103 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 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 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 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 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.

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.

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 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.

An approach of the present disclosure includes an algorithm to encode an instruction set of a domain-specific processor automatically in an optimal way, and which yields area-effective hardware. Instruction operands and opcodes can be encoded separately or at the same time. Instructions are not encoded one by one but can be encoded in groups, and instructions are grouped automatically. The decoding logic of control signals for each group of instructions, such as register read enable and write enable, is fed to a logic minimizer to obtain an encoding constraint matrix, and the encoding of each instruction in each instruction group is done using an optimal state assignment method.

The invention belongs to the technical field of identification of surfaces of special material products, and particularly relates to a system for identifying metal products. The system comprises an automatic product coding module, a laser code scanning recognition module, an automatic transfer module and an automatic identification module, the product automatic coding module is used for automatically coding a product surface code, converting the product surface code into a program command which can be executed by the product automatic identification device, and inputting the collected coding information into the laser code scanning identification module; the laser code scanning recognition module is used for recognizing coding information output by the product automatic coding module and feeding back the coding information to the automatic transfer module; the automatic transfer module is used for transferring the metal products to the automatic identification module and the product automatic packaging module in sequence from a feeding port; and the automatic identification module is used for performing automatic cold extrusion forming on the surface of the metal product. The working environment of personnel is solved, and the traceability of products and the working efficiency are improved.

The application discloses a kind of for clinical diagnosis text automatic ICD-10 coding text matching model's construction method, device and application, comprising: first, by the coding history text is processed into priority matching set, with the coding result of coding history of previous years Analogy reasoning the coding result of unencoded clinical diagnosis text;Then, realize a kind of based on interactive text matching mode, in matching process, each character in two texts is interactively matched, uses the matching strategy of element-by-element subtraction and element-by-element multiplication through a neural network, retains more original information, obtains better matching effect, in aggregation process, using convolutional neural network, using the convolution kernel with same weight is merged feature to retain more original information, further improve the matching effect;Finally, data that cannot be matched with the coding history of previous years still can be secondarily matched with ICD-10 disease standard name, ensure the comprehensiveness of matching.

The present application relates to devices and components including apparatus, systems, and methods to utilize auto-encoding for channel state information feedback.

In a method for real-time opportunity discovery for productivity enhancement in a production process, a processor extracts a set of features from time series data using autoencoding of a neural network based on uncontrolled variables in the time series data. The processor identifies one or more operating patterns based on the extracted features, wherein the features include a dimensionality reduction of a representation learned from the time series data. The processor identifies a neighborhood of a current operating state based on the extracted features. The processor compares the current operating state with historical operating states based on the time series data in the same operating pattern. The processor uses the neighborhood to discover operating opportunities based on the comparison of the current operating state with the historical operating states. The processor identifies controlled variables in the same pattern that are correlated with the current operating state.

The application relates to the field of artificial intelligence and discloses a text connotation quality evaluation method for improving the accuracy of text connotation quality evaluation and improving model training efficiency. The text connotation quality evaluation method comprises the following steps: obtaining an initial text from a preset medical record text; processing the initial text through a natural language processing algorithm to obtain a target text; performing text coding on the target text through a preset bag-of-words model and a preset automatic coding model to obtain first text features; performing feature extraction on the target text to obtain second text features, wherein the second text features comprise text complexity features, text grammar style features and medical semantic features; performing evaluation processing on the first text features and the second text features through a trained logistic regression model to obtain an evaluation result, wherein the evaluation result is used for identifying the connotation quality grade of the preset medical record text. The application also relates to the blockchain technology, and the target text is stored in the blockchain.

A method, apparatus, main controller, and storage medium for automatic jacquard encoding are disclosed, belonging to the field of textile technology. The method includes the main controller acquiring the Nth communication address of the Nth jacquard and sending a communication address query message for the (N+1)th jacquard to the Nth jacquard via an optical signal communicator; the Nth jacquard receiving the communication address query message for the (N+1)th jacquard and sending a communication address setting signal to the (N+1)th jacquard; the (N+1)th jacquard receiving the communication address setting signal sent by the Nth jacquard, setting the currently queried address value as the (N+1)th communication address of the (N+1)th jacquard, and responding to the main controller's communication address query message with the (N+1)th communication address as the communication address status of the (N+1)th jacquard, thus completing the automatic jacquard encoding. This solves the problem that using a mechanical dialer for jacquards requires manual dialing and is prone to setting errors.

The application relates to a network congestion link diagnosis method based on an adversarial automatic encoder and belongs to the technical field of network communication, which comprises the following steps: generating a data set containing link states and path states according to the congestion prior probability of a link; feeding the data set into an automatic encoder, superimposing a generative adversarial network on the encoder or the decoder of the automatic encoder, inputting the path states or the link states into the encoder, and reconstructing the link states or the path states; training the generative adversarial network, enabling the generative adversarial network to learn the congestion prior probability information of the link, and outputting a link state solution with the maximum congestion posterior probability; and reversely propagating the reconstruction loss and the generative adversarial loss, and iteratively updating network parameters. The application applies the adversarial automatic encoder, learns the unique mapping relationship from the link to the path through the link states and the path states, does not need to measure the network topology, and meanwhile, the adversarial automatic encoder is data-driven, has strong adaptability and has certain attack resistance.

The invention relates to the technical field of data processing, in particular to an automatic coding agent control system and method for a multi-task scene, and the system comprises a data collection module, a coding processing module, a quality evaluation module, a strategy optimization module, an execution control module, a defect tracing module, a parameter correction module and an iterative optimization module. The quality evaluation module is used for calculating the quality deviation index of the core quality index of each process based on the unified feature vector; and the strategy optimization module is used for fusing the multi-source information to optimize the process parameter regulation and control strategy of each process. According to the method, the limitation of traditional single-process coding is broken, cross-process data representation distortion is effectively avoided, and the coding accuracy and data link integrity of multi-process linkage control are remarkably improved; data coding, quality correlation and parameter iteration are integrated, the problems that a traditional system is insufficient in universality and lacks a closed loop are effectively solved, multi-process quality accurate control is achieved in an auxiliary manufacturing scene, and the production stability and the product consistency are improved.

The invention relates to a hydropower station KKS automatic coding method based on an artificial intelligence large model, and is suitable for the field of water conservancy and hydropower engineering. The method comprises the following steps: acquiring multi-source data of each hydropower station component from a hydropower station BIM model, wherein the multi-source data comprises geometric parameters, attribute data and unstructured function description of the component; carrying out standardization processing on geometric parameters and attribute data of the hydropower station components, extracting keywords from unstructured function description based on a domain dictionary, and forming multi-source features of the hydropower station components; the multi-source features of the hydropower station components are input into the fine-tuned large language model, and the large language model is combined with the KKS coding knowledge graph to output KKS codes of the hydropower station components; according to the invention, based on the multi-source data of the hydropower station component, KKS automatic coding is realized through combination of the large language model and the KKS coding knowledge graph, and the coding efficiency and accuracy are improved.