114 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 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.

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 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 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 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.

The application discloses a data collection method, device and equipment, a storage medium and a program product, and is suitable for the technical field of data processing, and is used for reducing the data collection cost. The method comprises the following steps: adding a probe in a database packaging program, the database packaging program being a program for performing a packaging operation on data in a database; when an application calls the database packaging program to perform a packaging operation on data in a first database, obtaining changed data generated by the current call through the probe, and storing the changed data in a preset temporary storage area; and sending the data in the preset temporary storage area to a second database. Through the probe adding mode at the application level, the database changed data is obtained, the data collection tool is not depended on, and the data collection cost is reduced.

The present invention discloses a unit testing method and device, and relates to the field of computer technology. A specific implementation of the method includes: in response to the startup operation of the unit test common component, referencing the test base class or marking the external interface; loading all services associated with the test base class or the simulation interface corresponding to the external interface to convert the unit test instruction that calls the remote service into the unit test instruction that calls the local service; when receiving the unit test instruction for a remote service, obtaining the dependency data corresponding to the remote service from the local service to perform the unit test operation based on the dependency data to obtain the test result. This implementation abandons the existing idea of ​​taking test data from multiple external interfaces, simulates these external interfaces and test data from the local test environment to reduce external dependencies, completes the corresponding test work in its own test environment, and saves time and effort as a whole.

The invention relates to the technical field of prediction, and provides a data prediction method and device. The method comprises the steps of performing frequency domain moving average adaptive normalization on to-be-predicted data to obtain target normalized data; performing grouping modeling on the target normalized data to obtain inter-channel dependency data inside and outside a group; inputting the inter-channel dependency data into a large language model, and performing fine tuning on the large language model to obtain prediction data output by the large language model; and carrying out reverse normalization on the prediction data to obtain target prediction data. According to the method, the problems of non-stationarity, channel dependence and insufficient data volume of operation and maintenance data can be fully solved, so that the prediction accuracy and the prediction efficiency are improved.

Systems and methods for generating deployment databases based on preloaded data from heterogeneous sources are disclosed herein. The system may receive first structured data. The system may extract first data of a first format and second data of a second format. The system may determine that the first data has a first update rate. The system may determine that the second data has a second update rate. The system may preload the first data by retrieving a first identifier and by storing a first preloaded representation of a first parent dataset for the first data. The system may receive a first request for a deployment database. The system, based on the first request, may retrieve the first preloaded representation. The system may generate the deployment database.

A method, apparatus, medium, device, and product for processing project component data are disclosed. The method includes: obtaining first data corresponding to a first function, wherein the first data is obtained based on the dependency information of the first function, the dependency information indicating a first file package that the first function depends on, the first file package being pre-associated with multiple first files, and the first files storing the data dependent on the first function; injecting a second function into the runtime environment of the first data; and obtaining the output result of the first function based on the first data. This disclosed solution is applied to the construction of application components in a project management system, achieving decoupling of component business logic from the backend, reducing component development costs, and improving system operating efficiency.

In a computer-implemented method for material decomposition in dual energy X-ray imaging, a first X-ray image data set (21, 24) corresponding to a first X-ray radiation spectrum and a second X-ray image data set (22, 25) corresponding to a second X-ray radiation spectrum are obtained. At least one material-specific image dataset (13, 14) is generated by applying a decomposition module (10) comprising a first sequence of processing steps or a machine learning function to input data dependent on the first X-ray image dataset (21, 24) and the second X-ray image dataset (22, 25), a filtering module and / or an artifact reduction module is applied to the input data before the decomposition module (10) is applied.

The present invention relates to a heterogeneous platform parallel optimization method for a hierarchical step transformation loop with dependencies, belonging to the field of electronic information technology. It includes: 1) The CPU preprocesses the outer-layer data dependencies; in the hierarchical step transformation loop, the outer-layer dependent data is extracted from the entire hierarchical step transformation loop and independently executed for calculation on the CPU side; 2) The hierarchical loop is adjusted to a single-layer loop; 3) The remapping of the dependent array after extraction is performed. The present invention extracts the outer-layer dependent data from the entire loop body and performs pre-calculation in the CPU. Thereby, it avoids the repeated startup and shutdown of the kernel function, resulting in time waste. The present invention proposes a method and steps for changing the hierarchical step loop to a single-layer loop, and at the same time modifies the data access method, reducing the frequent transmission of uncertain data and improving the execution efficiency.