87 results about "Database optimization" patented technology

The invention relates to the technical field of data processing, and discloses a steel structure welding seam quality ultrasonic detection method and system. The method comprises the following steps: scanning a welding seam to obtain digital parameters, constructing a virtual model to generate an ultrasonic response database, optimizing a detection parameter forming scheme, executing distributed acquisition to obtain a signal matrix, extracting defect features by using a neural network, and outputting a digital detection report through transfer learning. According to the method, automatic optimization of detection parameters and accurate extraction and intelligent analysis of defect features are realized, the accuracy, efficiency and reliability of detection are improved, and the dependence on artificial experience is reduced.

The invention discloses an automatic SQL tuning method and system combined with a large language model and a database optimizer, computer equipment and a storage medium, and the method comprises the steps: generating a query fingerprint according to an SQL query statement input by a user, detecting whether there is an optimization result matched with the query fingerprint in a cache, and only when there is no optimization result, executing the step 1; if yes, the SQL query statements input by the user and the optimization instruction are input into the large language model, and a plurality of candidate SQL query statements are generated; performing semantic equivalence verification on the candidate SQL query statements and an SQL query statement input by a user, and screening out semantic equivalence candidate SQL query statements; submitting the screened candidate SQL query statements to a database optimizer, and obtaining an execution plan and cost information of each candidate SQL query statement; calculating an evaluation score of each candidate SQL query statement, and selecting the candidate SQL query statement with the highest evaluation score as an optimization result; and writing the optimization result and the related execution data into a cache, and returning the optimization result and the related execution data to the user.

The invention discloses a pulsed eddy current detection method and system for a thermal power pipeline with a coating layer, and relates to the technical field of pulsed eddy current nondestructive detection.The pulsed eddy current detection method comprises the steps that pulsed eddy current detection simulation and experiment are conducted under the condition of a three-dimensional simulation model and a test piece, and excitation current waveform, amplitude and frequency parameters are determined; an induced voltage signal is collected, peak values, time points and attenuation characteristics are extracted, the corresponding relation between the wall thickness, the coating layer thickness and the welding seam structure and detection signals is constructed, a parameterized database is constructed, and temperature correction is completed; and optimizing a sensor structure and a signal acquisition scheme according to a parameterized database, obtaining detection data in a pipeline operation state, completing two-dimensional imaging of wall thickness distribution and welding seam positioning, and forming detection information covering a straight pipe, an elbow and a welding seam area. According to the method, wall thickness detection and welding seam positioning of the thermal power pipeline under the conditions of a thick wall and a thick coating layer are achieved through the full-process design of test piece modeling, simulation experiment and imaging collection.

The embodiment of the invention discloses a data skew detection optimization method and device for a multi-cluster database and a medium, belongs to the technical field of databases, and solves the problems of low efficiency and high error rate of a mode of manually detecting the data skew condition of the database under the condition of large-scale clusters. Comprising the steps of responding to a scheduling task instruction, and determining a target cluster based on the scheduling task instruction; obtaining a table object in the target cluster, and executing a corresponding data distribution detection strategy on the table object based on the type of the table object to determine line number distribution of the table object on each Segment node; obtaining a basic slope rate based on the line number distribution, and performing multi-dimensional adjustment on the basic slope rate to obtain a data slope rate corresponding to the table object; performing line number distribution standard deviation calculation based on the line number distribution to obtain a data inclination degree corresponding to the table object based on a calculation result; and matching a corresponding database optimization strategy based on the data inclination rate and the data inclination degree.

The invention relates to the technical field of database optimization, and discloses a multi-table connection sequence query optimization method, device and equipment, and the method comprises the steps: carrying out the feature extraction of a received database query statement, obtaining a query feature representation, and enabling the database query statement to comprise the data query of a plurality of tables; inputting the query feature representation into a pre-trained target network model, and predicting execution time of different connection sequences in a database query statement to obtain an execution time degree; and on the basis of a Monte Carlo tree, taking the query feature representation and the execution time degree as reward functions, and optimizing the connection sequence of the database query statements to obtain a target connection sequence. According to the method, feature extraction, model prediction and an optimization process based on the Monte Carlo tree are integrated, the execution time and complexity are comprehensively considered, the optimal connection sequence is selected, the limitation that a traditional method is only based on a cost model is overcome, and more accurate connection sequence selection is achieved.

In some embodiments, there is provided generating a logical query plan for a query that includes a single fact table and multiple reducer tables; optimizing the logical query plan for query execution; and executing the optimized logical query plan.

The present invention discloses a database intelligent parameter tuning system based on large language model enhancement, comprising: a large language model auxiliary module, which utilizes the rich prior knowledge of the large language model in the database field and the powerful reasoning and generation capabilities of the large language model to assist and enhance the capabilities of the tuning system; a parameter configuration recommendation module, which is used to automatically recommend the most promising database parameter configuration in an intelligent and efficient manner based on the current tuning scenario based on the assistance of the large language model; and a parameter configuration evaluation module, which is used to perform performance evaluation on the recommended parameter configuration during the database tuning process to obtain its specific performance value. The present invention combines artificial intelligence large language model technology with database optimization, breaking through the limitations of traditional database parameter tuning systems that are difficult to cope with multiple workloads and have high resource overhead required for tuning, fully capturing the heterogeneous characteristics between database parameters, and enhancing the tuning effect of the database system.

The invention provides a database optimization query method, device and equipment based on artificial intelligence, relates to the technical field of computers, and is applied to a database which comprises a plurality of fragments. The database optimization query method comprises the following steps: obtaining a query instruction of a user and multi-dimensional feature data of each fragment, and analyzing the query instruction to obtain a plurality of calculation factors; based on the multi-dimensional feature data, performing multi-dimensional dynamic decision processing on each calculation factor to obtain execution probability data of the corresponding calculation factor on different fragments; determining a pre-execution plan for executing the corresponding calculation factor on each fragment according to each execution probability data; based on a preset position prediction model, obtaining corresponding position prediction information according to the pre-execution results corresponding to the pre-execution plans; and sorting the pre-execution results of all the position prediction information to obtain a final query result. According to the method, the query efficiency is improved, data transmission and calculation redundancy are reduced, and the resource utilization efficiency is enhanced.

The invention discloses a MySQL table partition management method and device and a medium, belongs to the technical field of database optimization, and is used for solving the technical problems that static configuration and dynamic data of an existing MySQL table are not matched, the operation and maintenance cost is easily increased, and the resource utilization rate is poor. The method comprises the following steps: carrying out multi-modal calculation on real-time data characteristics in a MySQL table to obtain multi-modal characteristics; performing time sequence prediction processing on the multi-modal features, and outputting capacity expansion demand data; performing gene processing on a dynamic partition strategy of a decision space related to capacity expansion demand data through an intelligent decision engine to generate a new partition key; and performing rolling migration execution on the cold and hot data in the MySQL table according to a new partition corresponding to the new partition key to obtain a partition management result of the MySQL table.

The invention discloses a time sequence database LSM-Tree-oriented merging frequency optimization method and system, and relates to the technical field of database optimization. The invention aims to solve the problems of low system throughput and long response time caused by extra delay of a system due to excessive consumption of memory and disk IO (Input / Output) in the existing LSM tree merging method. The method comprises the steps that a database receives external time sequence data and stores the external time sequence data in MemTable; the MemTable sorts the time sequence data and judges whether the MemTable reaches a preset capacity limit or not, if the MemTable reaches the preset capacity limit, the MemTable is newly built, the delay processing data volume N is determined, the last N pieces of data in the MemTable are moved into the newly built MemTable, and the rest of data are written into the SSTable from small to large according to timestamps; otherwise, continuing to receive the time sequence data; finally, judging whether the timestamps of the time sequence data in different SSTtables are overlapped or not, and if the timestamps are overlapped, combining the SSTtables with the overlapped timestamps; and otherwise, directly linking the adjacent SSTable. The method is used for optimizing the LSM merging frequency.

The invention relates to the technical field of database optimization, in particular to a database preloading method and device based on a hybrid architecture and hotspot prediction.The method comprises the steps that on the basis of a preset sliding window mechanism, time sequence characteristics of historical access logs are extracted to recognize high-frequency hotspot data; based on a preset cache preheating mechanism, dynamically generating a loading priority queue according to the data access frequency of the high-frequency hotspot data so as to preferentially load the key data to a memory hotspot; and based on the monitoring data of the LRU list, the flush list and the check point state, a preset self-adaptive mechanism is linked to dynamically adjust a preloading strategy, and the preloading strategy is deeply integrated with a database check point mechanism to form closed-loop control, so that the preloading of the database is realized. Therefore, the problems that frequent changes of an access mode are difficult to cope with due to the fact that a database preloading method mostly depends on a static strategy or simple statistics, and particularly when the concurrency is high and the load fluctuation is large, hot area identification lagging, resource waste, cache hit rate reduction, response delay increase and the like are easily caused are solved.

The invention relates to the technical field of motor driving control, in particular to a multi-motor cooperative driving and torque distribution method and system, and the method comprises the steps: collecting motor rotating speed, current, battery SOC and whole vehicle driving condition data in real time, carrying out the preprocessing of the collected multi-source data, eliminating the noise interference, and carrying out the signal feature extraction; a torque demand model is established based on vehicle driving condition classification, and the target torque of the whole vehicle is calculated; constructing a torque distribution model fusing working condition weights, combining motor efficiency characteristics, a battery SOC state and road condition resistance, and dynamically distributing the torque of each motor through an improved particle swarm optimization algorithm; and the state of the motor is monitored in real time in the driving process, torque distribution data of this time is input into a database after the vehicle is driven, and the torque demand model and torque distribution model parameters are optimized. Through working condition identification, intelligent algorithm distribution and dynamic optimization, the driving safety of the electro-tricycle and the driving efficiency of the motor are improved.

The invention discloses a quantum method and device for processing database multi-query optimization and a storage medium. The quantum method comprises the steps that database multi-query optimization problem expression is converted into a secondary unconstrained binary optimization problem; running a quantum circuit on a quantum computer, and calculating an energy value corresponding to Hamiltonian according to a test state prepared in a quantum processor; summing the energy values on a classical computer, and optimizing parameters of the quantum circuit through a classical optimizer according to a summing result; and iteratively executing the steps of quantum circuit operation, energy calculation and parameter optimization until an energy value converges to a stable value, obtaining an optimal solution of a multi-query optimization problem, and solving the quadratic unconstrained binary optimization model in a quantum-classical hybrid framework by the variable component sub-feature solver algorithm. According to the method, the quantum computer is introduced into the database optimizer, the multi-query optimization problem is solved by using a variable component subscript solicitation solver algorithm, and a problem modeling strategy is improved.

The invention discloses a method and device for improving the read-write performance of a database, and relates to the technical field of database optimizing.The method comprises the following steps that firstly, a database read-write request initiated by a service system is intercepted, and query conditions in the request are analyzed; 2, performing shunt query according to the type of the query condition: if the query is single primary key or primary key list query, routing the request to a key value pair database and reading data; and if the query is composite condition query. According to the method, the database request is intercepted, and the original query of a relational database is converted into the query of a key value pair database or a full-text retrieval database, so that the respective advantages of the key value pair database in a single key value query scene and the full-text retrieval database in a complex query scene are fully exerted; the problem that an existing relational database is large in data volume and slow in query is solved, and the problem that all data cannot be queried at a time due to data separation caused by an existing data fragmentation scheme is solved.

The invention provides a database index table generation method, a query method and electronic equipment, and relates to the technical field of databases. According to the specific scheme, the method comprises the steps that in response to a received database optimization instruction, a to-be-optimized database and a target query statement are determined based on the database optimization instruction; determining a target query field from the target query statement based on the execution plan information of the target query statement; identifying the complexity of a target data column which is stored in the to-be-optimized database and is related to the target query field, and determining an identification result; and generating an index table for querying data in the target data column by using an index generation strategy matched with the condition type of the target query condition corresponding to the target query field under the condition that the recognition result represents that the complexity of the target data column meets the index generation condition.

The invention relates to the technical field of big data management and database optimization, in particular to an index tuning method and system for medical multi-modal data mixed query, and the method comprises the following steps: accessing structured and unstructured medical data into a data lake; performing expansion processing on a nested structure in the structured data according to field types, extracting semantic feature vectors from unstructured data, packaging the unstructured data into a standardized JSON format, performing data fusion, and loading the fused data to a PostgreSQL database; analyzing a mixed query SQL statement submitted by a user, and constructing high-dimensional mixed feature representation; carrying out clustering compression on the query request set by adopting a K-Medoids clustering algorithm to generate a representative query set; performing virtual index simulation evaluation on the structure field by utilizing HypoPG, creating a real index for the vector field in combination with a pgvector plug-in, and collecting a query performance index; and based on IBST multiplexing historical evaluation data, in combination with a structure change prediction model based on a Transform architecture, outputting an optimal index combination. According to the method, the query performance and the index recommendation efficiency are remarkably improved.

The invention discloses a spring type selection method and system for gear motor fault detection. The method comprises the following specific steps: S1, constructing a two-dimensional spring library comprising a spring parameter database and a scene rule database; s2, adopting a spring type selection algorithm, firstly receiving parameters, environmental conditions and tool requirements of a to-be-tested gear motor, and analyzing and standardizing the format; matching historical cases in the scene rule database through feature similarity calculation, and if the matching degree is greater than or equal to a set value, outputting a corresponding spring model; and otherwise, generating a spring performance screening interval from the scene rule relationship based on the theoretical model, and screening the spring model from the spring parameter database as a parameter library screening constraint condition. Through data-driven automatic matching and database iterative optimization, the spring type selection time is effectively shortened, and the timeliness and accuracy of fault detection of the gear motor are improved.

The invention relates to the technical field of intelligent database optimization, in particular to a reinforcement learning dynamic compression strategy tuning method for a column storage database, and the method comprises the steps: collecting the data feature information of a current column storage database; based on the data feature information, judging whether the current column storage database meets a preset compression condition or not; and if the current column storage database meets the preset compression condition, determining a target compression strategy according to the data feature information, and executing a corresponding compression action on the current column storage database based on the target compression strategy. Therefore, data features and loads are sensed in real time through the reinforcement learning algorithm, the optimal compression algorithm is intelligently selected, the problems of low storage efficiency, poor query performance and the like caused by the fact that traditional static compression cannot adapt to dynamic changes of data in the prior art are solved, and the comprehensive performance of an HTAP system is remarkably improved.

The invention discloses a carbonate rock distribution area soil heavy metal traceability method, and belongs to the technical field of soil heavy metal traceability, and the method comprises the following steps: (1) geological background analysis: constructing a regional association type geological background reference; (2) soil sample collection and chemical analysis: providing basic data for traceability work; (3) chemical fingerprint feature extraction: constructing a form-isotope two-dimensional fingerprint, and improving the source distinguishing precision; and (4) spatial distribution modeling and traceability: multi-dimensional data collaborative analysis is carried out, and accurate traceability is realized in combination with spatial visualization. On the basis of the geological background, the natural source reference is defined; establishing a characteristic system database by taking chemical fingerprint data as a core; the receptor model parameters are optimized, and the distinguishing ability of a natural source and an artificial source is enhanced; and the spatial distribution data is used as a carrier to verify the source spatial relevance, so that the traceability precision is effectively improved finally.

The invention relates to the technical field of database optimization, in particular to a master-slave data synchronization optimization method and system.The method comprises the steps that a one-master multi-slave cluster of a database is created, and the cluster is provided with at least two slave nodes; creating a monitoring program, wherein the monitoring program can count the master-slave synchronization delay time of each pair; when the master node has a large data volume write-in operation or executes a large transaction, the slave nodes can be triggered to pull a large amount of data from the master node, at the moment, a synchronous delay threshold value is set, master-slave data synchronization is performed according to the threshold value, and the slave nodes with low synchronous delay are preferentially synchronized; according to the method, the influence of master-slave synchronization can be reduced to the greatest extent, and master-slave synchronization can be quickly completed by the slave nodes.

The invention provides a mechanical design chart hybrid intelligent analysis method and system based on interactive learning, and provides an efficient and systematized solution for intelligent processing of mechanical design chart knowledge by adopting a multi-modal data fusion technology and combining deep learning, an OCR (Optical Character Recognition) technology and natural language processing. Through an accurate chart and text recognition method, the analysis precision and the practical efficiency of design knowledge are remarkably improved, and the precision requirement in standardized mechanical design is met; the system adopts a convolutional neural network and an attention mechanism, solves the problems of insufficient precision and inaccurate structured data output when a complex chart is processed in a traditional technology, and optimizes data integration and processing flows by seamlessly connecting an analysis result with an agent knowledge base and a database.

The invention relates to the technical field of database optimization, in particular to a distributed cache preloading-based SQL query result quick return method, which comprises the following steps of: performing parameter abstraction on an SQL to generate a key prefix; judging SQL fingerprint hotspots, marking preloaded objects, and writing the preloaded objects into a hotspot list; generating a preloading task by utilizing the hotspot list; designing cache and storage; loading the result set in a fragmentation manner, splitting the result set into a plurality of fragmentation tasks, and concurrently executing pre-computation by using threads; adding data consistency metadata when the cache is written; splicing cache keys, and querying cache and storage hit conditions; providing a cache failure and avalanche prevention mechanism of the step random TTL; and the gateway layer exposes cache system design and performance monitoring indexes, and triggers an alarm by using the cache system design or performance monitoring. According to the method, the problems of high passive back-to-source delay, inaccurate hotspot identification, poor data consistency, large result set loading bottleneck, cache avalanche and the like in the existing distributed cache technology are solved.

The invention relates to the technical field of micro-nano machining, in particular to a multi-layer thin film deposition and vacuum packaging integrated process optimization method and system, which comprises the steps of performing functional region division on an initial sensor to obtain a plurality of functional region sets, and obtaining deposition precision based on a deposition unit; obtaining a deposition thickness interval and a deposition layer number interval by utilizing a deposition function corresponding to the functional area set, obtaining a historical data set and a deposition raw material node set which are used as references, and constructing an updated deposition database by utilizing the deposition precision, the historical data set, the deposition layer number interval, the deposition raw material node set and the deposition thickness interval, optimizing the updated deposition database to obtain a deposition-sealing database, obtaining a deposition sensor by using the deposition-sealing database and an initial sensor, and performing vacuum sealing on the deposition sensor to obtain a sealed sensor. According to the invention, the accuracy of depositing and bonding the initial sensor can be improved.

Techniques for managing data stored within a database, such as a decentralized database are provided. Some techniques involve managing some data within a lower-trust database and some other data within a higher-trust database. A higher-trust database may be a decentralize database including a blockchain. A lower-trust database may store references to data within the blockchain, and optionally other data in association with those references. Disclosed techniques include WHERE clause query handling in databases with reference values, replacement of distinct data in a relational database with a distinct reference to that data, number line storing for secure indexing, APIs for databases, and consensus operations for private blockchain networks.

In some embodiments, there is provided generating a logical query plan for a query that includes a single fact table and multiple reducer tables; optimizing the logical query plan for query execution; and executing the optimized logical query plan.

The invention discloses a gallium nitride welding energy control method and system, and belongs to the technical field of gallium nitride welding. The method comprises the steps that original welding parameters during operation of the gallium nitride laser transmitter are obtained, a landmark area is obtained according to the original welding parameters and calibrated as welding body real-time monitoring data and internal path load data, and a welding period is equally divided to construct welding nodes; a full-process closed-loop system of data acquisition, analysis and decision, feedback execution and data precipitation is constructed through a welding energy control center; real-time monitoring and dynamic regulation provide an instant response basis for energy accurate matching, efficient utilization of energy accumulates a large amount of high-quality welding data for a system, and a historical database is fed back to optimize threshold parameters, such as internal and external temperature thresholds of a welding body, power upper and lower limit coefficients and the like; and a multi-module collaborative intelligent closed-loop management system is constructed, and continuous optimization and upgrading of the welding process are promoted.

The invention provides a database optimization method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: collecting a plurality of first configurations corresponding to a first database and first performance data corresponding to the plurality of first configurations; constructing a first probability model based on the plurality of first configurations and the first performance data corresponding to the plurality of first configurations, and determining one or more first parameter values corresponding to the first parameter set based on the first probability model; the one or more first parameter values are used for maximizing the first conditional probability; taking the one or more first parameter values as an initial solution of a first optimization problem, and solving the first optimization problem based on a set optimization algorithm to obtain one or more second parameter values; the first database is configured based on the one or more second parameter values.

The meta generation and database optimization device using questioner information disclosed herein includes a processor and a memory storing at least one instruction executed by the processor, the at least one instruction being configured to cause the processor to perform a natural language query transmission stage in which a user inputs a question and the result is transmitted to a transformation matrix via a result platform; a question processing stage in which the transformation matrix analyzes the natural language question via a large-scale language model and generates a meta natural language question by referring to related data from an embedded vector store; and a meta information generation stage in which a database query is generated based on the meta information generated by the optimal database generation unit to derive optimized results.

The invention discloses a financial security transaction system with big data encryption, and relates to the technical field of financial transaction security, and the financial security transaction system with big data encryption adopts a hybrid encryption algorithm, combines AES-256 symmetric encryption and RSA asymmetric encryption, improves the efficiency through symmetric encryption during data transmission, and improves the security of financial transactions. The security of the key is ensured by means of asymmetric encryption, and the key is managed by means of the block chain, so that the integrity and traceability of the key are ensured. Through big data analysis and machine learning technologies, transaction behaviors are monitored in real time, a behavior model is constructed to early warn risks, and an external risk database optimization model can also be connected. Encrypted data are stored by using a distributed storage technology, and are backed up regularly and remotely, so that data reliability is guaranteed. User identity authentication and authority management are enhanced by using multi-factor identity authentication and role-based access control. The system greatly improves the safety of financial transaction data, effectively prevents and controls risks, and guarantees the safe and efficient operation of financial transactions.

The invention provides a database optimization method and system based on photon energy parameters, and relates to the technical field of photon energy parameter data processing. The method comprises the steps of performing clustering processing based on key parameters on photon energy data in a database to form photon energy clustering result data; performing photon energy parameter-based compression feature extraction on the photon energy clustering result data to form photon energy compression feature data; and performing data association processing aiming at the growth trend according to the photon energy compression characteristic data to form height intervention association data. According to the method, reasonable body height intervention systematic data is established by performing data optimization on the photon energy parameter database, and the structural property and effectiveness of the database data are improved.