outline related to test case generation, and sending the text fragment, the triad and the article outline to a local second large language model; and the second large language model automatically generates a specific test case and a detailed test case document according to the information. According to the method, the test case book can be automatically generated, so that the working content is reduced, and the working efficiency is improved.

The invention belongs to the technical field of aviation equipment system modeling and analysis, and particularly relates to an aviation equipment system reliability analysis method and system based on MBSE model conversion, and the method comprises the steps: S1, building the function model reliability of an aviation equipment system through an MBSE model mapping conversion technology; s2, establishing a fault logic model of the aeronautical equipment system, and completing key fault identification and analysis evaluation of the aeronautical equipment system; s3, determining reliability index parameters of the aeronautical equipment system according to key fault identification and analysis results of the aeronautical equipment system; and S4, determining an aviation equipment system reliability analysis result according to the aviation equipment system reliability index parameters, and completing reliability analysis. According to the method, the model conversion method is adopted to analyze the reliability related indexes of the aeronautical equipment system, the situation that reliability analysis results of the same system are greatly differentiated due to manual intervention and do not conform to the actual system state can be avoided, and comprehensiveness and accuracy of fault logic relation analysis of the aeronautical equipment system are guaranteed.

The invention relates to an accelerated high-quality MRI (Magnetic Resonance Imaging) image reconstruction method based on a TC-KANReccon model. The method comprises the following steps: S1, collecting and preprocessing image data; s2, an accelerated high-quality MRI image reconstruction model based on a TC-KANReccon model is built; and S3, designing a noise cutting strategy of linear reduction of the cutting threshold. In each step of the sampling process, a linear decreasing cutting strategy is adopted according to the current progress of the sampling step. The strategy is applied to noise estimation generated by an MF-UKAN diffusion trunk so as to generate an MRI image with a better visual effect; s4, repeatedly training a diffusion model and a VAE module by taking the preprocessed MRI image and the full-sampling k-space data as input, optimizing network parameters, and continuously performing iterative optimization; and S5, inputting the under-sampled k-space data into the trained accelerated high-quality MRI image reconstruction model based on the TC-KANReccon model, and applying a matched noise cutting strategy of linear reduction of a cutting threshold to finally generate a high-quality MRI image.

The invention relates to the technical field of territorial space planning, in particular to a territorial space planning index calculation method and system based on a large language model. Deriving and outputting the name of the calculation tool and the actual value of the input parameter of the calculation tool through the large model, and generating a corresponding space calculation instruction; executing the space calculation instruction to obtain a corresponding index calculation result; and inputting the index calculation result and the corresponding first task cue word into the large model, and interpreting through the large model to obtain a corresponding territorial space planning result. By implementing the method, the accuracy, timeliness and reliability of land space planning index calculation can be improved.

The invention relates to the field of radiology department medicine and computers, and discloses a radiology report automatic generation method and system based on radiology priori knowledge, which comprises the following steps: constructing a radiology priori knowledge performing document dicing on the priori knowledge of a radiology report through a sliding window so as to construct the radiology priori knowledge base; data set reconstruction and fine tuning: constructing text data of a radiation report, and performing instruction fine tuning on the text data of the radiation report by using a large language model; construction of a comprehensive retrieval vectorization module: constructing the comprehensive retrieval vectorization module in combination with semantic similarity retrieval and full-text retrieval technologies; and automatically generating a radiation report by combining the matched radiation priori knowledge after passing through the comprehensive retrieval vectorization module. According to the system, the radiation prior knowledge base, the comprehensive retrieval vectorization module and the report generation module are integrated in advance, so that a doctor can generate a high-quality radiation image report according with a standard format only by inputting radiation diagnosis information.

The invention relates to the technical field of road engineering, in particular to a large language model-driven road change design method, which comprises the following steps of: 1) performing rough selection by using a large language model and a prompt project, and selecting a corridor zone; and 2) performing optimization through an improved artificial potential field MAPE algorithm. According to the method provided by the invention, by applying a natural language processing technology, automatic generation of a preliminary corridor belt design scheme is realized, the manpower and time cost of a route pre-selection stage is saved, a large number of alternative schemes can be produced to assist design, and the optimization effect is also higher than that of a relatively mature genetic algorithm; the method is a successful exploration of a large language model in intelligent road line selection application.

The invention discloses a navigation vehicle path planning method based on real-time traceability, and relates to the technical field of environment monitoring, and the method specifically comprises the following steps: collecting VOCs hotspot grids, industrial park limit values and limits, and historical navigation monitoring data; calculating a VOCs concentration high value point of the control area; generating a VOCs concentration space thermodynamic diagram and displaying the VOCs concentration space thermodynamic diagram on a GIS map; a key VOCs management and control area is visually marked with a GIS map as the background; establishing medium-scale and small-scale air pollution transmission simulation in combination with continuously updated precise regional pollution source lists; the pollution source and the contribution ratio are analyzed in real time, and the navigation path is planned according to the pollution source and the contribution ratio. According to the invention, through analysis of monitoring station data, pollution concentration hot spots and key management and control areas are identified and monitored, pollution sources are further searched based on a traceability model, and the problem of lack of planning of a navigation area is reduced; by designing the underway path planning and optimization algorithm, the underway route is automatically generated in the background, and the dependence of underway command on experience is reduced.

The invention provides an automatic generation method for safety management and control measures of secondary equipment of an intelligent substation, and belongs to the technical field of operation and inspection of intelligent substations. Comprising the following steps: constructing a safety measure target model according to an intelligent substation SCD file; perfecting a secondary safety measure operation object in the safety measure target model; adding a safety measure rule for the safety measure target model; according to secondary equipment to be overhauled, secondary equipment safety measures are generated by using the safety measure object model; and automatically generating a secondary safety measure management and control diagram and a secondary safety measure ticket. According to the invention, digital modeling is carried out on the primary and secondary equipment of the intelligent substation and the association relationship thereof, so that automatic functions of automatic generation of secondary safety measures, automatic drawing of a secondary safety measure management and control diagram, automatic issuing of a secondary safety measure ticket and the like are realized.

The invention belongs to the technical field of automobile structure design, and particularly relates to a large-model-based automobile body structure generative design method and system, and the method comprises the steps: building a multi-source database containing automobile body structure pictures, design variables and mechanical properties; training a vehicle body structure performance evaluation model and generating an evaluation model according to the database; on the basis of the generated evaluation model, the general text graph large model is finely adjusted, and a vehicle body structure generative design large model is obtained; generating a structure picture by using the obtained large model, and establishing a structure size evaluation model in combination with a database; inputting design requirements and constraint conditions, generating a vehicle body structure, evaluating the performance and size of the vehicle body structure, and outputting a final result. According to the method, the problems of low efficiency and strong dependence on experience of engineers in an existing automobile concept design stage are solved, the efficiency and creativity of automobile body structure design are improved, the design period is shortened, manual intervention is reduced, and automation and intelligence of automobile body structure design are promoted.

The invention relates to the technical field of operation and maintenance control, in particular to an operation and maintenance control method and system based on an automation strategy, and the method comprises the following steps: based on server state monitoring data obtained in real time, recording the current energy consumption and load data of a data center, recognizing the stable operation state and potential faults of a server, and determining the state of the server; and performing preventive maintenance on the equipment identified as the fault risk, and generating an equipment maintenance list. According to the invention, by automatically collecting the state data of the server and carrying out real-time performance evaluation and fault prediction on operation and maintenance management, early identification and preventive maintenance of fault risks and automatic generation and adjustment of a resource configuration table are realized, the data processing capability is directly influenced, continuous operation of key applications is ensured, energy consumption is optimized, and the service life of the server is prolonged. Automatic load balancing not only optimizes resource allocation, but also reduces errors and delay caused by manual intervention, and improves operation accuracy and response speed.

The invention discloses a signal timing scheme automatic generation system based on multiple algorithms, and the system comprises an index data statistics module which is used for carrying out the statistics of a plurality of preset index data in obtained traffic big data in any traffic region which is delimited in advance, and obtaining a statistical result of the index data; the index algorithm model management module is used for performing result representation on any index in the statistical result by using a corresponding specific index algorithm model; the optimization model generation module is used for constructing a multi-algorithm signal timing optimization model for a plurality of index algorithm models corresponding to the plurality of indexes; and the problem solving module is used for solving the optimization model and generating a signal timing scheme of the target traffic area according to a solving result. According to the invention, through integration of the multi-algorithm model, automatic generation of the traffic signal timing scheme is realized, the efficiency and effect of traffic management are improved, and powerful technical support is provided for sustainable development of urban traffic.

The invention provides an intelligent bidding and tendering file generation system and method, and belongs to the technical field of information processing, and the system comprises a demand obtaining module which obtains user input data and carries out the analysis, and further determines the demands of a user; the candidate determination module is used for determining an initial attribute value of the bidding project based on the demand of the user, and determining a bidding file candidate template in a preset bidding file template library based on the initial attribute value; the template screening module is used for analyzing the bidding and tendering file candidate templates based on a plurality of preset index types so as to determine a plurality of first templates; the combination determining module is used for determining an optimal template combination based on all the first templates and further generating a dynamic template file; and the file generation module is used for performing split analysis on the data input by the user and the dynamic template file, generating bidding and tendering file configuration information based on a split analysis result, and further generating an optimized and integrated bidding and tendering file in combination with the dynamic template file. And the efficiency of the bidding and tendering process and the accuracy of the file are improved.

The invention provides a method for generating a self-supervised learning contract template based on a trade system platform, and aims to automatically generate and continuously optimize a personalized contract template by using a self-supervised learning technology and a Transform model. The method comprises the following steps: extracting unlabeled data through a historical contract data input module; utilizing a self-supervised learning module to identify the relevance of contract terms; modeling a long-term and short-term dependency relationship of the contract text through a multi-head self-attention mechanism of a Transform model; and generating a high-dimensional semantic embedding representation of the contract terms, and automatically generating a personalized contract template based on the high-dimensional semantic embedding representation. The system continuously optimizes a generation rule through historical data and real-time service feedback, and dynamically adjusts the priority and content sorting of contract terms. According to the method, the contract generation efficiency and accuracy are improved, the dependence on manual labeling is reduced, the method is suitable for the iron and steel industry and other large-scale contract management scenes, the contract template can be flexibly adjusted according to market changes and business requirements, and the individuation and adaptability of the contract are remarkably enhanced.

The invention relates to the technical field of emergency communication, and provides an emergency flight plan generation method and device, equipment, a medium and a program product, and the method comprises the steps: predicting an emergency event in an emergency planning region, and obtaining the event description information of the emergency event; generating a multi-dimensional label of the emergency event according to the event description information; and inputting the multi-dimensional label and the event description information into a plan generation model, and generating an emergency flight plan of a low airspace in the emergency planning area by using the plan generation model. The plan generation model is obtained through iterative training based on a sample data set, and the sample data set is constructed based on historical emergency events in the emergency planning area and historical flight plans corresponding to the historical emergency events. The emergency flight plan can be generated online in real time through the plan generation model according to the prediction result of the emergency event, the efficiency and accuracy of generating the emergency flight plan are improved, and the requirement for frequent application of the low-airspace emergency flight plan can be met.

The invention discloses an automatic report generation method and device based on a digital certificate, equipment and a medium. The method comprises the following steps: firstly, acquiring original text information in a digital certificate image uploaded by a user; analyzing a service request instruction sent by a user, and selecting a to-be-filled spreadsheet template and a to-be-issued report template which are matched with a current service request; capturing target text information from the original text information, replacing the captured target text information into the to-be-filled spreadsheet template, and generating a spreadsheet file containing the target text information; and automatically replacing each piece of target text information in the spreadsheet file into a specified position of the to-be-issued report template, and generating a final issued report. By implementing the method and the device, the key information in the digital certificate can be automatically filled into the selected spreadsheet template and the report template, so that the purpose of automatically generating the report is realized, and the digital certificate processing efficiency of each department in an enterprise is effectively improved.

The embodiment of the invention provides a function test method and device for a random access memory and a storage medium, and belongs to the technical field of memories. The method comprises the following steps: configuring a test function instruction, an input parameter item and an output verification item corresponding to the test function instruction, and a parameter combination mode corresponding to the input parameter item in a preset test view; determining a plurality of target input data of the test function instruction according to the input parameter item and the parameter combination mode; calling a test template of the test function instruction, and generating a plurality of test cases according to the test template and the plurality of target input data; generating a verification script according to the output verification item; issuing a test instruction to a target random access memory according to the test case; and querying a test result from the target random access memory according to the verification script and outputting the test result. The embodiment of the invention can improve the test efficiency.

The invention relates to a user portrait generation method and system of a cloud enterprise micro, and relates to the technical field of portrait generation, and the method comprises the steps: obtaining user information and real-time environment information; determining scene change characteristics based on the real-time environment information and the historical environment information; when the situation change feature is greater than a change threshold in response, obtaining a portrait generation instruction; and determining a user portrait based on the user information and the real-time environment information according to the portrait generation instruction. According to the user portrait generation method, the user portrait can be automatically updated according to the change of the environment information, the user portrait generation process reduces the requirement of manual intervention, the maintenance cost is reduced, meanwhile, the conversion speed from data collection to actual application is increased, and the generated user portrait better conforms to the current condition of the user.

The invention discloses a home control method and a home control scene learning method and system. The method comprises the following steps: acquiring multi-modal data; and analyzing the multi-modal data by using a large language model, and generating a home control scene according to an analysis result. By means of the mode, automatic generation of the personalized scene is achieved, a user does not need to manually configure complex rules, the system can automatically learn and execute appropriate control strategies, user operation is simplified, and user experience is improved.

The invention discloses a vector data two-path space intersection method, device and system, and belongs to the technical field of geographic computation.The method comprises the steps that index blocks of different scales and an elastic distributed data set with index block identifiers as keys and a set of all vector data intersected with the index blocks as values are generated on the basis of a to-be-processed vector data set; inputting the elastic distributed data set into a trained index block calculation intensity prediction model to perform index block calculation intensity prediction, and obtaining the calculation intensity of each index block; based on the calculation intensity of each index block, mapping each index block to each task processing partition; performing two-path space intersection on vector data in the index block by utilizing each task processing partition; the index block calculation intensity prediction model is generated and trained by using a SparkML machine learning library based on a pre-acquired vector data set. According to the method, the two-path space intersection efficiency of the vector data can be improved, and meanwhile load balance in the parallel process is guaranteed.

The invention discloses a point cloud generation method and system for deep learning semantic segmentation of a building prefabricated component. The method mainly comprises the steps of creating a structured text of a building prefabricated part generation rule, automatically generating a standardized building prefabricated part model based on a Revit secondary development environment, and generating point cloud data by using a graphics method. According to the method, the document is divided into three parts, namely the prefabricated part category, the corresponding size and the geometric feature, so that the structured building prefabricated part text is obtained, the readability of the building specification is improved, a reader can understand the text conveniently, the building prefabricated part model meeting the requirements of the building specification is generated in an automatic mode, and the working efficiency is greatly improved. The method can efficiently realize automatic and standardized generation of the point cloud data of the building prefabricated part model, and serves for size and quality detection of the building prefabricated part.

The invention discloses a high-flux molecular modeling method of an asphalt binder. The method comprises the following steps: creating an asphalt binder component molecular library; according to given modeling requirements, component molecules of the asphalt binder component molecule library are randomly extracted, and an asphalt binder model is generated through modeling; on the basis of topological information of an asphalt binder model, according to a molecular simulation CVFF empirical force field, determining bonding categories of component atoms of the asphalt binder so as to determine atomic simulation force field parameters; and finally, outputting a data file of the asphalt binder model according to a set format requirement. According to the method, the initial model construction of the asphalt binder model and the automatic generation of the atomic scale simulation file are realized, the workload of manual modeling is reduced, the modeling requirement can be automatically processed, the asphalt binder molecular model can be automatically generated, and the model creation speed is improved.

The present application discloses a method for generating a virtual image and related equipment thereof, the method comprising: after acquiring voice data to be processed, firstly extracting voice features of the voice data to be processed to obtain voice features to be used, so that the voice features to be used can represent the voice information carried by the voice data to be processed; then determining virtual image display data corresponding to the voice data to be processed according to the voice features to be used, facial parameters to be used, and a pre-constructed virtual image generation model, so that the virtual image display data can represent a virtual image that matches the voice data to be processed, so that the virtual image can display the facial state presented by the character when the voice data to be processed is emitted, thereby achieving the purpose of automatically generating a virtual image.

The invention discloses an image generation method and device, a text graph network architecture and electronic equipment, and belongs to the technical field of artificial intelligence. The text graph network architecture comprises a text image feature extraction module used for generating a text image feature vector based on text graph requirement information; the text coding module is used for converting the text map requirement information into a text description feature vector; the image coding module is used for generating an image feature matrix based on the text map requirement information; the noise prediction module is used for obtaining a predicted image feature matrix based on the character image feature vector, the text description feature vector and the image feature matrix; and the image decoding module is used for generating an image matched with the text map requirement information based on the predicted image feature matrix.

The invention provides a map generation method, device and equipment, and the method comprises the steps: obtaining a fine feasible region corresponding to a robot, the fine feasible region being a region where the robot can reach; the fine feasible region comprises a plurality of grid points, for each grid point, if the grid point belongs to an obstacle region, it is indicated that the robot collides with an obstacle when the robot is located at the grid point, and if the grid point belongs to a free region, it is indicated that the robot does not collide with the obstacle when the robot is located at the grid point; generating a distance map based on the fine feasible region, wherein the distance map comprises a plurality of grid positions; for each grid position, the value of the grid position represents the distance between the grid position and the nearest obstacle; a basic road network map is generated based on the distance map, the basic road network map comprises a ridge line extracted based on the distance map, the ridge line comprises a plurality of topological nodes, and the distances from the topological nodes to surrounding obstacles are the same; and generating a target topological map based on the basic road network map. According to the scheme, the path planning task deployment efficiency can be improved.

An intelligent grinding wheel laser finishing system belongs to the field of grinding wheel finishing and comprises an input end, a machining system and an output end. The input end inputs grinding wheel data into the machining system, and the machining system is composed of a database, a CAM system and a monitoring system. The output end obtains products generated by the machining system. The method for trimming the grinding wheel through the laser comprises the steps that 1, grinding wheel data are input into the system; 2, a monitoring system automatically generates tool setting point and allowance information; 3, optimizing machining process parameters; 4, calculating a laser beam attitude adjustment mode and a laser energy compensation mode, and generating a trajectory plan; 5, performing interpolation motion, and trimming the grinding wheel; 6, the machining process is monitored, and allowance changes are observed; 7, after the allowance is met, the monitoring system sends out shutdown detection information; according to the method, laser beam posture adjustment and energy compensation can be carried out according to the outline dimension of the grinding wheel blank in the drawing design and finishing process, and autonomous iteration of the whole process can be achieved.