165 results about "Data dependent" patented technology

The invention discloses a data model dual-drive motor life prediction system and method. The system comprises an information acquisition module, a model fusion module, a model training module and a weight adjustment module. The information acquisition module is used for acquiring state data of different motors; the model fusion module is used for effectively fusing the data driving model and the physical information model; the model training module is used for training and optimizing the fused mechanism-data dual-drive model according to the motor state data provided by the information acquisition module; the weight adjusting module plays a key adjusting role in the whole model training process. The method not only depends on a data-driven model for prediction, but also combines a physical information model to make up for the problem that a pure data-driven method lacks interpretability. Multi-physical-field factors such as environmental stress, vibration impact, thermal management and an electromagnetic field are brought into modeling, so that the performance degradation of the motor is accurately described and evaluated, and the accuracy and physical interpretability of a prediction result are enhanced.

Aspects of the disclosure provide for an accelerator capable of accelerating data dependent, irregular, and / or memory-bound operations. An accelerator as described herein includes a programmable engine for efficiently executing computations on-chip that are dynamic, irregular, and / or memory-bound, in conjunction with a co-processor configured to accelerate operations that are predictable in computational load and behavior on the co-processor during design and fabrication.

The invention discloses a data processing method and device, a storage medium and electronic equipment. The method comprises the steps that a dependency collection plug-in is created under a construction node in a project object model file of a target application, the project object model file is analyzed through the dependency collection plug-in, initial dependency data of the target application is determined, and the target application is set to be deployed on at least two platforms with different platform identifiers; monitoring different stages in the construction process of the target application, and determining change dependency data of the target application; processing the initial dependency data and the change dependency data into target dependency data, and uploading the target dependency data to a target database; and in response to the target query message, querying and displaying the target dependency data from the target database. The technical problem of low query efficiency depending on data is solved.

The invention discloses a data consanguinity acquisition method and device based on a large language model, a storage medium and a processor. According to the scheme, firstly, an SQL statement to be subjected to blood relationship analysis is obtained, and the SQL statement serves as input of a large language model; extracting metadata of a table involved in the SQL statement by utilizing the large language model; and finally, the large language model outputs a blood relationship analysis result containing the SQL statement according to the SQL statement and the metadata of the table involved in the SQL statement and the constructed cue word. Compared with the prior art that the data blood relationship information is obtained by manually analyzing SQL statements, the method has the advantages that the accuracy of data blood relationship analysis is improved by means of a large language model, professional knowledge of database experts is not relied on any more, a data blood relationship analysis function is achieved without writing complex codes, and the overall input cost is reduced. Compared with an analysis scheme in the prior art, the method has obvious advantages.

The invention discloses a test method, device and equipment and a computer readable storage medium, which are applied to the technical field of computers, and comprise the following steps: obtaining multi-source data of a distributed storage cluster, the multi-source data comprising hardware state data, service associated operation data and business dependence data; constructing a relation dependency graph among the hardware nodes, the service nodes and the service nodes based on the multi-source data; adjusting the chaos test script according to the real-time state of the distributed storage cluster and the dynamic adjustment strategy to obtain a target chaos test script; and executing the target chaos test script, determining a fault range and a recovery mechanism based on the relation dependency graph, and outputting a test report. According to the method, comprehensive and accurate presentation of the dependency relationship of each node is realized, and the chaos test script can adapt to the dynamic change of the cluster, so that the test scene is more practical, the invalid test is avoided, the effectiveness, reliability and efficiency of the cluster chaos test are remarkably improved, and the high availability and service continuity of the cluster are powerfully guaranteed.

The invention relates to the technical field of big data and the field of financial science and technology, and discloses a data migration method, which comprises the following steps: acquiring to-be-migrated data, and filling the to-be-migrated data into a data file; obtaining an SQL statement template containing placeholders, and analyzing and identifying the placeholders in the template; establishing a mapping relationship between the placeholders and fields in the data file; combining the SQL grammar in the SQL statement template with the data in the data file to generate a complete SQL statement; adding an output position in the data file, and displaying the generated SQL statement; and copying the SQL statement to a database processing module for execution, and completing data migration and updating. According to the method, the SQL statement is automatically generated, and the to-be-migrated data is combined with the SQL statement template, so that the workload of manually writing the SQL statement is reduced, the error risk in the operation is reduced, the dependence on the file import permission of the database is not needed, the data migration process is simplified, and the data migration efficiency and accuracy are improved; and meanwhile, the consistency and the integrity of the data are ensured.

The invention relates to data analysis, in particular to an intelligent dependency graph construction method based on multi-dimensional analysis and self-adaptive optimization, which comprises the following steps: reading module data, and carrying out preliminary analysis on a dependency relationship among modules to construct a preliminary dependency graph; performing multi-dimensional analysis on a dependency relationship between each pair of modules in the preliminary dependency graph, and calculating a comprehensive score of each pair of modules according to a multi-dimensional analysis result; adjusting the preliminary dependency graph according to the comprehensive score of each pair of modules to obtain an adjusted dependency graph; performing cyclic dependency detection on the adjustment dependency graph, and merging static dependency data and dynamic dependency data of each module; self-adaptive optimization is carried out according to the complexity of the adjusted dependency graph, a final dependency graph is generated, and the final dependency graph is stored and output; according to the technical scheme provided by the invention, the defect that the software module dependency graph is difficult to accurately and efficiently analyze and construct in the prior art can be effectively overcome.

Technology for data dependent control of a delay between a data strobe signal and data signals. The delay may be adjusted to compensate for data dependent impacts on propagation delays in an unmatched data in path and data strobe path. The delay between the data strobe signal and data signals may be adjusted based on a number of toggles in data to be sent to a memory die.

The invention relates to the technical field of vulnerability detection, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses a dependency processing method, device, equipment and medium for a multi-component project, which comprises the following steps of: analyzing description files of different component types in a multi-language project, extracting dependency data to generate unified dependency description; the method comprises the following steps: constructing a panoramic dependency graph containing component nodes and dependency edges, identifying version constraints of the component nodes to generate a unified constraint model, determining a compatible version combination by processing the constraint model, extracting a multi-component operation environment configured with a tool during installation and operation of environment configuration data, and installing project dependency according to the compatible version combination. And scanning the panoramic dependency graph based on the vulnerability information source to generate a repair instruction for updating the vulnerability component version. According to the method, centralized analysis and collaborative management of multi-language dependency are realized through unified dependency description and constraint models, and conflicts are reduced and the consistency and security of the system are improved in combination with environment configuration and a loophole repair closed-loop process.

The present storage solution provides an order of operations of a computer-implemented method that includes implementing a primary-First principle with a first data Op received by the primary storage site being executed on the primary storage site and then replicated to the secondary storage site and a second data Op received by the secondary storage site being first replicated to the primary storage site. The method further includes acquiring overlap write manager (OWM) lock locally on the primary storage site for the first data Op if there are no conflicting ops that are already inflight working on an overlapping range, sending the first data Op to a file system of the primary storage site to modify the file system as per primary-first principle, and suspending any new Ops from the primary storage site that have an overlapping range that overlaps with a range of the first data Op.

The invention discloses a multi-objective optimization method based on a Black-Litterman model for an ore grinding grading process, a multi-objective optimization device based on the Black-Litterman model for the ore grinding grading process, multi-objective optimization equipment and a computer readable storage medium. According to the method, the experience viewpoint of a process expert and historical data are organically fused by adopting the Black-Litterman model; the limitation that a traditional optimization method only depends on data driving or only depends on expert experience is overcome, and collaborative complementation of subjective knowledge and objective data is achieved. The deep learning technology is introduced to extract time series data features, and the convolutional neural network is combined to fuse expert viewpoints, so that the optimization model can fully excavate complex dynamic characteristics and inherent rules in the ore grinding classification process. Practical experience is fused into a prediction framework, so that the result is more suitable for practical operation, and the accuracy of ore grinding classification multi-objective optimization is improved.

The invention discloses a method for automatically identifying unsaturated fatty acid and double bond positions thereof. According to the invention, the principle of regular fragmentation of unsaturated fatty acid in a collision dissociation mode of a liquid chromatography-tandem mass spectrometer after the unsaturated fatty acid is subjected to a Pattern-Buchi (PB) reaction is utilized; the invention develops an automatic data processing method for unsaturated fatty acid mass spectrum data acquired in two modes of a data dependent acquisition mode and a data non-dependent acquisition mode in liquid chromatography-tandem mass spectrometry, and can automatically identify unsaturated fatty acid and double bond positions thereof from huge DDA data of liquid chromatography-tandem mass spectrometry. And the quantitative analysis of the unsaturated fatty acid is realized by utilizing the strength information of the characteristic fragment ions in the DIA data, so that the accuracy and the efficiency of the qualitative and quantitative analysis of the unsaturated fatty acid are remarkably improved.

The embodiment of the invention discloses a workflow deployment method and device, a readable medium, equipment and a program product. The method comprises the steps that a workflow design interface comprising a workflow editing component and a workflow deployment component is displayed; in response to an editing operation on the workflow editing component, obtaining a selected reference node to construct a to-be-deployed workflow; in response to a trigger operation on the workflow deployment component, obtaining execution performance data of the reference node in the process of executing the data processing task, and optimizing the to-be-deployed workflow based on the execution performance data corresponding to the reference node to obtain a target workflow; obtaining a dependent data packet required by operation of a target node in the target workflow, and generating an operation data packet corresponding to the target workflow based on the dependent data packet corresponding to the target node; and creating a container instance corresponding to the target workflow based on the operation data packet to deploy the target workflow. By adopting the embodiment of the invention, the deployment efficiency of the workflow can be improved.

The invention discloses a monomolecular structure design and generation method based on a large language model, and relates to the crossing field of monomolecular electronics and artificial intelligence. In order to solve the problems of low single molecule design efficiency and insufficient data, SingMolT5 is obtained through field fine tuning on the basis of MolT5, and generation from a natural language to a molecular SMILES is realized. According to the method, data are collected from literatures in the single molecule field and an existing disclosed molecule library, a fine tuning data set containing 329 high-quality instructions is constructed, and the fine tuning data set comprises three basic data types including a molecular skeleton, an anchoring group and molecules. The model is initialized by a MolT5 check point, and a cross entropy loss function is used in the fine tuning process; reasoning, generating and using a beam search strategy; the follow-up evaluation indexes comprise BLEU, Levenshtein, MACCS, RDK, Morga and effectiveness. The method provides an innovative technical path for'low experience and data dependent type single molecule design 'in the field of single molecule electronics, and is particularly suitable for aided design of a single molecule device core function unit and a single molecule sensing probe molecule, namely, a target molecular structure can be quickly converted through a natural language, the design threshold of researchers is reduced, and the design efficiency is improved. And a traceable technical basis can be provided for screening and iteration of a monomolecular structure based on a high-quality data set and rigorous evaluation logic.

The invention relates to the technical field of power equipment early warning, and discloses a thermal power generating unit fault intelligent early warning method based on artificial intelligence, which comprises the following steps: step 1, constructing a transient working condition data base, performing time synchronization processing based on multi-modal online monitoring data, intercepting time sequence data by taking a unit dynamic event as a reference, and performing data processing on the basis of the time sequence data; and finally carrying out denoising and normalization processing on the intercepted data, and extracting features capable of representing transient behaviors to obtain normal operation data. And step 2, based on the constructed data base. According to the technical scheme, the physical information neural network is adopted to calculate the physical consistency residual error, and the physical consistency residual error and the data-driven abnormal score are subjected to double verification, so that the technical effect of high-fidelity estimation and detection of weak transient abnormal signals is achieved, and compared with a scheme purely depending on a data-driven model in the prior art, the method has the advantages that the reliability is high; the defects that the sensitivity to early weak faults is insufficient and the missing report rate is high under strong background noise are overcome.

The invention discloses an SQL generation method, system and device based on a large language model and a medium, and relates to the technical field of business data processing, the method comprises the following steps: obtaining a query log of a to-be-processed business, and determining a plurality of SQL templates and a corresponding associated statement formed by each SQL template; determining N target first association statements from all the first association statements according to a second query statement input by the user; forming a long text by all the target first associated statements, and inputting the long text into a large language model to obtain a first SQL statement; when a filtering condition exists in the first SQL statement, inputting the second query statement into a large language model to obtain a first filtering field; and determining a final executable SQL statement of the to-be-processed service according to the first SQL statement and the first filtering field. According to the method and the device, the problem of dependence on the relationship between data table metadata information and data is solved, the SQL use threshold of a user is reduced, and the SQL statement writing efficiency is improved.

The application relates to a software package installation method and a software package installation device. The method comprises the following steps: obtaining attribute information of a software package to be installed based on a software package installation request input in a software package management tool, and then querying the attribute information of the software package to be installed in a software package index tree; then, determining a list of dependent software packages of the software package to be installed from a dependency database in the case that the attribute information of the software package to be installed exists in the software package index tree; and finally, executing an installation step of the software to be installed based on the list of dependent software packages. The leaf node of the software package index tree is attribute information of a plurality of software packages; the dependency database stores key-value pairs of each software package; and the key-value pair of each software package comprises attribute information of the software package and a list of dependent data packages. The method can improve the software package installation efficiency.

The embodiment of the invention discloses a warehouse counting model timeliness improvement method and device, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining waybill data to be updated; determining an original node process for updating the waybill data to be updated to the target waybill model, wherein the original node process comprises at least two original task nodes with an original dependency relationship; for each original task node, adjusting an original dependency relationship corresponding to the original task node according to a direct source of data on which the original task node depends to obtain a target node process; and updating the waybill data to be updated to the target waybill model according to the target node flow. The dependency relationship between the task nodes is adjusted through the direct source of the data on which the task nodes depend, and the execution priority of the task nodes is improved, so that the data output timeliness of the waybill model is improved, and sufficient time is reserved for subsequent business data consumption.

The embodiment of the invention provides a data processing method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining table data in a data warehouse, the table data comprising access data, scheduling data and dependency data; inputting the table data into a pre-trained life cycle coefficient recommendation model to obtain a first life cycle coefficient; a target life cycle is determined according to the life cycle coefficient and an initial life cycle, and the initial life cycle is determined according to table data; and processing the table data according to the target life cycle. The technical problem of storage resource waste caused by the fact that the life cycle of the data cannot be accurately determined in the data management process is solved.

The application provides a dissolved gas in oil prediction method and device considering physical information, and the method comprises the following steps: performing an oil-paper insulation discharge test; collecting operating state parameters during the discharge process; collecting oil samples during the discharge process and obtaining concentration data of dissolved gas in the oil; constructing a neural network prediction model by using the operating state parameters and the concentration data and performing training; receiving real-time monitoring data of the oil-paper insulation discharge, and realizing dissolved gas production prediction in the oil by using the neural network prediction model. The application adds physical information constraints to the neural network model, uses energy and current as constraint conditions, avoids the shortcomings that an artificial intelligence data-driven model excessively depends on the number and quality of data samples, and can accurately predict the dissolved gas production in the oil, which has certain guiding significance for improving the fault diagnosis capability of deeply mining the dissolved gas in the oil and ensuring the safe operation of power transformers.

The embodiment of the invention discloses a data processing method and device, equipment and a medium, and the method comprises the steps: determining at least one to-be-used decision algorithm corresponding to a business request, further determining a target service corresponding to the at least one to-be-used decision algorithm, and transmitting the business request comprising a decision algorithm identifier to the target service, sending a decision algorithm identifier to the target service to call a corresponding target dependency item based on the decision algorithm identifier, and determining a feedback result based on the target dependency item and service information carried in the service request; and integrating the feedback results to obtain a target feedback result. According to the technical scheme provided by the embodiment of the invention, the problems of high network energy consumption, low decision-making efficiency and incapability of effectively multiplexing the data caused by centralized processing of all the obtained dependent data by the decision-making system are solved, the data transmission is reduced, the occupation of network resources is reduced, the multiplexing rate of the data is improved, and the user experience is improved. The technical effects of reducing the data processing pressure of the decision-making system and improving the decision-making efficiency are achieved.

A system may receive, by an artificial intelligence (AI) cluster from a gateway service, a request for at least two AI services and content associated with a communication session, wherein the request comprises a first request for a first AI service and a second request for a second AI service, and wherein an output of the first AI service is an input of the second AI service. The system may generate, using the content, the output of the first AI service. The system may transmit, to the gateway service, the output of the first AI service. The system may generate, using the content and the output of the first AI service, an output of the second AI service. The system may transmit, to the gateway service, the output of the second AI service.

The invention discloses a modular development life cycle management method, system and device based on hot update, and the method comprises the steps: responding to a detection result that a target module appears in a module warehouse, dynamically loading the target module in a preset isolation environment, and verifying the target module; when the target module passes the verification, registering the target module into a target system; obtaining a target function module corresponding to the target module in the target system according to the target module and the target system; obtaining calling data and / or dependence data of the target function module according to the target function module; obtaining target time based on a preset prediction algorithm according to the calling data and / or dependency data of the target function module; and according to the target time, loading the target module into the target system to replace the original module used by the target function module. The problems that in the prior art, a large amount of manual operation is needed in modular development life cycle management based on hot updating, and the automation degree is low are solved.

The invention provides a data processing method and device, electronic equipment, a computer readable storage medium and a computer program product, and relates to the field of computers, in particular to the technical field of parallel computing, data processing and directed relational graphs. According to the implementation scheme, configuration files corresponding to a plurality of operators are obtained; analyzing the configuration file to generate first mapping pairs, wherein each first mapping pair comprises the operator and corresponding output data corresponding to the operator; analyzing the configuration file to generate second mapping pairs, wherein each second mapping pair comprises the operator and corresponding dependency data corresponding to the operator; determining third mapping pairs based on the first mapping pair and the second mapping pair, each third mapping pair comprising two operators represented as pointing from the first operator to the second operator; according to each third mapping pair, the first operator and the second operator serve as nodes in the generated directed relation graph, and the edge pointing to the second operator from the first operator is used for representing that the dependency relationship exists between the two nodes.

This application relates to the field of network switching technology, and in particular to a network resource scheduling method based on a binary moth optimization algorithm. The method includes the following steps: generating a service requirement model, initializing the number of moths, and defining a service fitness function; calculating an initial moth path matrix and an initial flame matrix; iteratively optimizing the initial moth path matrix and flame matrix from the initial moth path matrix and initial flame matrix until a preset iteration termination condition is met, wherein in each iteration, the service fitness matrix of the current round of moths and flames is calculated and flame attenuation is performed; updating the flame positions in the flame matrix based on the updated moth paths; and selecting the optimal path from the final flame matrix as the service forwarding path. This invention solves the problem that existing technologies cannot cope with complex and dynamically changing QoS requirements, and has the characteristics of being data-independent and having low computational resource requirements.

There is provided a data dependent acquisition (DDA) method of mass spectrometry using a dual analyser mass spectrometer. The method comprises: performing, by a first mass analyser, an MS1 scan of the

The invention discloses an abnormal data identification method, apparatus and device, and a readable storage medium. The method comprises the steps of obtaining to-be-identified data; wherein the to-be-identified data comprises variable values of at least one type of data variables; performing probability density calculation on the to-be-identified data by using a target Gaussian mixture model obtained by pre-training to obtain a probability density value; obtaining a preset probability density threshold value; and when the probability density value is smaller than a probability density threshold value, identifying the to-be-identified data as abnormal data. The method does not need to depend on data labeling, is relatively flexible in data volume requirement, improves the accuracy of abnormal data recognition, and has probability interpretability.

The invention relates to the technical field of databases, and discloses a data recovery method and device for a PostgreSQL database, which are executed on the premise of not depending on pre-stored backup of the PostgreSQL database, and comprises the following steps: scanning file system metadata of a storage partition where the database is located, positioning a physical storage area which is occupied by database data files, and storing the physical storage area in the storage partition; the range is used as a target range of subsequent scanning data fragments; within the target range, executing deep scanning for the PostgreSQL storage structure to extract an effective data fragment set; based on the effective data fragment set, reconstructing a logic structure definition of a deleted table; and analyzing and assembling the effective data fragment set according to the logic structure definition, recombining the effective data fragment set into a complete logic data row record, and exporting a data file in a preset format to complete data recovery of the database.

The invention relates to using memory metadata to improve accuracy of a data dependent memory prefetcher. Data dependent (memory) prefetcher support is described. In some examples, a data dependent (memory) prefetcher utilizes metadata to predict whether a data word is a valid pointer prior to prefetch. Data words that are not regarded as valid pointers are not used for prefetch. The metadata may be linear or physical, depending on the example.

The invention relates to the technical field of computers, and discloses a business system testing method, device and equipment, a storage medium and a program product. The method comprises the following steps: under the condition that a service request sent by a terminal is received and dependency data is needed for processing the service request, obtaining the dependency data needed for processing the service request by an original service system and a first processing result obtained by processing the service request; the dependent data is user real data which is stored in an external system and is associated with the service request, and data provided for an original service system; sending the service request and the dependency data to a to-be-tested service system based on the message queue; receiving a second processing result sent by the to-be-tested service system; the second processing result is obtained by the to-be-tested service system based on the dependency data processing service request; and determining a test result of the to-be-tested service system according to the first processing result and the second processing result. By adopting the method, the accuracy of a test result can be improved.