304 results about "Code module" patented technology

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention relates to the field of twitter recognition, in particular to a twitter classification method based on harmonic enhancement and time-frequency semantic joint modeling, which comprises the following steps: collecting twitter samples and carrying out noise reduction and standardized preprocessing, carrying out multi-scale convolution operation on Mel spectrograms by utilizing a layered acoustic encoder, extracting time-frequency features in combination with a channel attention mechanism, and classifying twitter classification results. The method comprises the following steps of: generating adaptive position codes through a dynamic time-frequency joint coding module, carrying out time-frequency mode modeling by combining a global-local interaction mechanism, introducing a semantic fusion module which comprises a frequency band pyramid unit, a harmonic enhancement unit and a time-frequency gating unit, realizing dynamic weighted fusion of multi-layer features, and carrying out time-frequency mode modeling through a global-local interaction mechanism. And inputting the fusion features into a classification layer, training a network by adopting a cross entropy loss function and a gradient descent algorithm, and outputting bird categories through a full connection layer, thereby solving the key problems of insufficient description of a non-stationary time-frequency mode, insufficient modeling of a harmonic structure, reduction of recognition performance in a complex noise environment and the like in the prior art.

The invention discloses a transformer substation scene three-dimensional semantic segmentation method fusing local geometry and global context, and the method comprises the steps: obtaining the three-dimensional point cloud data of a transformer substation scene, and constructing a semantic segmentation data set; a three-dimensional semantic segmentation model is constructed, and fusion features integrating local geometric information, global context information and neighborhood information are extracted through a down-sampling module, a position coding module, a local-global fusion module and a neighborhood propagation unit in sequence; integrating the multi-scale fusion features through an up-sampling propagation module, and outputting a semantic category prediction result by using a semantic segmentation head; designing a loss function, and training the model; and loading the trained three-dimensional semantic segmentation model to realize refined segmentation of the substation scene. According to the method, the understanding capability of the complex three-dimensional structure of the transformer substation is effectively enhanced, the segmentation precision of refined power equipment parts is remarkably improved, and reliable technical support is provided for intelligent operation and maintenance of the transformer substation.

The invention relates to a power load spatio-temporal dynamic knowledge graph construction and load prediction method, which comprises the following steps of: constructing a text and digital sequence hybrid vector coding module, providing a hierarchical entity relationship joint extraction framework oriented to power system load data, constructing a Multi-Encoder-Bi-GRU-CRF power load entity recognition model, and constructing a power load entity model. Constructing a power load spatio-temporal dynamic knowledge graph in combination with a predefined relation rule base; meanwhile, time-space sub-graphs are divided, a space-time coupling self-adaptive adjacency matrix is constructed, and the space-time dependency relationship between nodes is quantified; and finally, combining the knowledge graph node embedded vector and the adjacency relation embedded vector, and jointly extracting the spatial feature and the time feature of the power load by adopting a space-time diagram convolutional neural network. Therefore, the load prediction algorithm provided by the invention not only can give full play to the advantages of multi-modal semantic integration and space-time modeling capability of the knowledge graph, but also can improve the load prediction precision, assist in realizing refined energy management of the power system and assist in making an optimal scheduling strategy, and has a good engineering application prospect.

The invention discloses a multi-modal image registration method and system based on deformation adaptation and computer equipment, and the method comprises the steps: collecting a plurality of groups of multi-modal images, carrying out the gray standardization, and constructing a diversified registration data set; building a registration network model comprising a pyramid coding module, a deformation adaptive module, a cross-modal interaction module and a registration parameter estimation module; inputting an image pair into the modules in sequence, respectively extracting basic feature mapping, deformation feature mapping and interaction enhancement feature mapping, and finally outputting an estimation conversion parameter matrix; a training process is supervised through a preset loss function, optimal network parameters are selected, and a trained registration model is obtained; in practical application, an image pair to be registered is input into the trained model, a conversion parameter matrix is obtained, and image registration is completed. The multi-modal image registration performance can be effectively improved, and the method still has good robustness and adaptability especially under the condition that serious geometric distortion and significant modal difference exist.

The invention relates to the technical field of artificial intelligence, in particular to an interaction system and method with an emotion dynamic evolution memory function, a medium and a processor. The interactive system comprises a sensing layer, a processing layer and a decision-making layer, the perception layer comprises a semantic text coding module and a fusion processing module; the semantic text coding module outputs the acquired audio as semantic features and high-dimensional features, and performs linear space conversion processing; the fusion processing module and the data output by the semantic text encoding module are linearly spliced to form multi-modal feature vectors, and the multi-modal feature vectors are classified into the current emotion state of the intelligent agent and the emotion state of the user; the processing layer is used for correcting the data output by the sensing layer; and the decision-making layer is used for carrying out information decision-making and storage on the data output by the processing layer. The technical problem that in the prior art, the number of labels is limited, undefined interaction modes are difficult to process, and consequently the character of a robot is limited is solved.

The invention belongs to the field of robot control, and particularly relates to a mobile robot path optimization system and method supporting track mode switching, and the system integrates the operation scene and the real-time load state of a mobile robot through a coding module, and generates a navigation task seed containing a path cost vector, a navigation strategy identifier and a scene conversion point parameter; and the central scheduler dynamically selects and calls a corresponding track navigation strategy sub-model or a flat ground navigation strategy sub-model according to the navigation task seed, and generates an executable track control instruction by combining with a space-time reservation table for realizing multi-vehicle conflict avoidance and resource scheduling based on a continuous value reservation weight. According to the method, load-adaptive path planning and multi-vehicle efficient cooperation are realized, smooth and seamless navigation switching between a track and a flat ground scene is guaranteed through the switching prediction sub-model, and the working efficiency and the system reliability in a mixed complex environment are remarkably improved.

The invention relates to an emotion recognition method and device based on multi-modal consensus and diversity decoupling. The method comprises the following steps: firstly, collecting multi-modal input data including language, vision and audio signals and carrying out corresponding preprocessing; then, constructing a multi-modal consensus and diversity decoupling emotion recognition model which comprises a multi-modal decoupling coding module, a prototype-Gram unification module, a feature enhancement module, a diversity classification module and an emotion prediction head; then, inputting the preprocessed multi-modal input data into the multi-modal consensus and diversity decoupling emotion recognition model, and performing model training optimization based on a total loss function formed by emotion prediction task loss, decoupling loss, unified target loss and diversity loss; and finally, inputting the multi-modal data to be recognized into the trained multi-modal consensus and diversity decoupling emotion recognition model, and outputting an emotion recognition result. And the accuracy, robustness and interpretability of the multi-modal emotion recognition system are improved.

The invention discloses a cloud-edge collaborative video content intelligent analysis and understanding system, which belongs to the technical field of video intelligent analysis, and comprises an edge intelligent sensing module, a semantic feature coding module, a cloud deep understanding module and a collaborative decision module, the coding module adopts Riemannian manifold mapping and quantum heuristic coding to realize efficient compression, the cloud module realizes deep semantic understanding through a graph neural network, the collaborative decision module constructs a closed-loop feedback mechanism to dynamically optimize parameters of each module, and the four core modules are deeply coupled to form a collaborative system. According to the method, cloud edge capability complementation, resource optimization configuration and continuous improvement of system performance are realized, the defects of a traditional scheme in the aspects of cloud edge cooperation capability, semantic understanding depth and adaptive optimization are effectively overcome, and an efficient, real-time and accurate technical scheme is provided for intelligent video analysis.

The invention discloses a cable partial discharge mode identification method and device based on multi-mode fusion, electronic equipment and a storage medium, and belongs to the technical field of cable partial discharge online monitoring. And converting the original signal into a multi-modal spectrum through phase resolution analysis, wavelet packet energy analysis, skewness and kurtosis statistics and recurrence plot analysis. The group of maps are input into a multi-modal fusion recognition model, the model analyzes the spatial distribution characteristics and the time sequence characteristics of the maps at the same time through parallel vision and time sequence coding modules, and two analysis results are subjected to weighted fusion, so that final classification recognition of the discharge mode is realized. Through the implementation of the method and the device, the problems that key information is lost due to dependence on single-dimensional feature representation and the identification accuracy is limited due to the fact that an existing identification model cannot analyze the spatial distribution characteristic and the time sequence evolution characteristic of the signal at the same time in the prior art can be solved.

The invention discloses a response verbal skill generation method and device, electronic equipment and a storage medium, relates to the technical field of computers, can be applied to financial science and technology and medical health business scenarios, and comprises the steps of randomly covering a single character for an original text of a user to generate a difference text; the pre-training intention recognition model comprises a text coding module, a feature fusion module, a multi-task collaboration layer and a classification layer, wherein the feature fusion module comprises a bidirectional long-short-term memory network and fuses an auto-encoder and an attention mechanism. The model is coded to obtain a vector containing overall and local semantics, an enhanced local vector is generated through a bidirectional long-short-term memory network, a global fusion vector is obtained through joint modeling, a multi-task layer optimizes slot positions to extract key information, and a classification layer combines the key information and the global vector to determine intention; and finally, calling the knowledge graph based on the intention, and generating a response verbal skill matched with the demand. According to the method, the response verbal skill matched with the real demand of the user can be accurately generated.

The invention discloses a time-space separation pulse Transform-based target recognition method, electronic equipment and a storage medium. The method comprises the steps of obtaining event stream data which is output by a target neuromorphic visual sensor and is used for target recognition; inputting the event stream data into a pre-trained target pulse neural network, performing pulse coding on the event stream data through a coding module, and outputting pulse coding features; performing local space-time feature extraction on the pulse coding features through a feature extraction module, and outputting local space-time pulse features; performing global spatio-temporal feature extraction on the local spatio-temporal pulse features through a spatio-temporal separation pulse Transform module, and outputting global spatio-temporal enhancement features; and performing linear mapping on the global space-time enhancement features through a task output module, and outputting a target recognition result. According to the invention, while event-driven sparse calculation and low power consumption characteristics are maintained, the discrimination stability of weak, small, sparse and slow moving targets is significantly improved.

The invention relates to the technical field of knowledge tracking, and discloses a graph enhanced double-memory collaborative knowledge tracking model based on an ACT-R cognitive architecture. Comprising a static knowledge structure coding module based on hypergraph projection, a batch-level dynamic learning track construction and coding module, a cross-graph gating fusion mechanism, a sequence modeling module and an expert hybrid prediction module. According to the method, long-term stable structured semantic association between concepts in declarative memory is modeled through a static knowledge structure diagram, a dynamic learning trajectory diagram based on batch reconstruction is designed to accurately capture an evolution rule of a behavior sequence in programmed memory, and on the basis, a cross-diagram gating fusion mechanism and a hybrid expert mechanism are introduced, so that the evolution rule of the behavior sequence in the programmed memory is accurately captured. And self-adaptive fusion and multi-path decision of double-graph features are realized.

The invention discloses a high-speed fixed-point multiplication circuit. The multiplication circuit is mainly composed of a multiplier coding module, a partial product generation module, a partial product compression module and a traveling wave carry adder module. The multiplier coding module is composed of a radix-4-Booth coding algorithm and an opposite number generation module, and is used for carrying out three-bit block coding on input multiplication data and generating an opposite number of a multiplicand in advance. The partial product generation module generates a plurality of groups of partial products with symbol extension according to the multiplicand coded signal. And the partial product compression module adopts an improved Wallace compression structure to perform layered compression on the partial product. And the traveling wave carry adder module sums the two groups of results output by compression and outputs a multiplication result. According to the invention, multiplication accumulation series can be reduced, the switching times of invalid signals in the circuit can be reduced, and the longest delay path in an operation link can be shortened, so that fixed-point multiplication which is high in speed, low in power consumption and more favorable for a comprehensive tool in structure is realized.

The invention relates to the technical field of intelligent costume design, and discloses an intelligent costume pattern generation system based on enhanced multi-modal generation, which comprises a multi-module input module, a layered attention fusion module, a self-adaptive position coding module and a hierarchical decoding module, the multi-module input module is used for receiving a clothing reference image, a version design demand text and structured data, and performing feature extraction respectively to obtain image features, semantic features and structural features; the layered attention fusion module adopts a three-layer attention mechanism to perform fusion processing on the image features, the text features and the structural features to obtain final fusion features; the self-adaptive coding module carries out self-adaptive coding on the final fusion feature and the model parameter of the clothing template, and outputs a coding result; and the hierarchical decoding module is used for decoding the coding result to generate a complete garment pattern.

The invention discloses a context understanding and memory management system and method in large-model multi-round dialogues. The system comprises a five-layer distributed micro-service technology architecture; wherein the business logic layer is integrated with a hierarchical memory management module, a dynamic context coding module, a semantic association engine module and a personalized adaptation module which cooperate with one another; the hierarchical memory management module is responsible for retrieval, hierarchical storage and dynamic updating of dialogue history; the dynamic context coding module is responsible for generating structured context representation; the semantic association engine module is responsible for constructing a cross-round semantic association network; the personalized adaptation module is responsible for generating personalized candidate responses. The technical problems of context loss, memory attenuation, incoherent semantic understanding, insufficient personalized adaptation, low resource utilization rate and the like of an existing large-model multi-round dialogue system are solved, and logic consistency guarantee, memory durability maintenance and personalized experience improvement in a long dialogue scene are realized.

The invention discloses an electric power overhaul video action detection method based on a multi-scale state space, and the method comprises the steps: firstly collecting long video data in an electric power overhaul process, segmenting the long video data into a plurality of time sequence segments, and carrying out the spatial feature coding and high-dimensional mapping of the plurality of time sequence segments, and obtaining a time sequence token sequence; then, an electric power overhaul video action detection network is constructed and trained, and the electric power overhaul video action detection network comprises a time sequence multi-scale coding module, a scale perception state fusion device and a multi-label prediction layer; and finally, performing action detection on a time sequence token sequence corresponding to the electric power overhaul long video data to be detected by adopting the trained electric power overhaul video action detection network to obtain an action category detection result. According to the method, timing sequence multi-scale coding, state space modeling and a scale perception feature fusion mechanism are fused, the short-time sudden action and the long-range dependency relationship can be captured at the same time, and the recognition capability and the positioning precision of the complex concurrent action in the electric power overhaul video are remarkably improved.

The invention relates to the technical field of circuit breaker control, in particular to a cooperative control system of a primary and secondary deep fusion pole-mounted circuit breaker, which comprises a perceptual coding module, an intelligent optimization module, a cooperative decision module, a digital twinning module, a causal reasoning module, a learning evolution module, a semantic communication module and an execution feedback module, multi-modal data acquisition is realized through a neuromorphic sensor, a protection strategy is generated in combination with quantum optimization and chaos detection, collaborative decision is realized by using multi-agent reinforcement learning, strategy verification is performed by means of digital twinning and meta-learning, a decision process is optimized based on causal reasoning, and continuous evolution of a model is realized through neural architecture search. And finally, a standardized control instruction is generated through semantic communication, and closed-loop control is formed, so that the fault early warning accuracy, the control response speed and the equipment service life are remarkably improved.

The invention discloses a federal invariant graph learning method and system for heterogeneous graph data of an unmanned aerial vehicle. The method comprises the following steps: acquiring heterogeneous graph data acquired by an unmanned aerial vehicle; the method comprises the following steps: splitting a local graph structure, and constructing a double-branch representation structure consisting of an invariant branch and a variable branch; processing a local heterogeneous graph based on a sub-graph generation strategy; extracting structure invariant features and local variable features by using a coding module; and stable aggregation of the global model is realized at the server side through double-gradient regularization. The system comprises an unmanned aerial vehicle client module, a server aggregation module, a model construction module, a structure representation module, a sub-graph processing module, a coding module and a training control module, and all the modules cooperate to complete federal invariant graph learning. According to the method, the difference and consistency of the unmanned aerial vehicle data are considered without uploading the original data, the problem of non-independent identical distribution is effectively relieved, and the stability and generalization ability of the model are improved.

The invention relates to the technical field of power system equipment detection, and discloses a mouse linkage wireless intelligent triggering system and method for a switch cabinet mechanical characteristic test, and the system comprises a motion collection module which is used for collecting the operation parameters of a mouse, building an operation behavior feature library, and transmitting the operation behavior feature data to a signal coding module in real time; the signal coding module is used for receiving the operation behavior characteristic data of the mouse, collecting an opening and closing instruction electric signal generated by a telecontrol background, extracting behavior characteristics and electric signal characteristics, and generating a unique opening characteristic code and / or a closing characteristic code; the tester intelligent receiving module is used for receiving the opening feature code and / or the closing feature code, and starting a dual mechanism of time window verification and feature code matching verification to verify the feature code; and the cloud collaborative management platform is used for receiving data uploaded by the action acquisition module, the signal coding module and the tester intelligent receiving module in real time. The efficiency of testing the mechanical characteristics of the switch cabinet is improved.

The invention relates to the technical field of pulse neural network computing hardware, in particular to a neural network computing circuit of a pulse self-attention mechanism. According to the method, invalid or inefficient pulse events are dynamically screened through the hardware mask module, the operation number is remarkably reduced, and calculation path delay and logic resource occupation are reduced; meanwhile, in order to adapt to novel networks with binary architecture such as QKFormer, calculation and storage of a V matrix are eliminated, and storage resources and calculation resources are further reduced; in addition, the event coding module only generates active neuron events, and input sparsity is achieved. A configurable IF neuron model is adopted, exponential operation is avoided, hardware implementation is facilitated, and the method is suitable for binary network deployment. The modular architecture can support function extension, assembly line and parallel work; and the parallelism degree of the design can be determined according to the actual data pulse distribution rate. Event driving and mask pruning are combined, so that the overall computing resources of the circuit are greatly reduced, and the power consumption is reduced.

The invention discloses a dual-mode communication chip, and belongs to the technical field of communication. The dual-mode communication chip comprises an HPLC (High Performance Liquid Chromatography) signal transmitting end and an HPLC signal receiving end; the HPLC signal transmitting end is configured as a coding module and is used for coding the load data to obtain coded load data; the first storage module is used for storing the coded load data; the channel interleaving module is used for carrying out interleaving processing on the bit data read out from the first storage module; the first bit buffer queue is used for sequentially writing the bit data from the channel interleaving module according to a first bit number and sequentially reading the bit data according to a writing sequence according to a second bit number; and the bit mapping module is used for mapping the bit data from the first bit buffer queue to each subcarrier with a second bit number. According to the invention, rate efficient matching of data processing in a high-order modulation mode and data transmission without subcarrier cross-physical blocks are realized.

The invention discloses a battery life prediction model and system and a readable storage medium. The battery life prediction model comprises a data processing module, an interactive coding module, a multi-scale fusion module, a global prediction module, a trend modeling module and a hierarchical fusion module, and all the modules cooperate to achieve high-precision battery life prediction. The data processing module extracts core features and constructs an input sequence to provide high-quality support for prediction; and the interactive coding module adopts a double-axis attention mechanism to mine feature association and time sequence rules and output enhanced features. The multi-scale fusion module extracts and fuses long-term trend and local fluctuation features through a double-branch structure; and the global prediction module generates a global prediction result through feature convergence, adaptive weighted fusion and regression mapping. The trend modeling module separates a core performance index sequence and captures a battery aging rule through a dual-path network; and the hierarchical fusion module fuses the two types of prediction results by means of a gating network to avoid single-mode deviation. All the modules cooperatively improve the data utilization rate, and the prediction precision and reliability are remarkably improved.

The invention belongs to the technical field of digital image transmission, and particularly relates to a digital image transmission system based on ultra-low time delay, which comprises the following steps: firstly, acquiring digital image data in a scene in real time by using a high-frame-rate and low-noise image sensor, and transmitting the acquired original image data; then, carrying out preprocessing operation on the collected original image data; then, a deep neural network model is utilized to adaptively adjust coding parameters and a coding mode according to the content characteristics of the image and the real-time transmission bandwidth condition; on the basis, the adaptive lightweight coding module is arranged, a complex inter-frame prediction algorithm in a traditional coding standard is abandoned, a block-level differential coding mode is adopted, and coding parameters and modes are adaptively adjusted according to image content characteristics and real-time transmission bandwidth; the method has the advantages that the coding delay is effectively reduced, and the high-real-time scene coding requirement is met.

This invention relates to a skeletal action recognition method based on spatiotemporal topology learning, belonging to the field of computer vision and artificial intelligence technology. The method includes: constructing a two-dimensional topology coding module to simultaneously capture the static and dynamic topological relationships of the skeletal structure; employing a multi-level spatial attention aggregation module to adaptively weight and aggregate skeletal features at different levels to enhance the expressive power of key nodes and edges; and designing a temporal dynamic sequence pooling method to extract high-order dynamic information from time-series data based on a dynamic modality decomposition strategy, thereby improving the accuracy of action recognition. The advantages are: this method can fully utilize the spatiotemporal structural information of the skeleton data to achieve accurate recognition of human actions, while improving the modeling ability for complex dynamic behaviors, avoiding the limitations of traditional methods in capturing high-order dynamic information.

The application relates to the technical field of data processing, and discloses an automatic prescription configuration system based on digital symptom analysis, which comprises a database module, a semantic analysis module, a first coding module, a second coding module and a retrieval module; wherein: the database module is used for storing prescription configuration data and description sentences of user's each round of symptom description; the semantic analysis module is used for performing semantic segmentation on the description sentences and identifying core descriptions and modification descriptions; the first coding module is used for respectively coding each group of modification descriptions and corresponding core descriptions into description vectors; the second coding module fuses description vectors of different rounds into comprehensive description vectors; and the retrieval module generates a prescription matching list based on the comprehensive description vectors and the prescription configuration data. Through improving the semantic structuralization degree and consistent expression capability of non-standard and multi-round symptom descriptions, the application improves the distinguishability and practical value of the description information in the configuration matching process.

The invention discloses a hardware acceleration device and method for high-throughput lossless data compression, and relates to the field of integrated circuit design. The device comprises a front-end LZ77 compression module, a middle buffer module and a rear-end entropy coding module, wherein the front-end LZ77 compression module is configured to search and match input data; the intermediate buffer module is configured to realize decoupling of front and rear end processing time domains through an asynchronous symbol buffer; the back-end entropy coding module is configured to receive an output of the intermediate buffer module, construct a microcode packet intermediate representation containing codeword information, and parse the microcode packet to generate a serial compressed bit stream. According to the method, invalid memory reading and assembly line pause can be effectively eliminated, the throughput and the energy efficiency ratio of the hardware compressor are remarkably improved on the premise of not sacrificing the compression rate, and the method is suitable for unloading acceleration of a high-performance solid state disk controller, an intelligent network card and a cloud server.

The present application relates to the technical field of software development, and especially relates to a webpage online updating method and device, equipment and a storage medium, the present application calls a to-be-developed page in a production environment on the client side according to a target unique identifier; receives a development code input by a developer through a front-end and back-end communication tool; renders the development code by at least one preset plug-in to obtain a plurality of code modules; updates the to-be-developed page according to the plurality of code modules, realizes the calling of the to-be-developed page in the production environment on the client side, and renders the development code input by the developer through at least one plug-in, thereby forming modular development code, updating the to-be-developed page according to the code modules, shortening the development cycle, and avoiding the technical problem that the developer is difficult to form joint debugging and testing with the client side when updating the webpage, thereby leading to a long development cycle.

Techniques for transforming code modules to different programming languages are disclosed. A system accesses a first non-code representation of a first code module expressed in a first programming language and parses the first non-code representation to identify a nested data element of the first non-code representation that represents a nested expression of the first code module. The system executes a transformation technique to transform the nested data element, in the first non-code representation, to a first non-nested data element in the first non-code representation. The system modifies the first non-code representation based on one or more attributes of a second programming language to generate a second non-code representation suitable for representing code modules in the second programming language. The system generates a second code module based at least on the second non-code representation.