248 results about "Coding quality" patented technology

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention provides a code quality adaptive evaluation and optimization method, system and device and a medium, and belongs to the technical field of computers. The method comprises the following steps: performing redundant information cleaning and standardization processing on an original code; selecting an adaptive parser to convert the processed code into an abstract syntax tree, and optimizing the abstract syntax tree; performing embedding processing on each node of the abstract syntax tree by using a pre-trained deep learning model to generate a node semantic vector, and performing aggregation, feature extraction and vector normalization operation to obtain a semantic vector representing code semantic features; extracting a dependency relationship from the abstract syntax tree to construct a program dependency graph, extracting node features by using a neural network model, and generating a dependency relationship analysis result; based on the semantic vector and the dependency analysis result, identifying a code potential problem by using an optimization algorithm and generating an optimization suggestion; codes are automatically modified according to optimization suggestions, the optimization effect is re-evaluated, and related models and algorithms are iteratively optimized according to feedback information.

The invention provides a Java-to-ArkTS code conversion method and system based on a large model, and belongs to the field of computers.The Java-to-ArkTS code conversion method comprises the steps that S1, a Java project to be converted is comprehensively analyzed, the dependency relationship and semantic information between files are extracted, and a code knowledge graph is constructed; s2, determining a code conversion sequence based on the code knowledge graph, extracting context information and multi-modal semantic information of a to-be-converted code, and completing conversion from Java to TS codes in combination with a multi-agent cooperation mechanism; and S3, comparing and analyzing the TS code with an official ArkTS coding specification, identifying a violation code, and correcting and verifying the violation code through a plurality of intelligent agents to generate an ArkTS code which accords with the ArkTS grammar specification and is consistent in semantics. According to the method, the automatic migration from the Java project to the ArkTS project can be effectively supported, the project migration efficiency and the code quality are improved, and the manual intervention cost is reduced.

The invention particularly relates to a mobile camera management system capable of adaptively adjusting video coding quality. According to the mobile camera management system capable of adaptively adjusting the video coding quality, a network condition monitoring and predicting module is used for monitoring the current network bandwidth and packet loss rate in real time and predicting the network state; the video coding control module is used for dynamically adjusting video coding parameters according to the current network bandwidth and the ROI; and the adaptive adjustment strategy module is responsible for issuing a video coding strategy to the video coding control module according to the data fed back by the network state monitoring and prediction module so as to realize dynamic adjustment of the video coding quality. The mobile camera management system capable of adaptively adjusting the video coding quality can avoid video lagging and frame loss when the network bandwidth is relatively low, is suitable for various mobile devices such as a vehicle-mounted device, an unmanned aerial vehicle and a wearable camera, effectively improves the continuity and availability of video transmission, and realizes stable application in an environment with unstable signals.

The invention relates to the technical field of video coding and image processing, and discloses a code rate control method and system based on video image segmentation, and the method comprises the steps: carrying out the pixel-level semantic segmentation of a to-be-coded video frame sequence; extracting a foreground region of interest; calculating a corresponding segmentation uncertainty parameter; establishing semantic mutation parameters of the foreground region of interest; generating a semantic perception weight; executing region-level target code rate redistribution and quantization parameter mapping; and executing partition coding control. In the prior art, code rate control mainly depends on motion intensity or pixel complexity, and especially when a foreground target suddenly appears or disappears in a monitoring scene, a technical problem that a key target is blurred or a background code rate is wasted is easily caused. Due to the fact that the uncertainty modeling and semantic mutation sensing mechanism of the semantic segmentation result is introduced, priority coding of the foreground interest area is achieved under the frame-level code rate constraint condition, and the video coding quality and the code rate utilization efficiency are improved.

The invention relates to the field of computer software, and provides an AI-based low-code visual component dynamic generation method and system. The method comprises the following steps: receiving a component demand description input by a user, and converting the component demand description into structured cue word data to obtain an initial cue word sequence; retrieving matched template data and grammatical rule data from a component knowledge base according to the initial cue word sequence, and fusing the template data, the grammatical rule data and the initial cue word sequence to obtain an enhanced cue word sequence; performing semantic analysis and code generation processing on the enhanced cue word sequence through a large language model to obtain a component source code; and performing real-time rendering processing on the component source code through a component rendering engine, and generating an interactive component instance in a preview interface to obtain reusable component resources. According to the method, the code quality consistency is ensured, and the development efficiency is improved.

The application provides a business module source code generation method and system based on large model business reasoning, and belongs to the technical field of code generation, which comprises the following steps: performing semantic analysis on business requirements, and constructing a business association graph with weights; performing module division to decompose a complete task into multiple subtasks; calling a coordination agent, assigning a corresponding large model-based module code generation agent to each subtask after decomposition, and injecting context information to generate corresponding module code; wherein the module code generation agent has been pre-trained and learned the corresponding relationship between the task and the injected context information and the code based on domain adaptation in stages; and calling a verification agent to verify the code quality. Advantageous effects: the module division is performed based on the business association graph with weights, the multi-agent collaborative code generation is constructed by adopting the domain adaptation and stage-by-stage training, and the quality of the generated code is verified, thereby improving the code generation efficiency and quality.

The invention discloses a unit test code generation method, system and device and a medium, which are applied to the technical field of code generation, and comprise the following steps: performing program structure interface analysis and abstract syntax tree analysis on a target engineering code to obtain a structured meta-information set and a source code syntax structure model; generating a structured context feature vector based on the structured meta-information set and the source code grammar structure model, and retrieving a historical test case sample set from a historical test case database, wherein the global line coverage rate weighted similarity of the historical test case sample set and the structured context feature vector meets a matching condition; after Prompt template filling of different test subtasks is performed based on a historical test case sample set and structured context data through an Agent agent, different modular test codes are generated by adopting different test code generation models, and closed-loop verification is performed after the different modular test codes are integrated into unit test codes. Therefore, the code quality, practicability and adaptability are improved.

The invention discloses a code generation method and device based on a large model, a storage medium and computer equipment. The method comprises the steps that a UI prototype draft of a target service is acquired; performing element identification on the UI prototype draft through a picture identification large model, and determining element information in the UI prototype draft; generating a code generation cue word based on the element information and a preset cue word, inputting the code generation cue word into a code generation large model to obtain an intermediate code corresponding to the UI prototype draft, and arranging a target service code of the target service based on the intermediate code. The automatic process from UI design to code generation is achieved, the tedious process of manually writing UI components and debugging interaction logic in traditional development is avoided, repeated work and human errors are reduced, developers can put more energy into core service function development, then development efficiency is improved, code quality is guaranteed, and the development cost is reduced. And rapid and high-quality delivery of the project is promoted.

The invention provides an AI auxiliary code generation method and system applied to a low-code platform, and relates to the technical field of low-code platforms.The AI auxiliary code generation method comprises the steps that firstly, component demand scenarized information is obtained and analyzed to obtain a scenario feature set, and a dynamic evolution rule set is generated in combination with platform real-time metadata; calling an AI code generation module, inputting the scene feature set and the rule set, and generating an initial component code; then deploying the initial code to a test environment, collecting and analyzing operation data, generating an iterative optimization instruction adjustment code and a rule set, and obtaining a target component code; and finally, embedding the target code into a platform specified function module to complete code generation. The method can adapt to diversified scene requirements, has a self-optimization capability in combination with the real-time state of the platform, and can improve the development efficiency and code quality of the low-code platform.

The invention discloses a code quality verification method and device, equipment and a storage medium. The method comprises the steps that a to-be-verified target code file and verification context information corresponding to the target code file are received; based on the verification context information, screening out a verification rule set meeting a preset requirement from a rule base, and arranging the verification rule set to generate a rule set arrangement task; calling a corresponding verification execution engine to perform automatic verification on the target code file according to the characteristics of each rule in the rule set arrangement task, and generating an initial verification result; and carrying out fusion processing on the initial verification result to obtain a fusion result, and carrying out risk grading on the fusion result based on a preset priority strategy to generate a standardized verification result. According to the code quality verification method and device, the verification rule set meeting the preset requirement is screened out from the rule base based on the verification context information, the target code file is automatically verified according to the rule set arrangement task, and the reliability and the verification efficiency of code quality verification are improved.

The invention relates to a code generation method and device, electronic equipment and a storage medium. Comprising the steps of obtaining a programming problem data set and a code generated by a target model, and obtaining a data set with rewards; based on an MSE regression training strategy and a comparison loss training strategy, training a reward model by using the data set with the reward to obtain a code reward model; and obtaining programming problem training data, sorting the programming problem training data by using the code reward model to obtain sorted training data, and obtaining a final reward according to the sorted training data, a reward model score output by the code reward model and a preset unit test score, and reinforcement learning training is carried out on a preset language model to obtain a code generation model, so that codes meeting preset performance requirements are generated through the code generation model. Therefore, the problems that in the prior art, the model reasoning cost is high, and it is difficult to capture the intermediate state and partial correctness in the code execution process to evaluate the code quality in a fine-grained mode are solved.

The invention discloses an audio signal processing method and device, equipment and a storage medium, and relates to the technical field of signal processing, and the method comprises the steps: obtaining an input target audio signal, and detecting whether the target audio signal is voice or not; if the target audio signal is voice, determining a first signal in the target audio signal based on a preset frequency range, and determining a first bit stream and a broadband voice signal based on the first signal by using a preset voice vocoder; determining a residual signal based on the target audio signal and the broadband voice signal, compensating the residual signal, and encoding the compensated residual signal by using a preset audio vocoder to obtain a second bit stream; and determining a target bit stream based on the first bit stream and the second bit stream, setting a voice mark in the target bit stream as a first mark, and sending the target bit stream to a decoding end, so that the decoding end decodes the target bit stream. Therefore, the coding quality of the voice signal by the audio vocoder can be improved.

The invention discloses a real-time video processing method and system based on artificial intelligence, and belongs to the technical field of computers. Through deep fusion of multi-source information and a regional adaptive coding mechanism, the problem of collaboration between content generation and network resource allocation in a high-interactivity live broadcast scene is solved. In the feature extraction link, video, audio and text streams are synchronously processed, so that three-dimensional perception of a live broadcast scene is realized; in the multi-mode driving instruction set generation process, audio event features and text interaction features are subjected to semantic association, so that content generation and real-time interaction are highly synchronized, and response delay caused by information splitting in a traditional scheme is avoided; according to the regional differentiation coding processing, resource allocation is dynamically adjusted according to the regional importance of visual semantic feature recognition and the network state, and the coding quality of a digital human region is preferentially guaranteed, so that the stability of core visual elements is maintained under the network fluctuation condition, and the intelligent level of real-time video processing and the user experience are improved.

The invention discloses an interactive front-end generation method and system based on API specifications and multiple intelligent experience certificates, and belongs to the field of front-end software development automation. A double-stage intelligent generation framework is adopted, natural language query and back-end API specifications of a user are received, an optimal retrieval stage and an iteration generation stage are executed in sequence, and a code version with the highest comprehensive score is selected from iteration history to serve as a generated interactive front-end prototype code. Wherein in the retrieval stage, related UI components, icons and general layout structures are accurately retrieved to provide a high-quality context; a closed-loop optimization process dominated by an encoder and coordinated by multiple agents is started in iteration generation, the code quality is verified in a multi-dimensional mode, and the encoder agents are driven to conduct multi-round iteration correction on the code according to the verification result. And finally, the prototype code preferentially output can perform real data interaction with a back-end API, and can be used as a Demo or a starting point for rapid development, so that the front-end development efficiency is remarkably improved.

The invention provides an automatic calculation method, system, equipment and program product for incremental code coverage rate, which are applied to the field of software testing, and the method comprises the following steps: receiving a code version change event, collecting and merging binary coverage rate data, obtaining an incremental code change set, and generating a structured coverage rate report. According to the scheme, the incremental code coverage rate can be accurately calculated, and the code quality measurement efficiency is improved.

The invention discloses an audio coding method and device, equipment and a storage medium, and relates to the field of audio processing, and the method comprises the steps: obtaining an initial audio signal in a target environment, and carrying out the framing processing of the initial audio signal, and obtaining corresponding audio frames; determining the type of each audio frame based on the time domain feature and the frequency domain feature of each audio frame; if the audio frame is a first type of frame, encoding the first type of frame based on a preset voice encoder to obtain a corresponding first audio code stream; if the audio frame is a second type of frame, encoding the second type of frame based on an Audio Vivid encoder to obtain a corresponding second audio code stream; if the audio frame is a third type of frame, encoding the third type of frame by using a preset weighted fusion encoding mode to obtain a corresponding third audio code stream; and determining a target audio signal based on the first audio code stream, the second audio code stream and the third audio code stream. Therefore, the audio coding quality can be improved.

The invention relates to the technical field of artificial intelligence, and discloses a code generation method and device, computer equipment and a storage medium in the fields of financial science and technology and medical health. The method comprises the following steps: performing semantic analysis on a code generation request to obtain a request semantic tag and a request keyword, and performing text embedding processing on the code generation request to obtain a request conversion vector; searching at least one candidate code node matched with the request semantic tag from a preset code knowledge graph; screening all the candidate code nodes according to the request conversion vector to obtain a reference code node; performing node expansion processing on all the reference code nodes according to the request keyword to obtain context nodes; determining target prompt information according to the code generation request, the reference code node and the context node; and inputting the target prompt information into a preset large language model to obtain a target code. According to the method, the semantic alignment degree and the code quality are improved, and the availability of the generated code is ensured.

The invention provides a controller program generation method based on semantic planning and quality optimization, and belongs to the technical field of automation. A multi-stage PLC program generation and optimization method oriented to natural language task description is adopted, and semantic retrieval enhanced task planning, structured code synthesis and a quality closed-loop optimization mechanism based on feedback are fused. A natural language task is automatically converted into a verifiable plan with logic constraints by constructing a semantic index, reasoning scheduling and an intermediate representation structure; through module clustering and multi-round prompt guiding type logic reconstruction, generation of a high-quality PLC program meeting the IEC 61131-3 standard is realized. And static analysis and test feedback are combined, and the code quality is evaluated and enhanced by adopting iterative optimization and a scoring mechanism, so that the problems of intelligence, structuring and verification of the whole process from natural language to PLC program are systematically solved, and the development efficiency, the program robustness and the feasibility of industrial deployment are remarkably improved.

The invention discloses a software code defect automatic detection and repair system based on deep learning, and relates to the technical field of deep learning technology.A data acquisition module acquires multiple source codes and metadata and adapts to a mainstream programming language; the preprocessing module cleans codes, extracts features and divides data sets; the detection model is used for identifying 12 types of defects by using a Bi-LSTM (Bidirectional Long Short Term Memory) and Transform mixed architecture; the repair model generates a repair scheme based on CodeBERT; defect verification is integrated with static / dynamic verification; the system management realizes configuration, user, task and log management, and supports RBAC authority control. According to the method, the code defect detection accuracy and repair efficiency are improved, and multiple defect types and programming languages are covered; the repairing scheme adapts to the scene and is explained, so that the labor cost is reduced; model dynamic updating and cross-language adaptation are supported, a development process can be integrated, and the comprehensive effects of code quality improvement, efficiency improvement and maintenance cost reduction are achieved.

The invention discloses a cross-platform page code generation method based on a large model, and relates to the technical field of front-end development, and the method comprises the following steps: cooperatively analyzing a UI design drawing through a plurality of special large models, and carrying out real-time cross validation and dynamic compensation on an analysis result by adopting a multi-model mutual verification mechanism; based on a predefined standard specification, dynamic adjustment is carried out through a platform adaptation rule base; generating a page object description tree embedded with the input / output processing function; cross-platform page codes are generated through a code generation engine, and sandbox testing and automatic correction are executed; outputting a target platform code by utilizing a unified compiling engine; an adaptive optimization loop is constructed based on test feedback. According to the method, through integration of multi-model collaborative analysis, platform rule dynamic adaptation, sandbox verification and closed-loop optimization, the problems of large analysis deviation, poor cross-platform compatibility, uncontrollable code quality and the like are solved, and end-to-end high-quality automatic generation from a design drawing to multi-platform codes is realized.

The invention discloses a WeChat applet JS-SDK code defect detection method, system and device and a medium, and the method specifically comprises the steps: based on an instrumentation framework, carrying out API calling through a wx proxy object of a WeChat applet, recording an API calling path, a parameter combination and an execution state, and generating API-level fine-grained coverage rate data; the API-level fine-grained coverage rate data, API calling tracking data and code version library change records are stored in an associated mode, and a time-space associated data warehouse is constructed; based on the data warehouse, modeling the historical coverage rate sequence by using an LSTM neural network, predicting the risk level of uncovered codes, and generating targeted test case suggestions; based on the data warehouse, uncovered codes and recent code changes are matched through an Szz algorithm, potential defect positions are positioned, and defect confidence is marked. The WeChat applet JS-SDK code quality guarantee method provides comprehensive and accurate support for WeChat applet JS-SDK code quality guarantee, and the development efficiency and the code reliability are improved.

The present disclosure provides a method and apparatus for processing code, a method and apparatus for training a code model, and a device, which relates to the field of artificial intelligence, and in particular, to the technical fields of software development, data processing, large models and computer communication. The method for processing code includes: acquiring target pasted code in response to a code paste event being monitored; performing dependency analysis for the target pasted code to obtain context-dependent data corresponding to the target pasted code; performing, according to the context-dependent data, code optimization for the target pasted code to obtain optimized code corresponding to the target pasted code; and outputting the optimized code. The present disclosure can greatly improve development efficiency, seamlessly integrate optimized code into the current code development environment, and improve code quality.

The invention discloses a multi-factor checking method and checking system for code quality, and belongs to the technical field of electric digital data processing, and the method comprises the following steps: obtaining a first code, code detection demand description and a knowledge base; obtaining a plurality of agents, wherein the detection factors of the plurality of agents are different; according to the code detection demand description, matching a corresponding agent; checking the corresponding demand description, the knowledge base and the first code through the intelligent agent to obtain a review result; and fusing the plurality of reviews to obtain a fusion result, and generating an inspection conclusion. A plurality of agents are used for checking a plurality of detection factors on a first code, the agents check the first code for factors which are good at the beginning, and the advantages of the agents are fully utilized; and the inspection results of the intelligent agents are fused to form an inspection conclusion, so that the inspection efficiency and the inspection effect are improved.

The invention discloses an AI-based code quality and defect analysis method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a code merging request submitted by a user; in response to the code merging request, extracting code change content based on the code change version; inputting the code change content into a pre-trained large model, and performing semantic analysis on the code change content by the large model to detect potential quality problems in the code change content; for each potential quality problem, obtaining related code context information of the potential quality problem, inputting the related code context information into a pre-trained large model, and performing authenticity verification by the large model based on the related code context information to screen out a real quality problem; and according to the real quality problem, generating a code review report. According to the method, efficient, objective and continuous code quality review can be realized on the premise of ensuring the review quality.

The application discloses a video time variable sampling method and device based on a reversible network and a medium, belongs to the technical field of video coding, and solves the problem of serious loss of information in the down-sampling and coding stages during video time variable sampling coding; the reversible time domain wavelet transform structure of multiple input and multiple output is adopted, input high frame rate video is decomposed into odd and even sequence frames, a bidirectional reference frame mechanism is introduced, a time domain low frequency subband and a high frequency subband are generated, high-quality video time frequency band decomposition can be realized without transmitting explicit motion information between forward transformation and inverse transformation, a gradient agent network simulating the compression behavior of a traditional encoder is introduced, motion vectors and quantization parameters are taken as input, a reversible affine coupling transform model encoder perceiving coding quality is adopted to code the distortion of the time domain low frequency, and the defects that the existing agent network depends on motion vector transmission or fixed coding parameters and is difficult to adapt to actual variable application scenarios can be overcome.

The application discloses a reliability evaluation and optimization method and system for large model function code, a medium and a program product, and belongs to the technical field of artificial intelligence. A demand prompt word, a test case and expected output are acquired to form a standardized test data set; an execution consistency score, a semantic consistency index and a static structure consistency index of the code to be evaluated are calculated and fused into an intention consistency comprehensive score; a safety score is obtained by performing static safety analysis on the code to be evaluated; the intention consistency comprehensive score and the safety score are fused into a final comprehensive score, and the reliability is judged; if the reliability is not good, a defect label is generated according to the scores of the indexes and weighted fusion is performed to obtain optimization guidance information, and the code is corrected or regenerated according to the information. The application comprehensively evaluates the code quality from four dimensions of execution, semantics, structure and safety, highlights the short board effect by adopting a fusion strategy, realizes closed-loop automatic optimization, and significantly improves the reliability and safety of the function code.

An audio decoding method is performed by a computer device. The method includes: obtaining an audio feature parameter corresponding to audio frames in an original audio; performing encoding bit rate prediction processing on the audio feature parameter through an encoding bit rate prediction model, to obtain an audio encoding bit rate of the audio frames, wherein the encoding bit rate prediction model is configured to predict the audio encoding bit rate according to a target encoding quality score; performing audio encoding on the sample audio frames based on the corresponding sample encoding bit rates to generate sample audio data corresponding to the sample audio frames; and generating target audio data from the original audio by performing audio encoding on the audio frames based on the audio encoding bit rate.

This invention provides a method for improving the coding quality of multi-channel signals and an encoder. [Solution] The method includes the steps of: acquiring a multichannel signal of the current frame; determining an initial interchannel time difference (ITD) value for the current frame; and controlling the number of target frames that are allowed to appear consecutively based on characteristic information of the multichannel signal. The characteristic information includes at least one of the signal-to-noise ratio parameter of the multichannel signal and the peak features of the cross-correlation coefficient of the multichannel signal, and the ITD value of the frame preceding the target frame is reused as the ITD value of the target frame. The method also includes the steps of: determining the ITD value of the current frame based on the initial ITD value of the current frame and the number of target frames that are allowed to appear consecutively; and encoding the multichannel signal based on the ITD value of the current frame.

The invention relates to a code generation method and system based on a large language model, and belongs to the technical field of computers. According to the method, dialogue is carried out on the basis of the generated first version code and the large language model to provide more detailed and specific input, so that the large language model generates a corresponding subprogram, and complete code logic is constructed; calling an example code stored in an example code library to further optimize the initial code based on multiple rounds of conversations, and generating a second version of code; and performing comprehensive quality evaluation on the generated second version code based on a preset code quality evaluation standard, automatically generating a repair rule based on an evaluation result, and generating a target code by using the repair rule. According to the method and the device, the repair rule is obtained according to the evaluation result obtained by performing comprehensive quality evaluation on the second-version code, the second-version code is repaired based on the repair, so that the high-quality target code is obtained, and the whole generation process is more intelligent.