3781results about "Database design/maintainance" patented technology

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

The invention provides an online map tile fast loading and caching optimization method, medium and system, and belongs to the technical field of map processing.The method includes the steps that a tile index database and an exchange priority matrix are established, a viewport moving trend vector is calculated according to user sliding operation, a future viewport range is predicted, and a preloaded tile list is screened; the method comprises the following steps: performing cache validity check on tiles, establishing a small change detection vector to judge whether updating is needed, establishing a tile hit rate statistical matrix, fusing a time decay factor and a spatial correlation factor to calculate a popularity score, and gathering high-popularity tiles in a quick access area by adopting a thermodynamics-imitating entropy reduction algorithm to realize cache ordering. And the next tile demand of the user is predicted, so that the technical problem that the overall loading response speed is reduced due to an overlong high-value tile access path caused by disordered distribution of cache resources in the online map tile loading process is solved.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention relates to the field of mold cooling optimization, and discloses an automobile mold cooling optimization system and method based on reinforcement learning, and the method comprises the steps: collecting the real-time temperature and flow data of each cooling loop, carrying out the preprocessing and abnormity filtering of the data, and constructing a multi-dimensional basic cooling state database through combining historical operation records and environmental parameters; performing spatial mapping and statistical analysis on the temperature and flow data in the basic database, identifying a high-temperature region with heat concentration and a part with remarkable temperature gradient, and forming a local heat feature model; establishing an independent control sub-model, and optimizing cooling water flow and temperature regulation parameters of each loop by adopting a simulation and reinforcement learning combined method; a local adjustment strategy and an overall cooling target are fused, and main loop parameters are optimized through a global control algorithm; in the mold cooling process, temperature and flow fluctuation are continuously monitored, and real-time data are input into an optimization system. The method has the advantage that the cooling precision and efficiency are improved.

The invention discloses a process intelligent simulation method and system based on rational number fusion, and belongs to the technical field of high-end equipment manufacturing and artificial intelligence. The method comprises the following steps of: constructing a fusion database used for storing a mapping relationship between process parameters and organization characteristics by taking a mathematical model fusion normal form as a core, taking the mathematical model data as theoretical model data and taking the mathematical model data as industrial field data; constructing a multi-scale AI simulation agent model corrected by the industrial field data, and constructing an intelligent prediction model used for representing an association relationship between organization characteristics and service performance; and finally, receiving a target performance index, carrying out global optimization under double constraints of a physical boundary of a theory and a simulation environment which is corrected by a number by utilizing an AI reverse design engine, and carrying out reverse solution to obtain an optimal process parameter. Through a two-way closed-loop mechanism of a theoretic constraint number and a number correction theoretic, the fundamental problems of distortion of a theoretical model caused by a scale effect and lack of physical constraint with an AI model are solved.

The invention discloses an intelligent building equipment historical data storage method, and relates to the field of building equipment historical data storage, and the method comprises the following operation steps: S1, collecting and preprocessing global data; s2, intelligent storage strategy decision making; s3, designing a semantic storage structure; s4, constructing and managing a distributed storage cluster; s5, intelligently writing and indexing historical data; and S6, efficient query and deep analysis of an interface. According to the historical data storage method for the intelligent building equipment, provided by the invention, all-around improvement is realized in the aspects of data quality, storage efficiency, association capability, system reliability, operation convenience and the like through refined design of a data full link; and finally, the equipment data of the intelligent building really becomes a core asset which can be perceived, analyzed and utilized, and powerful support is provided for efficient operation, energy conservation, consumption reduction and safety guarantee of the building.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

Disclosed are systems and methods for maintaining data integrity (e.g., of a database, a data lake, etc.). In some embodiments, one or more processing circuits identify an update, insertion, or deletion of unstructured data stored in an upstream source. The processing circuits determine a new schema of the unstructured data using an inferring function and further determine a divergence between the new schema and a previous schema of the unstructured data based on comparing the new schema to the previous schema of the unstructured data. In an example, the processing circuits store the structured data in a database. The database includes an established data channel between the database and a downstream source, and the structured data is a unique representation within the database of the update, insertion, or deletion of the unstructured data.

The invention relates to the technical field of material management, and discloses a dynamic rule generation and self-adaptive auditing system and method for material management, and the system comprises a rule intelligent extraction module, a rule management knowledge base, an enhanced auditing engine, a man-machine cooperation calibration module and a self-adaptive execution module. The method comprises the steps of automatic rule extraction, rule storage and management, enhanced auditing and reasoning, man-machine collaborative calibration, knowledge base real-time optimization and adaptive routing execution. According to the method, the rule is automatically extracted from the unstructured document, the problem that a traditional system rule depends on manpower and is lagged in updating is solved, dynamic optimization of the rule and confidence is achieved by introducing a man-machine collaborative feedback closed loop, the accuracy and transparency of an audit decision are improved by enhancing reasoning and explainable decision technologies, and the audit efficiency is improved. And the optimal balance between auditing efficiency and risk control is realized through self-adaptive routing execution based on credibility.

A computing system is disclosed for generating and optimizing schema-conformant object models. The system obtains input data from multiple sources in structured, semi-structured, or unstructured formats and processes the data using parsing logic to derive a structured rule set defining spatial relationships and constraint conditions. The system generates schema-conformant object representations comprising parametric assemblies, applies the constraint conditions using machine-executed resolution logic, and produces candidate object models through iterative modification of spatial components, dimensional parameters, and adjacency relationships. Performance values are computed for the candidate models using quantitative evaluation of spatial efficiency, material usage, and constraint compliance. An optimized object model is selected based on the computed performance values and may be used to generate construction project plans including technical drawings, material specifications, and scheduling instructions.

A system including a computing device configured to receive a time-based service record, generate a structured metadata object for the service record, the structured metadata including a plurality of programmatically assigned descriptors selected from a configurable schema, instantiate a dual-path processing routine as a function of the programmatically assigned descriptors, retrieve a constraint profile comprising at least one temporal delivery threshold associated with the resource allocation output and the utilization performance evaluation, aggregate time-based service records over a reporting interval as a function of the structured metadata, compute, using a time-mapping module, a constraint deviation metric as a function of comparing the time-based service records and the temporal delivery threshold, and generate an output object as a function of the constraint deviation metric.

The invention relates to the technical field of geothermal exploitation, and discloses a geothermal well group intelligent cooperative regulation and control method for geothermal exploitation, which comprises the following steps: collecting multi-source heterogeneous data in real time through multiple types of sensors deployed in a geothermal well group and a surrounding environment, data standardization, time sequence alignment and noise filtering are carried out through a uniform interface and a communication protocol, and a multi-dimensional monitoring database of the whole geothermal exploitation process is constructed; on the basis of multi-source heterogeneous data, historical mining data in a database is monitored in combination with a geologic structure of a geothermal reservoir, a seepage-heat conduction coupling mechanism and multiple dimensions. According to the invention, by deploying multiple types of sensors and constructing a multi-dimensional monitoring database, real-time acquisition and standardized processing of high-precision and multi-source heterogeneous data in the whole geothermal exploitation process are realized, a reliable data basis is provided for subsequent intelligent decision making, and the sensing ability of the system to complex geology and exploitation environments is significantly improved.

The invention discloses a system and a method for automatically collecting metadata for a heterogeneous data source, the system and the method for efficiently collecting the metadata for the heterogeneous data source are constructed through a full-automation, unification and intelligentization technical path, and the system and the method have the advantages of comprehensiveness, automation, intelligentization and real-time performance, that is, the system and the method are not limited to a certain type of data sources any more. Comprehensive support for multiple types of data sources is realized through a unified framework; an end-to-end automatic process is constructed, and manual intervention is not needed from source discovery to metadata extraction and storage; natural language processing and machine learning technologies are fused, so that an acquisition result has business semantic comprehension and classification capabilities; through an event-driven dynamic monitoring and incremental updating mechanism, high consistency of metadata and source data is realized.

A system and method for querying data sources for cybersecurity analysis is presented. The system and method include: receiving security logs from at least one data source, wherein the security logs lack pre-defined schema; generating a schema of the security logs based on at least a type of data of the security logs, wherein the generated schema includes fields of the security logs and values of the fields; embedding field vectors, wherein each field vector is a vector representation of a value of each respective field; embedding value vectors, wherein each value vector is a vector representation of a natural language description of each value in each respective field; and generating a query in a cyber language query, using an AI system, for execution on at least one target data source based, in part, on the generated schema, the embedded field vectors, and the embedded value vectors.

A system and method for generating a database query based on a natural language query is presented. The method includes receiving an unstructured natural language query directed to a security database, wherein the security database includes a representation of a computing environment; selecting a group of database queries from a plurality of preexisting database queries based on a similarity to the unstructured natural language query; generating a context for processing by a language model, the context including the selected group of database queries, an identified technology, and a schema of the computing environment; processing a prompt and the generated context utilizing the language model to generate a second database query; and executing the second database query on the security database.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

Apparatus and method for generating an output using an AI-PII model. The apparatus includes at least a processor and memory communicatively connected to the at least a processor. The memory instructs the processor receive personally identifiable information (PII) data, receive one or more model constraints, map, using an AI-PII model, the PII data to at least a data schema as a function of the one or more model constraints by identifying at least a PII datum of the PII data, categorizing the at least a PII datum to one or more categories of a plurality of categories, and mapping the PII data to the at least a data schema, modify the data schema based on a refinement datum, wherein the refinement datum is generated based on a temporal datum of the one or more model constraints, and generate an output as a function of the refinement datum and data schema.

A computer-implemented method for improved schema matching of two databases is disclosed. The method can receive a schema of a source table from a first database and a schema of a plurality of target tables from a second database, identify one or more matching tables among the plurality of target tables based on comparison of the schema of the source table and the schema of the plurality of target tables using a large language model, obtain first sample attribute data from the source table and second sample attribute data from a selected matching table, and identify one or more pairs of matching attributes between the source table and the selected matching table based on comparison of the first sample attribute data and the second sample attribute data using the large language model. Related systems and software for implementing the method are also disclosed.

A computerized method is provided for using large language models to integrate data from multiple sources including technical metadata (e.g. column and table names) with a dictionary of standard abbreviations employed in the metadata, representative data from the columns rows of the table, a business glossary of terms in the data, and representative queries used to interrogate the data, to create a non-technical or natural language description of business value of the table and its columns and data.

The invention provides an intelligent recommendation method, system and device for MySQL dynamic parameters and a medium, and belongs to the technical field of databases. The method comprises the following steps: collecting structured performance indexes in real time, extracting unstructured text knowledge, and storing the unstructured text knowledge into a data lake; performing cleaning, fusion and feature extraction processing on the acquired data, constructing a time sequence data set, and generating a multi-modal feature vector; constructing a parameter optimization model fusing the text encoder, the parameter dependency graph encoder and the reinforcement learning decision maker, and training the parameter optimization model; according to the real-time system state vector and the user optimization target, using the parameter optimization model to output parameter adjustment suggestions, generating a parameter adjustment suggestion list, and predicting potential risks; according to the parameter adjustment suggestion, a progressive adjustment strategy is adopted to execute parameter adjustment operation, and the adjusted performance index is monitored in real time; and feeding back a parameter adjustment implementation result to a parameter optimization model training process, updating a model weight, generating a parameter adjustment case and storing the parameter adjustment case in a knowledge base.

The invention provides an optical element surface type simulation method based on a physical information neural network, and relates to the technical field of element design and simulation, and the method comprises the steps: constructing a feedforward neural network; generating a deformation control equation model library; constructing a three-dimensional simulation database; forming a machine learning parameter library; training the PINN deformation simulation model in combination with a deformation control equation model library, a three-dimensional simulation database and a machine learning parameter library, and screening out a target PINN deformation simulation model meeting a preset condition; inputting the design parameters, the support structure parameters and the given external load parameters of different combinations into the target PINN model to obtain a key performance prediction result of the optical element; according to the method, the key performance of the element under various parameter combinations can be rapidly evaluated, the design iteration period is shortened, the trial and error cost is reduced, direct data support is provided for supporting structure optimization design, and the bottleneck problem that a traditional method restricts the research and development efficiency of a high-precision optical system is effectively solved.

The invention relates to the field of database reading, and particularly discloses an efficient database reading method based on multi-level cache optimization and dynamic index fragment.The efficient database reading method comprises the steps that high-frequency query data is accurately recognized through a sliding window algorithm, a dynamic cache loading and preheating strategy is implemented, query delay is effectively shortened, and the database burden is relieved; intelligent distribution of index fragments is realized by applying a consistent Hash algorithm, and the query efficiency is greatly improved in cooperation with distributed query routing and transaction processing optimization; the query load balancing ensures stable and efficient operation of the system in a high-concurrency scene by means of weighted polling and a minimum connection strategy in combination with real-time monitoring and an elastic capacity expansion and contraction mechanism. According to the method, the database access efficiency and the dynamic adaptive capacity are remarkably improved, and the method is particularly suitable for a large-scale distributed data processing environment.

The invention discloses a traceability application-oriented block chain database data management system, particularly relates to the technical field of block chain database data management, and is used for solving the problems of easy data tampering and low verification efficiency caused by traceability data logic association and physical storage splitting. The method comprises the following steps: firstly, matching a parent physical sector offset through an association analysis module, determining a storage partition, and assembling a write-in instruction; then, a topology binding module executes a disk write operation, synchronously reads parent node hash and performs combinatorial operation in combination with a physical address to generate a topology binding check value bound with the physical position; the aggregation anchoring module uses a Merkel tree aggregation check value to generate a root fingerprint and perform uplink, and establishes an anchoring voucher; and finally, a path verification module positions a physical position according to the certificate, executes reverse recursive addressing and step-by-step Hash recalculation, and assembles a pedigree verification packet, so that a closed-loop trust system from physical storage to chain certificate storage is constructed, and the tamper-proof capability and verification efficiency of the traceability data are improved.

Methods, systems, and devices for data management are described. A method may include obtaining a new snapshot of a relational database of a source computing environment, comparing the new snapshot of the relational database to a prior snapshot of the relational database, updating, in response to the comparison indicating that data of a row of a relational database table changed between the prior snapshot and the new snapshot, a field of a second versioning control column of a corresponding row of a backup database table of the set of backup database tables to indicate a version of the new snapshot, and adding, in response to the comparison indicating that the data of the row of the relational database table changed, an additional row to the backup database table, where a field of a first versioning control column of the additional row includes an indication of the version of the new snapshot.

The invention discloses a database watermark management system and method, electronic equipment and a storage medium, and relates to the technical field of information security, and the disclosed database watermark management system comprises an intelligent decision layer which is used for analyzing data features of input data and determining a watermark embedding mode of the input data according to an analysis result; the bimodal embedding layer is used for carrying out watermark embedding operation on first data of which the watermark embedding mode is a numeric embedding mode in the input data by adopting a preset improved least significant bit algorithm, and carrying out watermark embedding operation on second data of which the watermark embedding mode is a non-numeric embedding mode in the input data by adopting a preset improved least significant bit algorithm; carrying out watermark embedding on the second data by adopting a multi-system invisible character replacement technology; and the cross-modal traceability layer is used for analyzing the watermark information from the input data embedded with the watermark through a preset double-watermark collaborative verification mechanism. According to the method, the adaptation degree of database watermark embedding can be improved.

The invention discloses a data migration method and device, computer equipment and a storage medium, and realizes a data migration processing mode oriented to an unstable network environment by combining neural network prediction, decision tree anomaly recognition and a distributed cache mechanism. Therefore, the data transmission stability and the migration task completion rate in the cross-network environment are obviously improved. Firstly, a pre-trained neural network is utilized to carry out deep prediction on a real-time network state, and a potential performance reduction trend is identified in advance, so that a migration process can actively adjust a path or a strategy before a risk occurs. And secondly, by performing decision tree identification on a data packet sending sequence, the system can position a potential interruption point based on fine-grained time sequence characteristics, and quantify the risk in an interpretable manner, so that migration control can be accurate to a specific data packet level. Finally, under the condition that the risk is unacceptable, the system automatically activates a distributed cache mechanism, and the durability and the restorability of the data are guaranteed in a redundant copy storage mode.

The invention provides a data migration method which can be applied to the technical field of artificial intelligence, and relates to application of a large model in the fields of information security and financial science and technology. The data migration method comprises the steps that in response to a migration request for migrating target data in a first database to a second database, a target agent is called to split the migration request into N migration tasks, the database structures of the first database and the second database are different, and N is an integer larger than or equal to 2; calling a target agent to sequentially execute N migration tasks based on a preset tool set, so as to migrate the target data in the first database to a second database; wherein at least one non-target tool in the preset tool set is masked in advance based on the to-be-executed target migration task, and the target agent is configured to perform tool calling based on the masked preset tool set. The invention further provides a data migration device and equipment, a storage medium and a program product.

The invention discloses a cross-heterogeneous database data migration method and system, and the method comprises the steps: building a multi-data-source connection among multiple data sources, and enabling the multi-data-source connection to be used for representing the incidence relation between a source database and a target database; obtaining a source data table based on the migration sql, and querying source data corresponding to the source data table from a source database; converting the source data based on a preset model mapping rule to obtain target data; determining a target database into which the source data is migrated according to the multi-data source connection, and writing the target data into the target database to realize data migration; and performing data verification on the target database according to the checking rule to obtain a migration result, and generating a result report according to the migration result. According to the method, the source data is converted into the target data through the preset model mapping rule, migration obstacles caused by structural differences among heterogeneous databases are effectively solved, the consistency of the data is ensured through the checking rule, and the accuracy of data migration is improved.

The invention provides a cold and hot data dynamic migration method and device, a storage medium and computer equipment. Multi-dimensional feature data corresponding to business data to be migrated is obtained. And then inputting the multi-dimensional feature data into a preset intention prediction model to obtain the access probability of the business data in a future period of time, and accurately quantifying the expected popularity of the data. And then obtaining a current access probability threshold value, and performing cold and hot storage migration on the business data according to a storage area where the business data is located according to comparison between the dynamically calculated access probability and the access probability threshold value. In the process, the future access probability of the data is accurately predicted through the extracted multi-dimensional feature data, so that the change of a service access mode is dynamically adapted, the dynamic balance between the storage cost and the query performance is realized, and the flexibility and the adaptivity of cold and hot data migration are improved.

The invention discloses a real-time cleaning and aligning method for multi-source heterogeneous data, and particularly relates to the technical field of source heterogeneous data, a self-adaptive interface adapter is compatible with multiple types of data, and a semantic index atlas is generated through four-dimensional classification labeling; constructing a cloud-edge collaborative streaming processing framework, preprocessing edge nodes, and performing accurate cloud alignment; a dynamic rule cleaning and semantic-dimension-entity three-layer progressive alignment mechanism is adopted, and a four-dimensional quality evaluation system is combined for real-time monitoring; and through closed-loop optimization, homomorphic encryption, a block chain and other security mechanisms, data security and traceability are ensured. According to the real-time cleaning and aligning method for the multi-source heterogeneous data, the real-time performance and accuracy of data processing are effectively improved, the method is adaptive to multiple service scenes, and high-quality data support is provided for data value mining.