358 results about "Computer data processing" patented technology

The invention provides a park environment risk traceability and grading early warning management and control method, and belongs to the technical field of risk early warning based on computer data processing. The method comprises the following steps: firstly, collecting video monitoring, environment and equipment state sensor and real-time meteorological heterogeneous data in a whole domain and a peripheral specific buffer zone of a park, and preprocessing the data to obtain standardized multi-source data; dividing grid units by taking a park boundary coordinate as a reference and distributing a unique standardized code, associating standardized multi-source data to a corresponding grid according to a rule, and performing fusion calculation on a grid-level risk characteristic value of each grid; then combining real-time meteorological data, tracking a risk diffusion path through a reverse derivation algorithm, positioning a risk source position and a core range, and outputting related parameters; and finally, grid risk levels are divided according to the grid risk characteristic values and a preset threshold interval, a corresponding early warning mechanism is triggered, and early warning information is output. According to the invention, the precision of environmental risk positioning, the reliability of traceability analysis and the refinement degree of an early warning result are improved.

The invention relates to the technical field of computer data processing, and discloses a cross-border e-commerce compliance tax refund method and an intelligent declaration system based on block chain evidence storage, and the method comprises the steps: constructing a reference library containing commodity physical attributes and fair density intervals; order transaction, logistics actual measurement and fund time sequence data are acquired; executing multi-dimensional cross validation based on the reference library, wherein the multi-dimensional cross validation comprises physical space logic verification based on geometric boxing simulation, attribute consistency verification based on density distribution matching and quality and time sequence joint verification; a risk level is determined by integrating results; and generating digital fingerprints for compliance data, writing the digital fingerprints into a distributed account book, and generating a declaration file by using an on-chain index. According to the invention, trade authenticity is verified through physical simulation and multi-dimensional logic, evidence is ensured not to be tampered in combination with a block chain technology, and compliance and auditing efficiency of tax refund declaration are effectively improved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention provides a multi-stage electronic medical record generation method and system based on a structured thinking chain, and relates to the technical field of computer data processing, and the method comprises the steps: generating at least one medical record draft intermediate state based on input original medical data through a structured thinking chain processing flow, the medical record draft intermediate state comprises at least one specific paper to be verified; starting a double-path knowledge calibration mechanism, and executing calibration on the intermediate state of the medical record draft, the calibration comprising: a forward knowledge enhancement path: in response to a specific disguise, retrieving associated evidence related to the specific disguise from one or more authoritative knowledge bases; and a backward fact checking path: utilizing a small reasoning model to carry out consistency judgment on the specific discourse and the associated evidence; and correcting the intermediate state of the medical record draft based on the result of the consistency judgment so as to generate a calibrated electronic medical record. According to the method, the fact accuracy and credibility are improved, and the structural integrity and logic self-consistency are ensured.

The invention relates to the technical field of computer data processing and network security, in particular to an illegal character automatic cleaning system, which comprises a rule compiling module for monitoring rule change, performing semantic fusion and topological mapping on a rule set, constructing a deterministic finite automaton and mapping the deterministic finite automaton into a state transition table; the state switching module is used for constructing a double-buffer context and operating lock-free switching through an atomic pointer to realize hot updating; the speculation execution module is used for carrying out vectorization pre-scanning based on a state transition table by utilizing single-instruction multi-data stream parallel loading, identifying a walk path, falling into a safe state for releasing and falling into a trap state for triggering external verification; the self-adaptive feedback module is used for counting trap state triggering frequency, generating a rule allergy report and dynamically adjusting the size of a read fragment; according to the method, the contradiction between rule flexibility and execution efficiency is solved, and high-performance cleaning based on speculative execution is realized.

The invention relates to the technical field of computer data processing and mode recognition, and discloses an intelligent handwriting record analysis system supporting translation process traceability and capability assessment, which comprises the following steps: collecting multi-dimensional handwriting data generated when a user translates by using an intelligent pen; the Hidden Markov model decodes the handwriting into translation behavior primitive sequences such as smooth writing, hesitant pause and the like; automatically dividing semantic regions such as a main translation region and a draft region by a density clustering algorithm; performing space-time fusion attribution to generate a space-time attribution log for accurately recording behaviors, time and positions; and establishing a multi-dimensional capability index system containing translation efficiency, quality and strategies based on the log, and carrying out quantitative calculation and generating a visual user capability portrait. According to the method, the invisible translation cognition process is converted into traceable and quantifiable objective data, deep insight and comprehensive evaluation of the translation process are achieved, and a scientific and efficient analysis tool is provided for translation teaching and interpreter ability evaluation.

This invention provides a comprehensive digital and intelligent operation and maintenance management method and system for power distribution networks, relating to the field of computer data processing technology. The method includes: S1, obtaining pole and tower information collected by drones during line inspections, performing defect mining and analysis on the pole and tower information, and integrating the analyzed defect information with the pole and tower information to form a pole and tower information collection table; S2, selecting and extracting corresponding data from the pole and tower information collection table, and visualizing the data on a GIS map containing the lines obtained from the pole and tower information collection table using preset markers to generate an inspection status map; S3, statistically classifying the analyzed defect information and matching it through a big data platform to generate a defect repair and management summary table; S4, analyzing the faults using an AI model and a big data platform, combining the inspection status map and the defect repair and management summary table, obtaining priority-ranked repair tasks, and proposing corresponding solutions.

The invention relates to the technical field of computer data processing, and discloses a multi-modal data labeling method based on a large model technology, and the method comprises the steps: 1, carrying out the data collection of multi-modal data, and carrying out the data preprocessing of the collected multi-modal data; 2, single-mode feature extraction is carried out on each kind of single-mode data in the multi-mode data, cross-mode feature fusion is carried out, and a fusion feature vector capable of being co-processed is generated; 3, performing model training according to the fusion feature vector to obtain a joint training model, and labeling the multi-modal data based on the joint training model to obtain a preliminary labeling result; and 4, carrying out manual intervention and data storage on the preliminary labeling result, and outputting a labeling result. By means of the scheme, accurate and efficient annotation of various types of data such as texts, images, voices and videos can be achieved, and deep application and development of multi-modal data in the field of artificial intelligence are promoted.

The invention relates to the field of computer data processing and artificial intelligence, and discloses a mobile phone APP intelligent code scanning and AI automatic identification system which comprises a mobile terminal, an internet network and a local server. The mobile terminal is provided with an acquisition detection module, and a mobile phone end code scanning module automatically controls light supplement when the image brightness is lower than a threshold value. A server-side AI intelligent recognition module executes Gaussian filtering, binarization and geometric correction preprocessing on a code scanning image, and a text is extracted by using a deep convolutional neural network and a bidirectional long-short-term memory network. And the system further executes semantic analysis by traversing the business template library, judges the attribution of key fields by using a spatial Euclidean distance, and generates structured JSON data. The statistical module calculates a real-time production efficiency index, and the remote viewing module associates and displays a process optimization suggestion when the data is abnormal. According to the invention, high-precision identification, business semantic understanding and intelligent decision support of code scanning data are realized.

The invention belongs to the technical field of computer data processing and data analysis and prediction, and discloses a user behavior data analysis and prediction method based on big data and machine learning, and the method comprises the following steps: collecting multi-channel user behavior data; performing data preprocessing on the acquired data; extracting data features from the original data; selecting a corresponding algorithm model according to a prediction target and data characteristics; according to different algorithm models, adopting corresponding evaluation indexes to comprehensively evaluate the model performance; and inputting new user behavior data into the trained model, and predicting future behaviors of the user. According to the user behavior data analysis and prediction method based on the big data and the machine learning, deep mining and analysis are performed on the user behavior data by applying a big data technology and a machine learning algorithm, and future behaviors of the user are predicted; the method is applied to many scenes needing to accurately grasp user behaviors, such as Internet services, marketing, financial risk control and the like.

The invention relates to the technical field of computer data processing and artificial intelligence, in particular to a spatial intelligent data expression design and definition mode highly suitable for a large model, comprising: receiving multi-modal observation data as input; a probability deduction mechanism based on a generative large model; defining a probabilistic feature for each of the atomized spatial entities; constructing a geometric shape of the atomized space entity by adopting a semantic primitive; and generating a spatial entity objectification expression model containing the probabilistic features and the semantic primitives. According to the method, grid patches with high lexical element consumption are abandoned, and the geometric morphology is constructed by adopting the semantic primitives, so that the spatial data can be understood, generated and stored by a large model at extremely low lexical element cost.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.

The invention discloses a server fault diagnosis system based on service topology correlation analysis, and relates to the technical field of computer data processing and automatic fault localization, and the system comprises a service topology construction module which is used for collecting multi-source heterogeneous data to construct infrastructure, runtime environment and business performance feature vectors, a service dependence topological graph is maintained; the fault storm diagnosis module is used for identifying a fault storm by calculating node state weights and utilizing spatial compactness and a time synchronization coefficient, and positioning a root cause node based on reverse breadth-first search and a fault propagation index; and the fusion alarm generation module is used for calculating the response time contribution degree of the root cause node to verify the causal relationship so as to distinguish dominant storm and hidden faults. The system executes cascade alarm convergence for dominant storm, triggers snapshot capture during deep operation for hidden faults, locates hidden root causes under complex topology, and realizes automatic fault closed-loop processing.

The invention discloses a multi-port hybrid charging pile power pool reinforcement learning scheduling method and system, and relates to the technical field of computer data processing. The reinforcement learning scheduling method and system for the multi-port hybrid charging pile power pool comprises the following steps: S1, periodically collecting port scheduling data, and preprocessing the port scheduling data; s2, constructing and training a vehicle target SOC prediction model, outputting a vehicle target SOC, evaluating charging urgency of each accessed vehicle, and dividing scheduling priority levels; s3, constructing a port power distribution model, outputting a power distribution value of each charging port, and issuing the power distribution value to a power control interface for execution; and S4, evaluating power scheduling execution deviation, and entering a protection mode when evaluation is abnormal. The problems that the total power of the charging pile cannot be flexibly scheduled due to fixed-proportion power distribution and topological isolation of alternating-current and direct-current ports, high-power resources are occupied by high-electric-quantity vehicles, and charging of low-electric-quantity vehicles is limited are solved.

The invention discloses a script generation method and device, equipment, a storage medium and a product, which are applied to the technical field of computer data processing, and the method comprises the following steps: obtaining business demand information corresponding to a target business function; semantic analysis is conducted on the service demand information, a template configuration file is generated, and the template configuration file at least comprises service parameters needed for achieving the target service function, attribute information of the service parameters and logic rules corresponding to the service parameters; analyzing the logic rule to generate an abstract syntax tree corresponding to the target service function; and performing data integration on the service parameters, the attribute information of the service parameters and the abstract syntax tree to generate an executable script for realizing a target service function. According to the scheme, manual code writing and manual template configuration are not needed, and the script generation efficiency is improved.

The invention relates to the technical field of computer data processing, and discloses an immigrant archive multi-dimensional information integration and intelligent query method, which comprises the following steps: collecting and standardizing multi-source heterogeneous immigrant archive data to generate to-be-processed data; based on the to-be-processed data, constructing an initial event knowledge graph containing element nodes, event nodes and relationships; performing causal relationship discovery and correction on the initial event knowledge graph, and performing mining, confidence updating and logic completion on the causal relationship between events by fusing data statistical analysis and a business process normal form learned from historical data to generate a time sequence causal graph; and receiving a natural language query request of a user, and executing query and reasoning on the time sequence causal atlas. According to the method, the business process logic hidden behind the data can be revealed, intelligent questions and answers for complex problems such as event reasons and influences are supported, and the intelligent level of query and the interpretability of results are improved.

The invention provides a power equipment operation data anomaly detection method, and belongs to the technical field of power operation detection based on computer data processing. The method comprises the following steps: collecting multi-source data in an operation state of power equipment, preprocessing the multi-source data, inputting a designed depth feature extraction module based on multi-physical field signal characteristic analysis, and designing a depth feature extraction network oriented to different signal characteristics in the module, abnormal fragments can be automatically focused from an original waveform, a spatio-temporal evolution law can be excavated from a sensor network in spatial distribution, and sparse discharge mode elements with clear physical significance can be decoupled from partial discharge pulses; and carrying out weighted splicing on the extracted features to form global state representation, inputting the global state representation into the trained hybrid supervised variational auto-encoder, and outputting a category probability and an equipment state label. According to the method, deep fusion of panoramic state perception and multi-source information of the equipment is realized, the deep representation and identification capability of a complex abnormal mode is improved, and dynamic self-adaptive abnormal judgment and fault preliminary screening diagnosis are realized.

The invention discloses an industrial multi-source data fusion method based on causal inference in the technical field of computer data processing, and the method is applied to an aero-engine, and comprises the steps: obtaining the original performance data of the aero-engine, carrying out the normalization processing of the original performance data, and obtaining a target variable, the original performance data comprises rotating speed, exhaust temperature, oil pressure, oil temperature and metal abrasive particle component data; constructing a causal inference algorithm to calculate a dynamic threshold value; the causal intensity of each variable pair is calculated from the candidate causal set, variables with the first M ranks of the causal intensity are selected to form an optimization condition set, and a test algorithm is constructed; constructing a symmetric causal intensity matrix; and on the basis of the target variable and the feature matrix, performing quadrature to obtain a new data set after dimension reduction. By constructing the causal inference algorithm, the interpretability and generalization ability of the model can be enhanced, the performance stability is improved, the application range in the industrial field is expanded, and the calculation burden is relieved.

The invention discloses a voice real-time question and answer processing method based on a large model and a domain knowledge base, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring a voice input stream of a user, carrying out intelligent sound wave deconstruction on the voice input stream, generating a text stream, and carrying out dependency syntactic analysis and semantic role labeling; generating intention information and key information, dynamically accessing a domain knowledge base, executing predictive loading, generating a preloaded data subset, performing fusion processing by combining the intention information, the key information and the preloaded data subset, generating a fusion result, performing accuracy verification and correction, generating a natural language answer, and converting the natural language answer into voice output. The technical scheme of combining knowledge predictive loading driven by intention analysis, context fusion and answer traceability correction is adopted, and low-delay response, high-precision intention understanding and high-factuality answer of voice questions and answers can be achieved.

The invention relates to the technical field of computer data processing, and discloses an intelligent exhibition hall multi-modal interaction digital human system and an implementation method, which are used for solving the problem that multi-source data of multi-modal interaction lacks a unified clock and verifiable timestamp alignment mechanism in a traditional method. According to the method, a unified time domain and a time version are established on an edge side, terminal access is restrained, and an acquisition time mark, an access time mark and a serial number are written in data or a state; performing gating shunting according to a time version, performing de-duplication and out-of-order rearrangement based on a serial number and double time marks, and generating and solidifying a session window evidence index; locking a transaction window boundary according to the evidence index, generating a participation source list and a transaction number, establishing a fragment reference relationship and generating an alignment voucher; in the linkage stage, phase division issuing is carried out according to a preparation phase, an execution phase and a confirmation phase, backward reading verification is carried out according to an action sequence number, a transaction log is archived, and alignment, rechecking and playback of an interaction link are achieved.

The invention provides a multi-mode interactive data real-time fusion planning execution system, and belongs to the technical field of computer data processing. According to the method, a dynamic scene calculation graph is constructed and updated in real time, split multi-modal sensing data is integrated into a structured semantic framework, conversion from passive data processing to active scene understanding is achieved, relevance and potential importance of all elements in a scene are calculated in advance before a user instruction is sent, and therefore the user experience is improved. According to the method, intention anchoring can be achieved when an instruction is issued, cognition and response delay of a system is greatly shortened, interaction instantaneity is improved, a traditional linear task planning normal form is subverted by providing a task generation and elastic execution mechanism based on graph reasoning, and a task sequence containing alternative nodes is generated, so that the task planning efficiency is improved. And the execution unit directly subscribes to the state change of the scene graph, so that rapid link switching of a bottom layer is realized without depending on complete re-planning of an upper layer.

The present application relates to the technical field of computer data processing, and especially relates to an intelligent agent system for automatically identifying, downloading and installing self-controllable peripherals, which comprises a device information acquisition module for collecting data to generate hardware identification data; a driver compatibility determination module for comparing a pre-stored self-controllable peripheral list to match a driver resource to generate a resource identification; a secure downloading module for establishing an encrypted connection to download a driver file, storing the driver file in an isolated sandbox after hash verification and digital certificate verification; a driver migration module for simulating a system call environment through an operating system compatibility layer in the driver migration module, and converting a driver instruction into a host system instruction by using a user-level binary translator in the driver migration module; and an automatic installation module for calling a system interface to deploy the driver and verifying an installation state. The present application realizes closed-loop automatic processing from identification to installation of a peripheral driver, and solves the problem of compatibility between a Linux system and a Windows driver.

The invention discloses a method for dynamically putting a questionnaire in a third-party application through an SDK, and belongs to the technical field of computer data process.The method comprises the steps that six types of data are collected and subjected to multi-factor weighted decision making, a multi-platform exclusive integration scheme is provided, a questionnaire display and no-disturbing strategy is configured, the display effect is intelligently triggered and optimized, and answer data are verified in real time. Differentiated submission and multi-mode synchronous high-efficiency circulation are realized. According to the method for dynamically putting the questionnaire in the third-party application through the SDK, the compatibility and flexibility of questionnaire putting are improved through the multi-dimensional factor weighting decision model, and the method is suitable for digital operation scenes such as user feedback collection and demand investigation of various third-party applications.

The invention provides a homomorphic encryption privacy computing system and method supporting post-tracing and auditing, belongs to the technical field of computer data processing, and is used for deeply coupling generation of an encrypted auditing trace and a homomorphic encryption computing process and carrying out distributed random storage and chained verification. According to the method, the calculation chain based on the chain pointer is constructed, and the unique decryption and verification authority of the user side is combined, so that the integrity of the data processing process is guaranteed, and a user can obtain a credible calculation result and can verify the continuity and consistency of the whole calculation chain.

This invention relates to the field of computer data processing, and particularly to an efficient replication method, system, device, medium, and program product for incremental logs in an LSM storage engine. The method is characterized by: receiving a data replication request from a slave node; determining a first position in the WAL log data and the current write position of the WAL log data, wherein the first position is the position where the last scan of the WAL log data ended; scanning the WAL log data from the first position to the current write position; when it is determined that new WAL log data has been added, scanning the new WAL log data and repeating this process a predetermined number of times until no more new WAL log data is found; storing the data at a second position, which is the position where the scan ends when no more new WAL log data is found; and sending the WAL log data to the slave node. This invention can ensure data consistency while effectively reducing latency.

The invention discloses a virtual space-time environment automatic completion method and system based on data density dynamic perception and a medium, and relates to the field of computer data processing. The method comprises the following steps: monitoring a data activeness index (such as spatio-temporal information entropy) of a spatio-temporal grid in real time in a distributed index database of a virtual spatio-temporal environment; when the index meets a preset data sparseness condition, generating a completion request; analyzing the space-time attribute tag of the target grid and acquiring associated multi-modal reference data from an external heterogeneous data source; carrying out feature recombination on the reference data by utilizing a generative neural network model, and generating discrete spatio-temporal data slices containing spatio-temporal anchor points and scene content attributes (such as environment illumination and material mapping); and finally, injecting the slices into the database to fill data holes. According to the method, a passive trigger mechanism and a structured reconstruction technology of unstructured data are introduced, so that the computing power cost of a large-scale virtual environment is effectively reduced, the problems of data sparseness and cold start of a long-tail region are solved, and the historical authenticity and time-space consistency of a virtual scene are ensured.

The invention provides a data processing method, and relates to the technical field of computer data processing and flow control, and the method comprises the steps: receiving a resource processing request which comprises to-be-processed target resource quantity information; in response to a splitting instruction for the resource processing request, segmenting the resource processing request into at least two sub-requests according to a preset splitting verification rule; wherein the splitting verification rule is used for ensuring that the sum of the resource quantities corresponding to the sub-requests after splitting is not greater than the to-be-processed quantity in the target resource quantity; executing a resource binding operation on the sub-requests passing the verification or the unsegmented original requests; and operating an event monitoring task based on a preset trigger rule, and generating an event notification when the condition of the trigger rule is met by scanning the state attribute and the time attribute of each request.

The application relates to the field of computer data processing and discloses a traffic automatic playback method for software testing, which comprises the following steps: obtaining software traffic data and obtaining data packets and time intervals in the software traffic data; according to interval values and time sequence distribution relationships of the time intervals, a first category is obtained through multi-threshold segmentation, a third category is obtained through threshold segmentation of the first category, and all intervals except the third category in a time interval sequence are classified into a fourth category; a first compression rate of each interval in the fourth category is obtained, and a second compression rate of each interval in the third category is obtained; a correction coefficient is obtained according to the number of elements of the first category to which each interval in the fourth category belongs and packet values of adjacent data packets, and a third compression rate is obtained; and the traffic data is compressed and automatically played back according to the obtained compression rates. The application aims to solve the problems that the existing equal compression of time intervals between data packets leads to packet loss and large differences between traffic data before and after compression.

This invention relates to the fields of computer data processing and network communication technology, specifically to a method and system for real-time synchronization of paperless interactive information across multiple terminals. The method includes: a logic construction step: acquiring the original interactive command stream, constructing a semantic causal logic tree based on a vector clock, and dividing strong barriers and weak streaming events; a backpressure monitoring step: establishing a rendering backpressure model to calculate the rendering backpressure value of the target terminal; a dynamic shaping step: responding to a backpressure value exceeding a threshold, reducing the dimensionality of the command stream and reorganizing its timing, attaching weak streaming events to strong barrier events and merging them into an atomic state snapshot for transmission; and a consistency verification step: comparing the screen content-aware hash value, and if a mismatch occurs, performing full state synchronization. This invention solves the problem of visual semantic confusion caused by differences in rendering capabilities between heterogeneous terminals.

The invention relates to the technical field of computer data processing, and provides a soil classification prediction method and system based on environment and remote sensing multi-source data. The method comprises the following steps: constructing a soil characteristic data set containing spectral characteristics, physicochemical characteristics and environmental characteristics; calculating the contribution degree of each feature to soil classification and the correlation degree between the features, generating an initial weight configuration, updating a weight coefficient according to the contribution degree of the spectral feature and / or the correlation degree between the physicochemical feature and the environmental feature, generating an adaptive weight configuration, and based on the adaptive weight configuration, carrying out weighted fusion on a feature subset of which the contribution degree exceeds a contribution degree threshold value, so as to obtain an adaptive weight configuration; generating a soil classification feature vector, constructing a soil classification decision tree based on the soil classification feature vector, and determining an optimal classification path; and predicting a classification result of the to-be-classified soil sample based on the optimal classification path, if the confidence of the classification result does not meet a confidence threshold condition, updating the weight coefficient, and returning to the step of generating the adaptive weight configuration until the confidence threshold condition is met, thereby obtaining a final classification result.