45 results about "Autocode" patented technology

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.

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 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.

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.

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 application discloses an intelligent network connection terminal multi-attack intrusion detection method based on OTA signature update, first injects multi-type combined attacks into an ICTs platform, then performs signature update based on OTA upgrade, next designs a CNN IDS based on automatic coding design, and finally realizes classification of attack modes after training. The application improves attack detection success rate and reduces calculation cost.

The application provides a knowledge extraction method and system based on prompt learning, embedding obtained unstructured text data to obtain a cache vector value; embedding a homogeneous prompt string to obtain a homogeneous prompt symbol vector value, and embedding a heterogeneous prompt character string to obtain a heterogeneous prompt character vector value; splicing the cache vector value, the homogeneous prompt symbol vector value and the heterogeneous prompt character vector value to obtain a spliced vector, taking the spliced vector as a cache vector value of a pre-training language model; adopting a regular matching method to obtain structured data from text data generated by the pre-training language model; the application uses automatically encoded prompt characters, automatically learns a potential semantic representation of a label, and solves a knowledge extraction problem in a general way through a generative large-scale pre-training language model, thereby improving the precision and efficiency of knowledge extraction.

The invention discloses a method and a device for realizing user-defined data directory coding based on online configuration. The method comprises the following steps: configuring a directory dictionary to define business attribute fields and enumeration code values required by coding, and supporting manual input or interface import; a coding rule is dynamically constructed through a visual interface and is formed by flexibly splicing a plurality of code segments such as business fields, sorting codes, dates, serial numbers and the like; setting a header, a screening condition and operation item display of the directory list on line; the coding rule is bound to the whole process of auditing, publishing, portal displaying, changing and offline of the resource catalog, and automatic coding generation is achieved; when the rule is changed, historical codes are reserved to ensure the continuity of stock data, and a new rule is only applied to a newly added instance. Zero code management is realized through template online configuration, multi-version parallel and multi-scene adaptation are supported, the customization cost is remarkably reduced, and the flexibility, readability and retrieval efficiency of a data directory are improved.

The present application relates to the technical field of data processing, and especially to an automatic coding intelligent agent control system and method for multi-task scenarios, comprising a data acquisition module, a coding processing module, a quality evaluation module, a strategy optimization module, an execution control module, a defect traceability module, a parameter correction module and an iterative optimization module; the quality evaluation module is used for calculating the quality deviation index of each process core quality index based on a unified feature vector; the strategy optimization module is used for optimizing each process parameter regulation strategy by fusing multi-source information. The present application breaks the limitation of traditional single process coding, effectively avoids cross-process data representation distortion, significantly improves the coding accuracy and data link integrity of multi-process linkage control; has the functions of integrating data coding, quality correlation and parameter iteration, effectively solves the problems of insufficient generalization and closed loop loss of traditional systems, and assists in realizing multi-process quality accurate control in the manufacturing scene, improving production stability and product consistency.

A wireless device (10) measures Channel State Information, CSI, data for a wireless channel between the wireless device (10) and a node (100) of the wireless communication network. Further, the wireless device encodes the CSI data by an encoder part of an machine-learning based autoencoder. Further, the wireless device (10) applies a mask to the encoded CSI data. The mask defines unmasked data elements and masked elements of the encoded CSI data. The wireless device (10) then transmits the encoded CSI data excluding the masked data elements to the node (100).

The invention relates to the technical field of automatic coding, in particular to a one-object-one-code system-oriented unique code automatic coding method and device, and the method comprises the steps: determining a matched character space according to a unique code character demand parameter and an actual application parameter required by a user, screening out a matched target sub-character space, and carrying out the automatic coding of a unique code. Carrying out cyclic coding operation on current space parameters, parameters of all characters used for constructing a character space and unique code character demand parameters to obtain a character cyclic coding result, obtaining time, an actual application scene and actual use time in combination with corresponding characters, and analyzing to obtain a unique code required by a user; the uniqueness of the unique code is guaranteed from the source, the coding length is reduced, the density of the formed unique code is reduced, and the definition of the jet-printed unique code is guaranteed, so that the scanning accuracy and efficiency of the unique code are improved, and the safety management and control of full-link traceability (traceability) of articles / products based on the jet-printed unique code are improved.

This disclosure provides a task scheduling method, a task scheduling device, and an automatic coding system, relating to the field of computer technology, and particularly to the field of artificial intelligence technology. The method includes: retrieving a target event from at least one type of event queue using a distributed lock mechanism, wherein the target event is retrieved by one of multiple scheduler instances; determining a subtask of the code development task corresponding to the target event; selecting an executor instance from multiple executor instances, and assigning the subtask corresponding to the target event to the selected executor instance. This scheme improves the efficiency and reliability of the automatic coding system.

The application discloses a disease classification ICD automatic coding method and device based on contrast learning, and belongs to the field of natural language processing and artificial intelligence. The technical problem to be solved by the application is how to use natural language processing technology to match corresponding diagnosis coding, i.e., disease classification coding, for patient electronic medical record texts, so as to reduce the workload of medical personnel, improve the automatic coding matching accuracy, and improve the matching speed. The technical scheme adopted is as follows: ① a disease classification ICD automatic coding method based on contrast learning, which comprises the following steps: S1, constructing an ICD automatic coding model training data set; S2, constructing an ICD automatic coding model; and S3, training the ICD automatic coding model. ② an ICD automatic coding device based on contrast learning, which comprises an ICD automatic coding model training data set construction unit, an ICD automatic coding model construction unit and an ICD automatic coding model training unit.

The invention discloses a multi-task Bayesian optimization self-adaptive knowledge migration method, which comprises the following steps of: capturing a non-linear relationship among data sets by utilizing a kernel automatic coding mechanism so as to measure the similarity among tasks; the priority of auxiliary task selection is dynamically adjusted through a heuristic rule, selective cooperation between tasks is ensured, and interference is reduced. Bayesian optimization modeling and black box function optimization are used to reduce the number of times of task real evaluation. According to the method, an explicit knowledge migration mechanism is introduced, the nonlinear mapping matrix is used for converting the optimal solution of the auxiliary task, efficient knowledge migration and collaborative optimization among the multiple related tasks are achieved, and the optimization efficiency and the resource utilization rate are remarkably improved.

The embodiment of the invention provides a ship base unique ID automatic coding method and system for back-end delivery. The method comprises the steps that an assembly tree, model attributes and arrangement information of an equipment model are read; extracting related information of the base, and combining the related information according to a set rule to generate a unique ID of the base; and writing the unique ID into the corresponding base model. A structure tree and arrangement information of an equipment model are automatically read through a program, information such as a project model, a system code, a cabin number and a three-dimensional coordinate of a base is extracted, and after the information is combined according to a certain rule, unique ID coding of the base is achieved, and the manufacturing requirement of a back-end factory is met.

Various embodiments of the present disclosure provide machine learning architectures for improving predictive functionality of a computer. The techniques apply a multi-stage machine learning automated coding pipeline to a coding domain to generate a code prediction for a text segment. During a first stage, the techniques may include inputting a text segment from a file to a machine learning encoder to generate a text segment vector and extracting a subset of searching codes from a vector data store based on a comparison between the text segment vector and a plurality of code vectors within the vector data store. During a second stage, the techniques may include generating a generative model prompt based on the subset of searching codes and inputting the generative model prompt to a generative model to generate a code prediction for the text segment.

The application discloses a movie recommendation method based on triple auto-encoder combined with a knowledge graph, which comprises the following steps: 1) encoding the comment information between the user and the movie into an emotional classification as the input of an automatic encoder; 2) merging the rating of the movie, auxiliary information and generated comment representation into a semi-automatic encoder for reconstructing the output, learning the low-dimensional feature representation of the extended information through the semi-automatic encoder, fusing the obtained low-dimensional feature representation into the original feature space of the movie, and inputting the new feature as additional information into the semi-automatic encoder model; 3) designing a serial connection of the semi-automatic encoder and the automatic encoder, and comparing the output prediction score matrix of the third auto-encoder with the original score matrix. The application can expand the features of the movie information by using the interaction information between the user and the movie and the knowledge graph, and process the expanded features through the auto-encoder, so that the more accurate recommendation for the user is achieved.

The invention belongs to the technical field of data transmission, and particularly discloses a weak network connection-oriented adaptive video transmission method, device and equipment. According to the method and the device, the sending prediction frame is reconstructed based on the semantic decoding unit of the sending end, and when the current video frame meets the scene dynamic switching requirement, the residual error of the sending prediction frame and the sending original frame is coded; a target output video frame is generated in conjunction with the intra-coded bitstream and residual coding. Through the above mode, the coding and decoding unit based on the automatic coding structure performs coding and decoding to ensure that the semantic decoding unit maintains relatively high reconstruction fidelity in a weak network narrow bandwidth scene, and when the scene dynamic switching requirement is determined to be satisfied according to the semantic features, the residual error coding and decoding channel is controlled to start and stop, so that the scene dynamic switching efficiency is improved. According to the invention, adaptive adjustment of algorithm calculation amount and bandwidth occupancy rate is realized, and video frame output is carried out in combination with intra-frame coding bit stream and residual coding, so that the code rate of video transmission in a weak network narrow bandwidth scene can be effectively reduced, and the fidelity can be improved.

The application discloses a cultural constraint rule coding and conflict graph solving method, system and device for intelligent protection of human settlement heritage community and a storage medium, and relates to the technical field of digital heritage protection and rule reasoning. The method comprises the following steps: obtaining multi-source heterogeneous data, performing standardization pretreatment, constructing an environmental state vector to uniformly represent the community protection state, extracting entities and relationships from a regulation text through natural language processing to construct a cultural knowledge graph, converting constraint clauses into machine executable condition-action constraint expressions coded in an abstract syntax tree to form a rule base, constructing a rule driven decision model for multiple subjects, performing legality clipping on candidate actions to generate a legal action space, generating a joint action combination and constructing a conflict graph, finally forming a target protection scheme in the form of a space unit level control instruction set, and performing incremental updating according to execution feedback to form a closed loop decision. The application realizes automatic coding and execution of cultural constraints and computable collaborative solving of multiple subjects.

This invention presents an image retrieval method based on the fusion of attention enhancement and autoencoding. It uses an improved ResNet50 network to extract global and local feature maps, obtains global and local feature descriptors based on these maps, calculates image similarity, and derives the target image through similarity comparison. This image retrieval method improves upon traditional residual blocks by employing automaton encoding, effectively unifying local and global features into a single network. It uses an attention mechanism to extract regions of greater interest, avoiding algorithmic overhead and resulting in faster retrieval speed and higher accuracy.

The invention relates to an industrial measurement point automatic coding method and system, a medium and equipment. The method comprises the following steps: acquiring a target field in a to-be-identified document; analyzing and identifying the target field to obtain a measurement point object and a measurement feature; performing automatic coding based on the measurement point object and the measurement characteristic to obtain a measurement point code; and storing the measurement point codes in a database for an external system to call. According to the method, the required measurement points can be automatically identified from the document, the measurement point codes are obtained through automatic coding and stored in the database, so that an external system establishes basic historical data based on equipment, the problems existing in manual measurement point extraction in the prior art are solved, and data base construction in digital development can be met.

The invention discloses an evidence perception long context refining method based on reinforcement learning. The method comprises the following steps: firstly, constructing a hierarchy-evidence matching matrix to obtain a state space, designing a discrete action space to operate an original medical record, and generating a refined context; step-by-step reasoning decision making is carried out according to a refining context and a coding decision big model, and a final diagnosis code is obtained; combining information extraction quality, format compliance and path accuracy to construct a composite reward and punishment function, and performing quantitative scoring on the refining behavior and the coding result; and finally, designing an asynchronous cooperative training mechanism based on double-layer optimization, finely adjusting the coding decision large model, and updating the refining module by adopting an actor-commentator framework. The method is suitable for a long medical record text ICD automatic coding task, and by designing a dynamic evidence perception and cooperative training mechanism, the precision of model long context processing and decision coding is remarkably improved.

This invention discloses an automatic coding and attribution analysis method for qualitative interview data. Its key feature is the adoption of a multi-stage collaborative framework driven by both coding and attribution, integrating code generation, relation extraction, and attribution analysis into a dynamically collaborative, data-driven closed-loop system. This system achieves automatic coding and attribution analysis of qualitative interview data, specifically including steps such as data acquisition and segmentation, candidate code generation, semi-supervised coding, dual-track output, category aggregation, relation extraction and network construction, core category identification, attribution analysis illusion suppression, and human-machine feedback loop. Compared with existing technologies, this invention improves analysis efficiency, coding consistency, and research credibility, effectively solving problems such as low efficiency of manual coding and the inability to backtrack manual corrections. It can complement or replace existing computer-aided qualitative data analysis software, providing a new generation of intelligent analysis solutions for the field of qualitative research and showing promising application prospects.