179 results about "Error location" patented technology

The invention relates to the technical field of drawing auditing, and discloses an artificial intelligence assistant for auditing a CAD drawing of a transformer substation. The artificial intelligence method for auditing the CAD drawing of the transformer substation comprises the following steps: obtaining a DWG drawing and an auditing specification PDF file through a uniform interface, analyzing a primitive by using a pyautocad or ezdxf library, extracting equipment information and a topological relation, constructing a position-based drawing knowledge graph, and storing the position-based drawing knowledge graph in a Neo4j database. And performing OCR and BERT vectorization on the PDF, and storing the PDF into an FAISS search library. A LangChain framework is utilized to integrate a knowledge graph and a text library to construct a mixed retriever, an optimization cue word is generated and input into a Deepseek large model for examination and reasoning, and finally, an error position is marked in AutoCAD with a red box. According to the method, the equipment information can be automatically extracted from the drawing, the topological relation of the equipment can be identified, and accurate and rapid auditing is performed in combination with related auditing rules, so that the substation drawing auditing efficiency and accuracy are remarkably improved.

The embodiment of the invention relates to the technical field of natural language processing, and provides a text error correction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-corrected target text, and carrying out the semantic boundary recognition, and obtaining all semantic boundaries; segmenting the target text into a plurality of clauses, distributing IDs according to the appearing sequence of each clause, and generating structured data with sequence IDs; inputting the structured data into a language model, and calling a target domain knowledge base corresponding to the target text as a constraint condition to perform semantic analysis, and identifying an error type; adjusting an error correction path based on the error type so as to re-identify error information, the error information including error content, the error type and the error position; and recombining a plurality of clauses based on the sequence ID, the error content, the error type and the error position, and generating an error correction result containing the error position, the error type and the correction suggestion. Therefore, collaborative detection and correction of grammar, semantic and logic errors are realized.

The invention relates to an intelligent error correction method and system based on a deep thinking dialogue large model, and the method comprises the steps: receiving a to-be-detected text, guiding a large language model to execute a content correction and verification task for the to-be-detected text through employing a pre-constructed multi-modular prompt instruction, and generating a preliminary processing result containing structured error information and correction suggestions; based on context information of a to-be-detected text, error positioning in the primary processing result is accurately calibrated, verification is carried out through three verification mechanisms of semantic verification, knowledge graph verification and historical verification, and a final processing result is generated and stored in a data storage unit; the final processing result is analyzed in real time in a data stream transmission mode, an analysis result is displayed on a user interface, and a corresponding position in the to-be-detected text is synchronously displayed in a highlight mode. According to the method, errors can be accurately positioned, model illusion is reduced, the correctness of terms and factual contents is ensured, and an efficient and reliable solution is provided for new media content auditing.

A method for quantum error correction includes: trapping and / or manipulating an array of qubits that include encoded neutral atoms or ions and forms a computational space; encoding the array of qubits to a state characterized in that an error during at least one of an idling, gate, or other operation results in a respective qubit transition to a disjointed state where that error can be detected without disturbing the qubits remaining in the computational space; performing a syndrome measurement of a quantum error correcting code; identifying the presence and location of an error by detecting the corresponding neutral atom or ion has left the computational space after performing the operation; identifying a logical error in the quantum error correcting code associated with a loss of qubit information based on the identified error location and results of the syndrome measurement; and adjusting subsequent computational steps based on the identified logical error.

According to the RS error correction decoding method and system compatible with the multiple two-dimensional codes, a finite field and module value corresponding table is defined, a corresponding relation table between power and data is constructed, and the decoding requirements of the multiple two-dimensional code types are met. The method comprises the following steps: acquiring image data by using an image sensor, extracting two-dimensional code data, and converting the two-dimensional code data into a data code word stream; and performing error detection and correction on the two-dimensional code data based on a preset power data corresponding table, including judging whether an error exists or not by calculating a syndrome, if an error is detected, calculating an error position polynomial by using a Berlekamp-Massey algorithm, then positioning an error position by using a Chien search algorithm, and finally calculating an error value by using a Forne algorithm and performing correction. According to the method, the decoding efficiency and the fault-tolerant capability of the two-dimensional code are remarkably improved, the method is suitable for various standards, and especially when the two-dimensional code is partially damaged or an error occurs in data transmission, the original coding information can be effectively recovered.

The invention discloses an English writing grammar error detection system based on deep learning. The system comprises an original data acquisition module, a data preliminary processing module, a grammar error positioning model construction module, a grammar error classification model construction module and a grammar error detection module. The invention relates to the technical field of text grammar error detection, in particular to an English writing grammar error detection system based on deep learning. A data preliminary processing method of text data trimming, text vectorization, data standardization and data set segmentation is adopted; a lightweight gradient elevator model is adopted as a grammar error positioning model, and the error positioning precision is improved through adaptive fusion of rule conflicts and statistical mode anomalies; a transformer model is adopted as a grammar error classification model, and classification accuracy is improved by modeling dependency relationships among words and introducing graph structure constraints.

The embodiment of the invention discloses a method for intelligently assisting in reviewing a water conservation scheme of a water and soil conservation production and construction project, and the method achieves the intelligent feature matching of a new scheme and similar cases through the construction of a tagged knowledge base integrating historical cases of multiple industries, so as to recognize the generality compliance. A double-engine review mechanism is adopted, a form review module automatically checks document structure integrity and format specifications, and a logic review module executes cross-chapter data consistency check and business logic compliance analysis and highlights and positions error positions in a document. And recording decision logic in real time to form a feedback closed loop. And dynamically optimizing case library matching weight and recognition rules based on high-frequency error statistics and expert correction records. The modes of positioning review key points to chapters and sections, describing specific requirements of each review key point in a language, adding a check formula among data to review content needing to be calculated, determining a check type to which each review key point belongs, and the like are adopted, and the model is trained more clearly, so that the model has relatively professional review ability.

The invention provides an automatic workflow code generation method driven by a large model, which comprises the following steps: sequentially converting an original demand into a subtask set, a node relation graph and structured workflow description data through multi-stage agent cooperation, and forcibly checking a data closed loop between nodes by the last-stage agent; tool nodes and content generation nodes are defined in the workflow description data, and decoupling of content generation and tool calling is achieved; analyzing the structured workflow description data, dynamically generating a strong type state class mapped with the description data, and automatically filling a node calling logic and a parallel routing rule based on a preset code template to generate a workflow code; when the code runs wrongly and the number of retry times exceeds a preset threshold value, an execution track is generated through the error positioning agent, candidate schemes are generated in parallel through the multiple repairing agents, and after self-adaptive scoring is conducted through the scoring agent based on the error type, the repairing schemes are verified and executed in sequence.

The invention discloses a data error correction method and device based on low-density parity check, equipment and a medium, and relates to the technical field of data error correction, and the method comprises the steps: reading data to be corrected from a data memory, and carrying out the pre-verification of the data to be corrected and a verification matrix of a preset low-density parity check code, and obtaining a pre-verification result; based on the pre-verification result, determining a first error position set in a code word position set; performing bit flipping operation on code words indicated by the first error position set to complete pre-error correction; and inputting the code word after pre-error correction processing into a low density parity check decoder for decoding, outputting data after error correction, pre-detecting the error position of the code word, and performing bit flipping on the code word at the error position, thereby reducing the number of error bits entering LDPC decoding, and improving the decoding efficiency. Therefore, the number of error bits does not exceed the decoding capability limit of hard decoding, the probability that the system enters soft decoding is reduced, and the decoding throughput rate of the system is ensured.

The invention provides a large language model JSON output repairing method and system based on ANTLR grammar analysis, and belongs to the technical field of model repairing. The method comprises the following steps: preprocessing an original output text of a model to obtain a to-be-repaired JSON text; constructing a fault-tolerant grammar rule, and generating a JSON grammar parser according to the fault-tolerant grammar rule; a JSON text to be repaired is input into an analyzer, grammar analysis is carried out in stages by adopting a progressive grammar matching strategy, and error node position coordinates and error types of grammar analysis anomalies are positioned by analyzing a context tracking mechanism; according to the position coordinates of the error nodes and the error types, performing context-aware repair on the text by adopting a hierarchical repair strategy to obtain a repaired JSON (JavaScript Object Notation) text; and performing multi-layer verification on the repaired JSON text by using a multi-layer verification mechanism, and outputting the repaired JSON text after the multi-layer verification is passed. According to the method, the error classification accuracy and the error positioning precision in the JSON output repairing process are improved, and then the repairing success rate of large language model JSON output is improved.

The invention provides a user-defined script dynamic editing and executing method and system of a Unity engine. The method comprises the following steps that codes are input and preprocessed; executing text cleaning, word segmentation and annotation preprocessing according to the line feed as a code line set; grammatical analysis and semantic understanding; constructing an abstract syntax tree, identifying a syntax structure, and combining context analysis semantics to generate a symbol table; processing errors in real time; positioning an error line through a multi-stage matching strategy, highlighting an error position through a visual interface, and generating a repair suggestion in combination with a line number mapping table, error propagation analysis and a multi-evidence fusion mechanism; executing dynamic codes; explaining and executing the corrected script, and feeding back an execution result; through interaction with a Unity engine, operation control of a script on a game object and a scene is realized; through three core technologies of semantic weight dynamic matching, an anti-interference navigation state machine and a hybrid sorting algorithm, the long-standing problems of low matching precision, poor interaction experience and the like in the field of code completion are solved.

The invention relates to a data verification method, a data verification device and a storage medium. The method comprises the following steps: firstly, through semantic analysis, establishing a mapping relationship between a first position corresponding to first text data in an editable format in a to-be-verified first document and a second position of second text data in a target format in a second document, so that an original to-be-verified text and a to-be-verified text after format conversion form a position corresponding relationship; then, verifying the second document through semantic analysis, and taking second text data which does not pass verification in the second document as annotation data; and finally, based on the mapping relationship, determining the labeling position of the labeling data in the first document, and generating an editable error prompt at the labeling position. Therefore, the time for manually searching the error position of the original document is shortened, and the verification and correction efficiency is improved, so that the data verification efficiency is improved.

The embodiment of the invention provides a purchase file verification method and device, a medium and a product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring an electronic purchase file subjected to format standardization preprocessing; comparing the electronic purchase file with a preset early warning knowledge base to obtain a comparison result; under the condition that the comparison result shows that the electronic purchase file passes verification, atomic knowledge points of the electronic purchase file are obtained by using a preset hybrid neural network model, and a verification report is generated based on the atomic knowledge points; the verification report includes at least one of a pass item, an error location, or a correction suggestion. According to the scheme of the invention, the format of the electronic purchase file is unified according to the electronic purchase file subjected to standardized preprocessing, and the defects of a traditional contract generation technology are overcome by using the dual verification of the preset early warning knowledge base and the hybrid neural network model.

The present invention relates to communication system and method for error-proportional energy-saving in encoding and decoding in Body Area Networks (BAN) using Human Body Communication (HBC). The method includes receiving message signal from data sources. Further, the method includes encoding data symbols using encoding algorithms. Furthermore, the method includes calculating syndrome values for the encoded data symbols. Furthermore, the method includes generating an error signal when the calculated syndrome values corresponds to a non-zero value. Furthermore, the method includes receiving the error signal from the syndrome module. Furthermore, the method includes activating an Error Location Polynomial (ELP) module when the syndrome values corresponds to a non-zero value. Subsequently, the method includes generating an error indication signal. Consequently, the method includes activating an Error Magnitude Polynomial (EMP) solver module and a chien search module. Finally, the method includes decoding the encoded data symbols.

A memory device includes a syndrome calculator configured to generate an error location signal based on first data and an error correction code; an error corrector configured to generate second data by correcting an error in the first data according to the error location signal; and a data mask (DM) calculation circuit configured to generate a DM signal according to logic high bits of the first data and change a logic level of the DM signal according to at least one of an increase signal and a decrease signal, by activating the increase signal when an error bit is detected in logic low bits of the first data and activating the decrease signal when the error bit is detected in the logic high bits of the first data, based on the error location signal.

To prevent degradation of service to users by analyzing reading errors of an absolute positioning system that detects absolute positions.SOLUTION: An elevator system includes an APS tape on which absolute position information is continuously set corresponding to the position of the car in the direction of movement within the elevator shaft, an APS sensor installed on the car and equipped with two cameras that read absolute position information corresponding to the car's position, and an information processing device that processes the absolute position information read by the APS sensor. The two cameras are positioned so that the reading positions of the absolute position information are separated by an offset distance in the direction of movement. The information processing device includes a recording processing unit that records error information including the error position when a reading error occurs in each of the two cameras, and a compilation processing unit that compiles compilation data that associates the error position with the number of errors based on the recorded error information.SELECTED DRAWING: Figure 8

The invention discloses a dynamic error correction system and method for an OTP (One Time Programmable) memory, a computer and a storage medium, and the system comprises a cross-clock synchronization module which is used for synchronizing asynchronous data of an OTP interface to a system clock domain and generating a control signal according to a data format; the double-buffer-area module is used for alternately writing data and reading a control signal by utilizing two independent buffer areas, and correcting and outputting the written data; the CRC decoder module is used for carrying out error detection on the synchronous data; the error position searching module is used for positioning an error position candidate item of each channel; the error position matching module is used for finding out a uniquely matched error position; and the read control module is used for judging whether data reading needs to be paused or not and starting error correction operation, and sending a control signal to the double-buffer module. According to the invention, a modular architecture design is taken as a core, and a complete processing flow from data receiving, error detection, error positioning to error correction output is constructed.

A memory control circuit stores codewords in a memory module configured to limit errors to one wire of a memory interface. A codeword includes a first block with a first data portion and a second block with a second data portion. An error correction code symbol in the second block is used to locate and correct errors in the second block. Some bits of the first block are repurposed as metadata. The remaining bits are used as parity bits for detecting errors in the first block. The second block is merged with the first block before being stored to memory and demerged from the first block in a memory read operation. Demerging causes errors in the first block to create errors in a corresponding location in the second block. The location of errors found in the second block is used to locate and correct parity errors in the first block.

The present invention provides a novel storage device. A test bit generation unit has the function of generating a test sequence from an information sequence. A storage unit has the function of storing, in a flip-flop circuit, a codeword that is constituted by the information sequence and the test sequence, and outputting the codeword as a received word. An error detection correction unit has the function of outputting an error vector which indicates the position of an error in the received word and a decoded word in which the error in the received word has been corrected. The storage unit has the function of storing the decoded word in the flip-flop circuit in accordance with the error vector, the function of writing the decoded word to a holding circuit, and the function of writing back, to the flip-flop circuit, the decoded word written in the holding circuit. The flip-flop circuit has a first transistor. The holding circuit has a second transistor. The off current of the second transistor is smaller than the off current of the first transistor.

The present invention discloses an error correction method and system for FPGA, which relates to the technical field of data encoding and decoding, and includes: generating Galois field elements based on the received symbol vector and the primitive polynomial, and calculating the syndrome polynomial through 8-symbol parallel processing; calculating the coefficients of the error location polynomial through the BM iterative algorithm based on the syndrome polynomial; calculating the error location by parallel substituting 10 Galois field elements through the Chien search method based on the error location polynomial; establishing the evaluation polynomial and the error pattern calculation formula, and performing polynomial division calculation through the derivative of the error location polynomial to obtain the error pattern; performing error code judgment based on the error location and the error pattern, and correcting the error symbol through the XOR operation in the Galois field to obtain the updated symbol vector. The present application adopts a parallel pipeline design structure, which enables the FPGA to automatically and real-time correct the received error module data without retransmission, effectively improving the real-time performance and reliability of error correction.

The invention discloses a weak supervision semantic segmentation method based on category representation comparison and aggregation, and the method comprises the steps: firstly carrying out the image enhancement preprocessing of N images containing C target categories; secondly, introducing a category characterization concept, constructing a category characterization extraction module, obtaining category characterization based on the preprocessed image, constructing a category characterization aggregation module, aggregating the category characterization, and obtaining category characterization aggregation features; then, carrying out category characterization on the aggregated features, and generating a pseudo tag and a category score through normalized global weighted pooling; and finally, constructing a segmentation model, and outputting a semantic segmentation result based on the pseudo tag. According to the method, the error activation of the error position is effectively inhibited, the generation quality of the seed region is effectively improved, and the semantic segmentation correctness is further improved.

The invention belongs to the technical field of artificial intelligence, and relates to an image encryption method, which comprises the following steps: acquiring a to-be-encrypted original image; preprocessing the original image based on a preprocessing module to obtain a state matrix corresponding to the original image; performing dynamic key generation processing on the state matrix based on the target zero neural network to obtain an original key; performing random enhancement processing on the original key based on a random security enhancement module to obtain a target key; performing encryption processing on the original image based on the target key to obtain an encrypted image corresponding to the original image; and carrying out output processing on the encrypted image. The invention further provides an image encryption device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the error positioning information can be stored in a block chain. The image encryption method and device can be applied to business scenes of image encryption in the financial field and the medical field, and dynamic adaptability and safety of the image encryption process are effectively ensured by combining multiple function modules.

The invention provides a front-end application program error positioning method, which comprises the following steps of: analyzing a program code in a code warehouse of a front-end application program, and determining a characteristic graph of each interface component related to the program code and a style inheritance graph of each interface component; receiving a page screenshot corresponding to the front-end application program; identifying an abnormal interface component in the page screenshot, and extracting structure information and abnormal information corresponding to the abnormal interface component; and positioning an error program code segment corresponding to the abnormal interface component from the program code according to the structure information and the abnormal information corresponding to the abnormal interface component, the characteristic atlas of each interface component and the style inheritance atlas of each interface component. Through the method, the error program code segment in the front-end application program can be positioned based on the page screenshot containing the abnormal interface component. The invention further provides a front-end application error positioning device, electronic equipment, a computer readable storage medium and a computer program product.

The invention discloses a flash memory error correction method and system based on adaptive learning, and relates to the technical field of data storage.The flash memory error correction method comprises the steps that flash memory voltage distribution and error code positions are collected in real time, and dynamic feature vectors are generated in combination with environmental parameters; constructing a hypergraph neural network based on the dynamic feature vectors, obtaining error propagation risk scores of the storage areas, and generating a priority list; based on the priority list, the check connectivity of the low-density parity check matrix is adjusted through matrix reconstruction, and a sparse check matrix is formed; an initialization parameter and a solution vector are generated based on the decoder ready state signal, sampling iteration and threshold judgment are carried out, and a decoding result is output; and when decoding fails, performing multi-device parameter updating by adopting aggregation optimization, and performing decoding again to output final correction data. According to the method, collaborative error correction is optimized through multi-device parameter aggregation, so that the limitation of single-device decoding is solved, the global error correction performance is optimized, and the fault-tolerant capability is enhanced.

The invention provides an automatic software test method and system, a storage medium and equipment, and the method comprises the steps: obtaining a target code, extracting related information and test requirements, and generating a test case according to the target code and the test requirements through a trained large model; in response to the code change information, evaluating the influence range of code change on the test case, performing static analysis, determining the change of a code structure, performing dynamic analysis, and determining the change of a code execution path; according to the evaluation result of the influence range of the code change on the test case, determining a test case automatic adjustment strategy, running the adjusted test case, obtaining log data in the running process, extracting error information in the log data, performing code mapping on the error information, and determining an error position. According to the method, a large model technology is utilized, and the processes of automatic generation, intelligent adjustment, automatic result analysis, accurate problem positioning and the like of the test case are optimized, so that the efficiency and the accuracy of software testing are remarkably improved.

The invention discloses a decoding method, a decoding device, a chip and a storage medium. The decoding method comprises the steps that according to a receiving vector, a corresponding first generalized syndrome is constructed, the receiving vector is a result obtained after a code word is subjected to error interference, and the first generalized syndrome comprises the receiving vector, a first polynomial and parameters of a check matrix corresponding to a code; obtaining an incidence relation between the receiving vector and the first generalized syndrome; calculating a first generalized syndrome based on the association relationship to obtain a calculation result of the first generalized syndrome; based on the calculation result of the first generalized syndrome, error information in the receiving vector is solved, and an error position and an error value are obtained; and correcting the receiving vector based on the error position and the error value and obtaining a code word. According to the decoding method, the complexity of calculating the generalized syndromes can be reduced, and unified decoding of various types of codes is realized.

Receiving a codeword of a forward error correction (FEC) code, each codeword in the FEC code having a minimum distance of at least seven to any other codeword in the FEC code, calculating multi-bit syndromes by applying columns of a check matrix to the received codeword using combinatorial logic, and responsively determining the received codeword comprises at least three errors, determining coefficients of an error locator polynomial using a coefficient combinatorial logic circuit by combining the multi-bit syndromes using non-iterative combinatorial logic performing arithmetic operations in a finite field F(2 n ), determining respective error positions for each error of the at least three errors by determining roots of the error locator polynomial using a non-iterative root-finding combinatorial logic circuit, the non-iterative root-finding combinatorial logic circuit performing arithmetic operations in the finite field F(2 n ), and correcting each of the at least three errors in the received codeword.

A memory device includes a memory cell array and an error correction code (ECC) circuit. The ECC circuit, which is configured to correct an error in a data code read out from the memory cell array, includes: (i) a syndrome calculating unit configured to operate a plurality of syndromes based on the data code and an H-matrix, (ii) an error location detecting unit configured to generate an error vector based on the plurality of syndromes, and (iii) an error correcting unit configured to correct an error within the data code based on the error vector, and output corrected data.

A method may include generating a state signal indicating a state of error location polynomial (ELP) determination by an ELP determination circuit; and control the ELP determination by the ELP determination circuit in response, at least in part, to a value of the status signal.