136 results about "Autocode" patented technology

The invention provides a pre-training enhanced space-time Transform traffic flow prediction method, which belongs to the field of intelligent traffic, aims to improve the prediction performance in a complex traffic environment by designing a space-time Transform model and a pre-training strategy, and comprises the following steps of: firstly, pre-training an encoder through a mask automatic encoding strategy by utilizing long-term historical traffic flow data, and then pre-training the encoder through a mask automatic encoding strategy; extracting long sequence features by adopting time perception attention embedded with time period information; constructing a pre-training space-time sparse graph generated by a data-driven graph learning layer to capture a dynamic spatial dependency relationship; in the prediction stage, a time feature extraction module and a spatial feature extraction module are designed, an improved multi-head self-attention mechanism is utilized to model correlation between time and space, and long sequence features and spatial-temporal features are adaptively fused through a gating mechanism to obtain a prediction result. According to the method, long-term historical traffic flow sequence information can be effectively extracted, and the traffic flow can be predicted more accurately under a complex traffic condition.

A publishing system with components, including:a system configured to receive at least one document including text that defines a base alphabet in one or more formats;a system configured to provide additional data for a reader to better understand the document which includes:a method of encoding or marking up non-phonetic words in the document to enable the reader to decode sounds of each non-phonetic word; anda system configured to output an encoded document with the text and the additional data in one or more formats,wherein the method of automatically encoding the non-phonetic words to make the encoded words phonetic:for at least one character (“spelling character”) in the non-phonetic word, using a compound character that includes the spelling character and a sound character, wherein the sound characters:are human-readable characters in the base alphabet and / or in one or more secondary alphabets,are added to the spelling characters to indicate that each spelling character makes the usual sound of the sound character,are added so that spelling characters can be visually discriminated from sound characters,are added such that a reader can recognize the non-phonetic word by sight because the spelling of the word is unchanged, andare added to the spelling characters such that the spelling characters and the sound characters remain human-readable such that the spelling character and the sound character of each compound character are within one visual field; andautomatically outputting the encoded words in a human-readable form / format such that the compound characters in the encoded word visually indicate which of the spelling characters have a sound other than their usual sound and what sound each character makes in the non-phonetic word when it does not make its usual sound.

The invention provides an electronic health record automatic coding method and system, and the method comprises the steps: segmenting an electronic health record text into a plurality of text segments, and obtaining a plurality of unstructured text contents; obtaining structured key medical information based on a preset large language model and the plurality of unstructured text contents; generating a preliminary coding result based on a preset coding rule base and the structured key medical information; and obtaining a final coding result based on a preset verification model, a preset auditing model and the preliminary coding result so as to complete automatic coding of the electronic health record. According to the method, the key health information in the non-structural text is coded, and the coding result is verified, so that the accuracy of the coding result is improved.

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional autoencoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional autoencoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and denoising. The models trained on the MNIST handwritten-digit and CIFAR-10 image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

The invention belongs to the technical field of medical data processing, and discloses an automatic ICD coding method and system based on fusion of various label information, and the method comprises the steps: carrying out the feature extraction of the description of a clinical text and an ICD label, and obtaining the representation of the clinical text and the representation of the label; performing interactive fusion on the ICD tag description and the ICD tag description to obtain tag-related context representation, extracting co-occurrence representation of the tag representation, and fusing the co-occurrence representation with the ICD tag description and the tag-related context representation to obtain enhanced tag representation; performing interactive fusion on the enhanced tag representation and the clinical text representation to obtain a document representation corresponding to each tag, and predicting an ICD code based on the document representation; and defining a loss function, and training the model. According to the method, information from three aspects of context, tag original semantics and tag correlation is fused, richer input features are provided for automatic ICD coding, the comprehensiveness and accuracy of coding are improved, and important support is provided for clinical decision making and medical resource allocation.

The invention discloses an AI-based BOM material information automatic coding method, which comprises the following steps of: unifying data structures of BOMs of products at different stages, storing the data structures as a single BOM data set, screening single data with a single bit number, comparing part of data with a standard database, cleaning and then comparing when the data are not consistent with the standard database; a knowledge graph, vector conversion, cosine distance and the like can be used for further processing so as to convert single bill of material data into standard data; for data which cannot be matched, multiple strategies can be adopted to select matching entries; the invention further relates to format recognition of the bill of materials and selection of processing tools so as to ensure data integrity. When data of a single bill of material is denoised, key parameters are extracted in combination with multiple data sources, association rules are mined through a data mining algorithm, and denoising operation is executed. The method can improve the accuracy and standardization degree of material data, greatly improves the coding efficiency, and achieves the intelligentization and refinement of material management.

The invention discloses a digital typesetting design system and method for a clean workshop enclosure structure based on a BIM technology. The digital typesetting design system comprises a room processing unit which comprises a room generating unit, a room merging unit and a room separating unit; the typesetting area division unit is used for distinguishing room practice, cutting ceilings, combining the ceilings, typesetting partitions and cancelling the partitions; the typesetting unit comprises typesetting configuration and one-key typesetting; the top plate typesetting and adjusting unit comprises ceiling typesetting, top plate adjusting, top plate dividing and top plate combining; the wallboard typesetting and adjusting unit comprises wallboard typesetting, wallboard adjustment, wallboard segmentation and wallboard combination; the optimization typesetting unit is used for optimizing non-standard boards of the wallboards; the plate coding unit comprises coding rule definition, automatic coding and non-standard combination; and the typesetting result output unit is used for generating a plate purchase list, a profile purchase list and a typesetting drawing. According to the method, rapid and refined generation, three-dimensional intelligent typesetting, non-standard plate optimization and numbering drawing of the building envelope model can be realized, the building envelope typesetting efficiency and quality are improved, and the problems of low typesetting efficiency and insufficient optimization in an existing method are solved.

Examples described herein generally relate to alerting metric baseline behavior change. The examples include performing at least one of a radial basis function (RBF) kernel procedure and an autoencoding procedure for a time-series data; determining whether one or more change points occur in a seasonal pattern of the time-series data based on at least one of the RBF kernel procedure and the autoencoding procedure; and transmitting, to a user, an alert indicating the one or more change points based on a determination that the one or more change points occur in the seasonal pattern of the time-series data.

The invention discloses a graph processing method and system based on graph automatic coding, and the method comprises the steps: carrying out the graph node preprocessing of graph data based on a mask and noise; performing graph edge preprocessing on the graph data based on the directionality between the graph nodes and the graph edges; inputting the preprocessed graph data into a graph neural network model to complete a classification task; the graph neural network model comprises an encoder and a decoder. The system comprises a data acquisition module, a preprocessing module and a graph structure application module. By using the method and the device, high-quality graph representation can be generated under the condition of not depending on a large amount of label data, so that tasks of various graph structure data are better matched, and the accuracy of related tasks is improved. The method can be widely applied to the field of graph processing.

The invention discloses an automatic ICD coding method and system based on hierarchical feature optimization and prediction correction, and relates to the technical field of natural language processing, and the method comprises the steps: inputting a current electronic medical record text into a pre-trained automatic ICD coding model for coding, and obtaining a corresponding ICD code; the automatic ICD coding model comprises a text representation module, a coding representation module, a synonym fusion attention module, an initial prediction module and a prediction correction module which are connected in sequence; the text representation module processes input medical record text features by using a cascade convolutional network, the synonym fusion attention module introduces synonym fusion attention to enable ICD coding synonym description and electronic medical record text deep semantic representation to interact, and the prediction correction module based on a mask auto-encoder is further introduced. According to the method, the initial prediction result can be optimized and reordered, and the coding combination which most accords with the semantic and dependency relationship is selected, so that the prediction accuracy is improved, the coding efficiency is improved, and the automatic ICD coding problem is solved.

The invention discloses a program generation method and device combining evolutionary calculation and deep learning, equipment and a storage medium, and relates to the technical field of computers.The method comprises the steps that an initial population is constructed according to target input data, a deep learning model and a genetic programming algorithm; crossing a plurality of initial individuals in the initial population to obtain a to-be-evolved population; according to the target deep learning model, performing semantic mutation on to-be-mutated individuals in the to-be-evolved population, and constructing an iterative population until the iterative population meets an evolution termination condition; and determining a target individual according to the fitness value of each iteration individual in the iteration population so as to determine a target symbol program. Through the above mode, the powerful search capability of evolutionary computation and the generalization capability of a large-scale pre-trained neural network model are combined, under the coding task, the search efficiency is improved, the precision of the automatic programming task is ensured, and the symbol program corresponding to the automatic coding task can be efficiently and accurately generated.

The invention relates to a nuclear power process pipeline welding seam automatic modeling method and system, a storage medium and equipment. The method comprises the following steps: S1, constructing a weld joint part library; and S2, in the pipeline three-dimensional model, determining a corresponding weld joint part from the weld joint part library, and automatically generating a weld joint model. And S3, according to a preset automatic welding seam encoding method, the welding seam models are encoded in a unified mode. According to the method, on the basis of a parameterized weld joint part library and design rules, appropriate weld joint parts are selected from a pipeline three-dimensional model to automatically generate a weld joint model. The weld joint number is solidified in a naming mode, the requirement of digital transfer for a refined model is met, the unique weld joint number of the power plant is generated, the traceability of weld joint life cycle information is guaranteed, and the operation and maintenance efficiency and quality of the power plant are improved.

The invention discloses a hierarchical coding system-based intelligent recognition and language conversion method for traditional building-riding facade components, which realizes automatic coding and standardized description generation by constructing a six-level hierarchical coding system and training a special component recognition model, and lays a data foundation for evolution analysis of large-scale building-riding buildings. The method comprises the following steps: S1, constructing a six-level standard coding system; s2, collecting, preprocessing and labeling images, and constructing a labeling data set; s3, constructing a building-riding part identification model, and training and identifying the building-riding part identification model by using the existing data set and the annotation data set; s4, allocating coding information of a standard coding system for the recognition result of S3, extracting multi-dimensional features, performing component correlation analysis, and constructing a component feature database; s5, standard component description texts of three levels are automatically generated based on the steps S3 and 4; s6, constructing a structured knowledge graph of the apartment facade components based on the steps S3 to S5; and S7, integrating the processing capabilities in the steps S2 to S6 into a complete system.

The invention belongs to the technical field of financial science and technology, and discloses a multi-agent collaborative document-driven quantitative research and transaction self-evolution system and method. The system adopts a layered architecture, comprises a data and factor service layer, an execution engine layer, an agent layer, an interface service layer and a front-end control layer, and aims to solve the core problems that research and engineering implementation are separated in quantitative strategy research and development, code generation lacks interface constraints and strategies lack self-evolution ability. According to the system, through cooperative work of a research agent, a factor agent and a coding agent, a strategy idea described by a natural language is automatically converted into a standardized document, configuration and executable code; through a closed loop formed by self-monitoring, self-diagnosis and self-improvement intelligent agents, automatic diagnosis and optimization of problems of strategy overfitting, factor attenuation and the like are realized, continuous self-evolution of the strategy is driven, and full-life-cycle automatic management from strategy conception, factor design, automatic coding and real disk back-testing to feedback autonomous optimization is realized.

The invention discloses an automatic coding device and system. The automatic coding device comprises a first optocoupler module, a second optocoupler module, a third optocoupler module and a control module, the input end of the first optocoupler module serves as the input end of the device and is used for receiving an addressing signal. The output end of the first optocoupler module is connected with the control input end of the control module of the next-level automatic coding device; the control output end of the control module is connected with the input end of the second optocoupler module; the output end of the second optocoupler module serves as the device output end and is used for outputting an addressing signal to a next-level automatic coding device. And the output end of the third optocoupler module is connected with the input end of the first automatic coding device of the branch loop. The addressing signal is output to the next-level automatic coding device only after the equipment corresponding to the current device is detected to complete coding, and transmission loss caused by too long cable distance and transmission loss and spurious signal interference caused by long spurious signal interference are avoided.

The invention relates to the technical field of medical record automatic coding, and discloses a medical record automatic coding method comprising the following steps: S1, obtaining a to-be-coded medical record, and dividing the medical record into a medical record home page and other parts of medical records; s2, inputting the home page of the medical record and other parts of medical records into a preset coding model for processing to obtain standardized diagnosis information; and S3, querying a medical record coding result corresponding to the standardized diagnosis information in a preset mapping table, and taking the medical record coding result as a medical record code of the to-be-coded medical record. According to the automatic coding method for the medical records, full-information automatic coding of the medical records is achieved, meanwhile, the consistency, accuracy and efficiency of coding are greatly improved, and the error rate and the labor cost are effectively reduced. The invention further discloses a medical record automatic coding system.

The invention discloses an automatic coding method for realizing international disease classification, and the method comprises the steps: obtaining electronic medical record data from a medical database, and carrying out the preliminary cleaning of the data; extracting an electronic medical record text, associating the electronic medical record text with an ICD code diagnosed by a doctor, and constructing a training data set; a multivariate knowledge base is constructed, wherein the multivariate knowledge base comprises a UMLS synonym base, an unstructured knowledge base and an auxiliary knowledge base generated through LLM; constructing an ICD coding model which comprises a context coding module, an auxiliary knowledge screening module, a multiple attention interaction module and a fusion prediction module; a binary cross entropy loss function is adopted to optimize an ICD coding model, and automatic coding of international disease classification is achieved. According to the method, multi-level semantic interaction between the text and the code can be effectively captured, and the distinguishing capability and the coding precision of different levels of semantic information of the complex text are improved; the key information can be extracted more accurately, and the coding quality under complex cases and fuzzy diagnosis scenes is improved.

An autoencoding device for a synthesizable molecule generative model may include a memory configured to store chemical reaction data, chemical reaction learning information, and molecular structure information; and a processor configured to learn a neural network for a molecular generative model based on the chemical reaction data, the chemical reaction learning information, and the molecular structural information. The molecular generative model may include: a reaction sequence encoder configured to generate a latent space based on the chemical reaction learning information; a seed molecule encoder configured to generate an embedding space based on seed structural information for a seed molecule that is a final product of chemical reaction; and a decoder configured to output a reconstruction reaction sequence by decoding a reaction molecule predictive neural network and a reaction template prediction model generated from the latent space and the embedding space.

The invention provides an automatic coding method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining an initial code generated by a preset programming large model based on a programming prompt word; inputting the initial code into a preset compiling agent, so that the compiling agent performs compiling verification on the initial code and generates error information of the initial code; and the error information is input into the programming large model, so that the programming large model corrects the initial code based on the error information. According to the method, the codes generated by the large programming model can be automatically input into the compiling agent for compiling verification, errors existing in the codes are automatically found out through the compiling agent, error information is automatically input into the large programming model, the codes are automatically corrected through the large programming model, and the efficiency is improved. Developers do not need to manually find errors existing in the codes and manually input error information, and the code writing efficiency is improved.

Apparatus and methods to automatically create and implement update and migration workflows are provided. A digital twin auto-coding orchestrator may receive an updated version of a software program. The auto-coding orchestrator may gather infrastructure hardware data and version data. The auto-coding orchestrator may analyze the data. The auto-coding orchestrator may generate a workflow and code to update the older version to the updated version with the least amount of disruption and downtime. The auto-coding orchestrator may implement the workflow and update the software program to the updated version.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Collaborative training with compressed transmissions is performed by partitioning a plurality of layers of a neural network model into a device partition and a server partition, combining a plurality of encoding layers of an auto-encoder neural network with the device partition, wherein a largest encoding layer among the plurality of encoding layers is adjacent a layer of the device partition bordering the server partition, combining a plurality of decoding layers of the auto-encoder neural network with the server partition, wherein a largest decoding layer among the plurality of decoding layers is adjacent a layer of the server partition bordering the device partition, transmitting, to a computation device, the device partition combined with the plurality of encoding layers, and training, collaboratively with the computation device through a network, the neural network model.

The invention discloses a medical clinical data coding method based on a large language model and artificial code multiplexing. The medical clinical data coding method comprises the following steps: acquiring medical clinical original data to be coded and identifying a coding type; automatically generating a data cleaning dynamic template, and inputting a data cleaning instruction into the large language model to obtain cleaned data; calling a preset automatic coding rule base to execute automatic coding, wherein the automatic coding rule base comprises matching rules sorted according to priorities; if the automatic coding is not hit, entering a manual coding process and entering a synonym library for semantic association matching of subsequent automatic coding; after the medical coding dictionary is updated, a preset upgrading rule base is called to automatically process coded data, data to be upgraded are recognized, a coding upgrading suggestion is generated in combination with a retrieval enhancement generation technology, and data upgrading is achieved; and exporting a coding result. According to the invention, the efficiency and accuracy of medical clinical data coding can be improved, and manpower and time costs are reduced.

Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

The invention discloses an automatic ICD coding method which comprises the following steps: S1, processing a clinical text, extracting text information of each part in an EHR, and processing the text information into an input text; s2, generating ICD code options through pre-screening of a classification model; s3, predicting cue words based on the ICD code of multi-option verification, wherein the cue words comprise an instruction, an input text, options and an output template; s4, inputting the designed cue word into the LLM for reasoning, and outputting each token in sequence by the LLM through an autoregression mode and decoding the token into a text character; and S5, processing the free text output by the LLM, and outputting a standard ICD code. By adopting the method, the difficulty of an ICD coding task is simplified by adjusting a task form, and the advantages of a large language model in a medical long text reasoning task are fully played.

A source code translation system translates a source code snippet in an original source code programming language into distilled code that preserves the core semantics, logic flow, and data flow of the source code snippet in a language-independent format. The distilled code is then translated into a semantically-equivalent source code snippet in a different programming language by a decompiler. The decompiler is configured as a neural transformer model with attention pre-trained for translation using masked language modeling, denoising auto-encoding and multilingual program generation. The decompiler translates the distilled code into one of several different high-level programming languages.

The invention provides a battery device coding system, an automatic coding method and device thereof and an energy storage system, the coding system comprises a battery management system and at least one monitoring module, and each monitoring module comprises a battery monitoring unit and a monitoring sensor. The battery monitoring unit and the monitoring sensor are respectively connected with the battery management system through different buses; wherein the monitoring sensor is used for coding under the control of the battery management system. The anti-interference capability of the coding system can be improved, and the stability of the system is improved.

The application discloses a kind of management methods and systems to BIM model geometry data, according to the demand of construction stage, based on the geometry data of all components required, calculate axis-aligned bounding box, form spatial root node;Determine the mode of cutting to cut, generate spatial subnode;According to the centroid coordinates of each spatial node after cutting, according to the construction space code generated according to multistage space division rule;Geometric operation is carried out to component and spatial node, and cut component is obtained;According to the version number of cutting, identify incremental spatial subnode and cut component, respectively separate the non-geometric attribute information and geometry data of spatial subnode and cut component and store the graph library;According to construction space code, obtain the geometry data of required component from graph library.The flexible cutting of different construction stages and granularity requirements is adapted by multi-mode cutting framework;Multistage division and automatic coding provide standardized spatial identification;Digital-analog separation storage guarantees the efficient management of cutting data.