120 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 relates to the technical field of database optimization, in particular to a slow SQL (Structured Query Language) statement optimization method and system, electronic equipment and a storage medium, the slow SQL statement optimization method comprises the following steps: preprocessing a collected target slow SQL statement to obtain multi-modal data of the target slow SQL statement; inputting the multi-modal data of the target slow SQL statement into the multi-modal large model, and performing feature extraction and fusion to generate comprehensive semantic representation; generating an optimization suggestion of the target slow SQL statement according to the comprehensive semantic representation; the optimization suggestions are verified, and then the optimization suggestions which are verified to be qualified are executed in a database. Through multi-dimensional data acquisition, large-model deep analysis and automatic strategy execution, full-life-cycle quality detection and optimization of the slow SQL statement are realized.

The invention provides an intelligent inspection method and device for a database, and belongs to the technical field of databases. The method comprises the following steps: acquiring a multi-modal data set corresponding to the operation state of the inspection database by presetting a multi-source heterogeneous data acquisition mode; based on a cross-modal attention mechanism and a self-learning model, establishing a multi-dimensional data association relationship corresponding to the multi-modal data set, and in combination with historical time sequence data and unsupervised anomaly detection, determining a database health value and anomaly early warning information; wherein the multi-dimensional data association relationship is at least established among the SQL performance, the resource consumption, the transaction behavior and the business load. And generating a database optimization scheme according to the database health value, the abnormal early warning information and a preset feedback optimization mechanism, and sending the database optimization scheme to the user terminal. According to the scheme, the database is comprehensively inspected in combination with the cross-modal attention mechanism, the self-learning model, the knowledge graph association reasoning model and the time sequence prediction model, and the automation, dynamic and intelligent levels of inspection are improved.

The invention discloses a substation equipment state intelligent sensing and early warning method based on multi-source data. The method comprises the steps that real-time operation data and historical abnormal state data of substation equipment are acquired; obtaining time sequence data based on all historical abnormal state data to form a reference template; comparing the real-time operation data of the substation equipment with the reference data based on a dynamic time warping algorithm, and calculating the similarity; judging whether the current running state is abnormal or not based on a similarity result, if yes, classifying an abnormal type, and analyzing and predicting a future running trend; and uploading the newly appearing exception to a database, and optimizing the algorithm. The method has the advantages that the real-time data and the historical abnormal data are combined, the operation state of the equipment is monitored in real time, the abnormity is accurately identified and classified based on the dynamic time warping algorithm, potential faults are predicted in advance, scientific basis is provided for equipment maintenance and management, and sudden faults are 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 invention relates to the technical field of database optimization, provides a massive astronomical data query processing method, and aims to solve the efficiency bottleneck of astronomical data in storage, management and query processes. By constructing a distributed spatial database, efficient storage and management of large-scale astronomical data are realized; an index structure of astronomical data is optimized by utilizing an LB-ANN index, so that query load balance and data retrieval efficiency are improved; besides, efficient fusion of structured query and unstructured query is realized through SQL-ANN mixed query in combination with a JIT compiling technology, the query execution sequence is optimized, and the query efficiency is remarkably improved; the overall method breaks through the bottlenecks of traditional astronomical data query in calculation complexity, storage efficiency and index optimization, and an accurate and efficient solution is provided for efficient intelligent processing of large-scale astronomical data.

The invention discloses a database optimization method and device based on a large model, equipment and a medium, and relates to the technical field of database performance optimizing.The method comprises the steps that a target SQL query is analyzed to obtain a target abstract syntax tree corresponding to the target SQL query, and candidate execution plans corresponding to the target SQL query are determined, determining a target feature vector of each candidate execution plan based on the target abstract syntax tree; training a preset deep learning model based on historical SQL query to obtain a target cost evaluation model, and determining a plan evaluation result corresponding to each candidate execution plan based on the target feature vector by using the target cost evaluation model; and determining a target execution plan from the candidate execution plans according to the plan evaluation result and a preset optimization plan selection mechanism, and executing the target SQL query based on the target execution plan by using the target database. In this way, the prediction precision of the SQL query execution cost can be remarkably improved.

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.

The invention discloses a database performance optimization method based on partition management. The database performance optimization method comprises database storage optimization and key query table efficiency optimization. The database storage optimization comprises the steps of confirming the partition data volume and creating a corresponding partition large file table space; verifying whether the table can be partitioned; creating a temporary table which is the same as the original table, and creating partitions and partition names according to company column values; copying the data to a temporary table; copying the dependent object; partitioning the original table according to a list rule, and emptying data in the original table; synchronizing the data, the trigger and the index information in the temporary table to the partitioned original table; checking whether the partitioned data is consistent or not, verifying whether front-end query is influenced or not, checking index parameters, adopting the database optimization method, dividing the data into a plurality of partitions, mapping key large table data into different partition tables, reducing the data size of a single table, improving the efficiency and availability of the system and ensuring the stability and reliability of the system.

The invention discloses a real module matching and database optimization method for designing a high-strength silicone oil shock absorber, and particularly relates to the technical field of data processing. By combining structural parameters and application conditions of the shock absorber, a preliminary mechanical simulation system integrated with the coupling model is established, a preliminary true module is formed, actual measurement damping data under multiple temperatures and multiple rates are obtained through a dynamic loading test, iterative correction is carried out through an advanced parameter optimization algorithm, high-precision true module matching is completed, and the actual measurement damping data are obtained. Real module parameters obtained through optimization under different design schemes and a corresponding damping performance index system are filed into a database, and data reliability and retrieval efficiency are continuously improved through database internal data quality evaluation and intelligent optimization; accurate prediction and efficient design of the damping performance of the silicone oil shock absorber under the extreme temperature and high-speed working conditions are achieved, and the design consistency, adaptability and engineering application reliability of the shock absorber are greatly improved.

The invention relates to the technical field of distributed database optimization, and discloses a real-time database distributed query optimization method based on memory computing, comprising the following steps: modeling query delays, the query delays comprising computing delays and communication delays; and based on the modeling result of the query delay, the query progress and the resource allocation strategy of each node are determined, each node is a single calculation unit in the distributed database, the calculation delay of the node is inversely proportional to the resource allocation and query load of the node, and the communication delay between the nodes is determined by the data transmission quantity and the bandwidth between the nodes. Through an optimal control theory, a game theory model and a self-adaptive control mechanism, dynamic allocation of calculation and communication resources, collaborative optimization of resources among nodes, self-adaptive adjustment of query loads and resource scheduling optimization driven by historical data are realized, the query execution efficiency is effectively improved, and the overall query delay is reduced.

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 database optimization method and system based on artificial intelligence, and belongs to the technical field of database management, the implementation of the method comprises: data collection: collecting operation data of a database, including query logs, performance indexes and system resource use conditions; intelligent analysis: analyzing the collected data by using a machine learning algorithm, and identifying performance bottlenecks and optimization points; optimization strategy generation: automatically generating an optimization strategy according to an intelligent analysis result; optimization execution: applying the optimization strategy to a database system, and monitoring an optimization effect; and feedback learning: carrying out self-learning and adjustment according to an optimization execution result so as to improve the accuracy of a future optimization strategy. The method can improve database performance, realize automatic optimization, adapt to dynamic change, enhance user experience, lower technical threshold, improve resource utilization rate and realize sustainable development.

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 field of warehouse management, in particular to an intelligent goods shelf system based on the Internet of Things technology, which comprises a cabinet body, a plurality of supporting plates are horizontally arranged in the cabinet body along the vertical direction, and a plurality of groups of movable trays are horizontally arranged at the top ends of the supporting plates along the length direction of the supporting plates; a conveying module for driving the tray to move is fixedly arranged on the supporting plate, a detection unit for detecting the position of the tray is arranged on the supporting plate, a lifting unit for driving the tray to ascend and descend is fixedly arranged on the side portion of the cabinet body, and a control panel is fixedly arranged on the side portion of the cabinet body. Space can be fully utilized, full-automatic accurate control is carried out when goods are stored and taken, manual intervention is reduced, meanwhile, data can be uploaded to a cloud database, an inventory management strategy is optimized, the position and state of the tray can be monitored in real time, goods can be conveniently operated, and the working efficiency is improved. And remote monitoring and management can be realized through Internet connection, so that the intelligent level of the system is improved.

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 database optimization, provides a database optimization method based on artificial intelligence, and aims to solve the problems of low efficiency, high resource consumption and insufficient privacy protection in a traditional query method. According to the method, query tree construction, semi-connection traversal, aggregation query and noise addition technologies are combined, the query efficiency is remarkably improved, the Laplace noise and confusion circuit privacy protection technologies are utilized, the security of sensitive information is protected, and the effectiveness and reliability of a query result are ensured; through accurate feature extraction and intelligent decision tree optimization, a query method is automatically selected and optimized, and high precision and low resource consumption are realized in a complex query scene with a large data volume; the method provides efficient and safe technical support for intelligent analysis and decision-making of college employment planning data, and has a wide application prospect.

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 present invention discloses a smart vehicle audit method and system based on big data edge computing equipment, which relates to the field of smart vehicle technology, including collecting environmental information and vehicle operation status data through sensors on smart vehicles, and preprocessing the data; based on the identified situational features, fog nodes collaborate with cloud databases to generate audit strategies for specific situations; sending the audit strategy instruction set back to the smart vehicle, the vehicle implements strategy adjustments and feeds back effect data; the fog node receives feedback data to evaluate strategy performance, optimizes the existing audit strategy based on the evaluation results, and sends the optimized strategy to the smart vehicle. The present invention optimizes the personalized strategy of smart vehicles by integrating edge computing and cloud databases, greatly improving environmental adaptability, driving safety and decision-making efficiency.