164 results about "Correlation database" patented technology

The welding path autonomous optimization system includes a quality—welding-condition correlation database in which a constraint condition of welding by a welding robot that operates a welding torch is stored, an interference analysis part configured to determine a welding path that does not interfere with an object to be welded based on three-dimensional CAD data of the object to be welded and the constraint condition stored in the quality—welding-condition correlation database, and a welding-robot-operation / welding-condition output part configured to output welding process information based on the welding path.

The invention discloses a casting density optimization system and method based on cooperative regulation and control of molding sand moisture and clay blue absorption amount, and relates to the technical field of casting control. According to the method, a self-cleaning wear-resistant alloy probe is used for synchronously collecting a molding sand high-frequency eddy current signal and a multi-spectrum near-infrared signal in the sand mixing process; inputting a pre-training support vector machine regression signal fusion model to obtain a real-time moisture content and a clay blue absorption amount; a preset density target threshold value is combined, a density-molding sand parameter association database is inquired to determine a parameter target interval, a regulation and control instruction is generated after a deviation value is calculated, and a water adding device and a clay conveying device are controlled to adjust the use amount; the density of the casting after regulation and control is detected, if the density does not reach the standard, model output weight iteration optimization is corrected until the density reaches the standard, the system comprises a detection module, a signal processing module, a regulation and control execution module, a density detection and correction module and a central control module, closed-loop control is achieved, and the density stability of the casting is improved.

A system and method for predictive control of an algae reactor are disclosed. The method involves receiving correlation data from a correlation database, determining one or more correlations between parameters above a predetermined threshold, querying a parameter database to select associated data, training a machine-learning model based on the correlations and selected data, polling sensor data, predicting future values using the trained model, and instructing the algae reactor to perform required actions based on predicted values exceeding operational thresholds. The machine-learning model is trained to optimize predictions for improved control of the algae reactor.

The invention relates to the technical field of application of the Internet of Things, in particular to a food safety traceability method and system based on the Internet of Things, solves the problems of a traditional food safety traceability method, improves the overall efficiency and effect of food safety management, and provides technical support for food safety. The method comprises the following steps: performing safety detection on food to obtain a food safety quality detection set; comparing the food safety quality detection set with a food safety quality standard set, and extracting and summarizing items with detection results exceeding the standard to obtain a food safety exceeding item set; for each standard-exceeding item, extracting a food stage correlation vector corresponding to the standard-exceeding item from a preset item stage correlation database; and calculating a processing stage correlation coefficient sum, a transportation stage correlation coefficient sum, a storage stage correlation coefficient sum and a sales stage correlation coefficient sum in all the food stage correlation vectors, and sorting the stages according to calculation results.

The invention discloses a wolfberry quality stability evaluation method and system based on a neural network, and the method comprises the steps: collecting wolfberry samples from different producing areas, obtaining the growth environment parameters and harvesting and processing information of the wolfberry samples, and building a correlation database of the producing area environment and the wolfberry quality, analyzing the influence rule of the producing area factors on the quality stability of the Chinese wolfberry through a data mining algorithm to obtain a quality stability static prediction model of the Chinese wolfberry in different producing areas; a long-short-term memory neural network is adopted, a multi-level feature extraction structure is designed, network parameters are optimized through an adaptive learning algorithm, generalization and robustness constraints are introduced at the same time, a quality stability dynamic prediction model is obtained, and the prediction capacity and the anti-interference capacity of the model for new samples are enhanced. According to the method, the problems of unstable quality caused by large climate and soil difference in the wolfberry planting area and high subjectivity of wolfberry quality evaluation are solved, and accurate prediction of the wolfberry quality stability is realized.

The invention discloses an image data management method and system for high-precision size visual inspection, and relates to the technical field of image data, the image data management method for high-precision size visual inspection comprises the following steps: S1, collecting a data set, and carrying out region segmentation processing; s2, correcting and enhancing the data set, and extracting contour feature parameters; s3, mapping association is carried out, and screening classification is carried out; s4, parameters are optimized and adjusted, and a standardized size detection feature set is generated; s5, constructing a size detection model to obtain a target size quantification detection result; and S6, performing accuracy verification, and establishing an image feature size parameter association database. According to the invention, through synchronous acquisition of a multi-view image data set and a physical size calibration data set, and in combination with calibration reference of a geometric quantity standard appliance, the problems of incomplete information and large physical size mapping deviation of a traditional single-view image are solved.

A structure authentication system includes a sensor configured to sense identifying features of a three-dimensional (3D) printed structure and generate identifying feature data based on the identifying features, as well as a processor configured to receive, from the sensor, the identifying feature of the 3D printed structure. The processor is also configured to analyze the identifying feature data along with a non-fungible token (NFT) database that correlates a plurality of NFTs to a plurality of identifying feature data to determine whether the 3D printed structure is associated with an NFT within the NFT database. Additionally, the processor is configured to query a blockchain to determine whether the NFT is authentically associated with the 3D printed structure. The processor is also configured to, in response to determining that the NFT is authentically associated with the 3D printed structure, send a notification to a user device indicating that the 3D printed structure is authentic.

The invention relates to the technical field of portrait analysis, and discloses a space-time portrait analysis method for personnel and places based on perceptual data, which comprises the following steps: summarizing perceptual data captured by a camera and access control perceptual equipment; preprocessing the grabbed sensing data; behavior characteristics in portrait and vehicle track data in the perception data are extracted and classified, sorted and summarized, and a related database is established; according to the extracted data, setting association relationships among personnel, vehicles and places in different dimensions in a period; aggregating association relationships between the personnel and the places; and space-time portraits of personnel and places related to the business are constructed. According to the method for analyzing the space-time portraits of the personnel and the places based on the perceptual data, complete space-time tracks can be extracted, continuous and complete space-time information can be formed, the behavior modes and activity rules of the personnel can be reflected more accurately, deeper behavior analysis ability is provided, and the accuracy and comprehensiveness of the relation portraits are improved.

The invention discloses a highway bridge damage assessment method based on specific traffic flow and structure monitoring response, which comprises the following steps: firstly, capturing traffic flow information, acquiring real-time dynamic response data of a bridge structure under the action of environmental excitation, and carrying out space-time matching on the traffic flow information and structure response monitoring data to obtain a real-time dynamic response result; constructing a vehicle load-structure response association database; classifying the vehicles of the same type into the same layer for classification, sampling from a database by adopting an optimal distribution sampling method according to different vehicle types, and sampling from the database to obtain three-layer specific traffic flows of small cars, medium-sized trucks and heavy-duty trucks and corresponding structure monitoring response characteristic values; and carrying out weighted average on the specific traffic flow structure monitoring response characteristic values obtained by each layer, calculating the overall structure response under a single-day specific traffic flow load, and carrying out bridge safety evaluation through long-term monitoring. The method can replace a traditional bridge load test, and rapid and real-time safety assessment of the highway bridge is achieved under the condition that traffic is not interrupted.

The invention discloses a square billet on-line fixed-length and fixed-weight adjusting method based on a visual technology, which comprises the following steps of: acquiring an end face image of a square billet through a high-resolution industrial camera, calculating the area of the end face, combining parameters such as steel rolling weighing data, pulling speed, steel grade components and the like, establishing an associated database, and dynamically adjusting the cutting fixed-length length of the square billet. The problem that in the prior art, the fixed-length and fixed-weight precision is insufficient due to changes of steel grade components, the pulling speed and the like is solved, high-precision fixed-length and fixed-weight control is achieved, and the device has the advantages of being non-contact, resistant to high temperature, high in accuracy, high in adaptability and the like. The machine vision technology is applied to end face area detection and calculation and introduced into fixed length and fixed weight of a continuous casting billet, the fixed length of the square billet is dynamically adjusted according to the change value of the end face area of the square billet, the target of the same standard fixed weight of each square billet needed by a steel rolling mill is achieved, and the target of 2kg / t control precision can be achieved.

The invention discloses a molten metal fluidity improving method and system in the V-method casting process, and relates to the technical field of casting, a high-pressure airflow and mechanical brush composite mode is adopted for cleaning a casting mold cavity and preheating the casting mold cavity, and precoated sand is selected for preparing a sand mold through vacuum negative-pressure compaction; a sectional type negative pressure regulation and control strategy is adopted, and the stepped composite pouring gate structure is optimized; degassing the molten metal by argon or nitrogen, controlling the temperature, and adding an inoculant and a flow promoter in proportion; the casting speed is controlled according to the three-section type rate through the casting table with the adjustable inclination angle; arranging an insulated riser at the thick and large part of the casting, maintaining a negative pressure field after casting, and covering an insulating material; dividing cooling areas according to wall thickness, and gradually relieving pressure after cooling to a set temperature; and after shot blasting and flaw detection, recording data and establishing a process parameter-flowability association database. Through mold preheating, negative pressure gradient regulation and control and pouring gate design improvement, the molten metal fluidity is remarkably improved, and the casting quality stability and the production efficiency are greatly improved.

The invention discloses a chronic wound nursing scheme recommendation method and system based on clustering and a knowledge graph, and relates to the technical field of chronic wound nursing, chronic wound nursing associated data is collected to obtain chronic wound nursing multi-source data, and the chronic wound nursing multi-source data is preprocessed; and analyzing the preprocessed chronic wound nursing multi-source data, clustering the core demand problem and the nursing scheme data, and establishing a correlation database between the core demand problem and the nursing scheme. Through a technical path from multi-source data fusion to semantic cluster generation by layer clustering to cluster-level map construction to dynamic semantic matching, three major pain points of knowledge fragmentation, inaccurate demand matching and lack of recommendation basis in chronic wound nursing are solved. Compared with a traditional method, image volume accumulation captures multi-dimensional association, and semantic noise is reduced through cluster mapping; the evidence chain runs through the whole process of problems, schemes, wound types and literature sources.

The invention relates to the field of data processing technologies and financial science and technology, and provides an HQL statement detection method which comprises the steps that an HQL statement to be detected is analyzed to generate an abstract syntax tree and optimized, and an execution plan is generated according to the optimized syntax tree; performing operation step decomposition on the execution plan according to task process stages, and generating a performance analysis syntax tree corresponding to the HQL statement by taking operation steps obtained after decomposition as syntax tree nodes; querying a database according to a query object contained in the operation steps in the performance analysis syntax tree to obtain a related database table of the HQL statement, and calculating data of the related database table and data of the operation steps of the performance analysis syntax tree to obtain numerical values of a plurality of preset data indexes of the HQL statement; and calculating the score of the HQL statement according to the numerical value of the preset data index and a preset data index scoring algorithm, and generating a detection report of the HQL statement. According to the HQL statement detection method, the quality of the HQL statement can be automatically evaluated according to a unified rule.

The invention discloses a bridge engineering bidding and bidding quotation risk early warning and cost dynamic regulation and control system and method. The system comprises a data acquisition module, a data preprocessing module, a risk early warning module, a cost dynamic regulation and control module, a linkage execution module, a data storage and update module and a man-machine interaction module. On one hand, a multi-dimensional data acquisition system of bridge exclusive basic data, market dynamic data, policy and regulation data, bidding transaction data and construction pre-judgment data is covered, cooperative operation of static feature risk association factor extraction and dynamic time sequence risk change trend capture is realized, and the accuracy of risk assessment is improved; and meanwhile, a risk-cost closed-loop linkage mechanism is established, a cost-risk association database is constructed, and differential cost regulation and control strategies are formulated in combination with management and control targets of different stages of bidding and tendering, so that the technical problems of low regulation and control efficiency and poor adaptability are solved.

The invention relates to the technical field of industrial detection, and discloses a lithium niobate metasurface defect intelligent identification method based on reactive ion beam etching, which systematically solves the problem of scarcity of experimental data through physical driving simulation and a physics-based rendering algorithm, and provides large-scale field specific training data for a deep learning model. The bidirectional coupling simulation model generates defect morphology prediction data conforming to an etching dynamics law based on real process parameters and material parameters, the prediction data is converted into a synthetic image with real microscopic image statistical characteristics by a physical rendering algorithm, and the quality and consistency of the synthetic data are ensured by an automatic labeling and statistical verification process. According to the method, the number of samples of the ternary association database is multiplied, and extreme process conditions and rare defect modes which are difficult to obtain through experimental collection are covered.

A method and an appliance for anomaly inspection based on correlations of packets are disclosed. The appliance (1) includes a processing unit (13), a first communication channel (C1), and a second communication channel (C2), wherein the first communication channel (C1) transmits a first packet under a first communication protocol (16) and the second communication channel (C2) transmits a second packet under a second communication protocol (17). The processing unit (13) performs a field breakdown procedure to the first packet and the second packet to respectively obtain multiple packet fields of the two packets, matching relevant fields of the two packets based on a correlation database (15) and computing the correlation of the relevant fields. Therefore, an alarm is made when the correlation of the relevant fields is different from an expected correlation type.

The invention provides a steel rail flash welding process detection and evaluation device, the device comprises a data detection module, a process analysis module and a quality inspection expert module, the data detection module is installed on a steel rail flash welding machine, and the data detection module transmits data with the process analysis module and the quality inspection expert module through wireless transmission. According to the invention, through the sensor, the communication module and the storage module which are installed on the rail welding equipment, field welding data are obtained in real time and a related database is established. Meanwhile, a welding model analysis module based on mechanical learning, a neural network and a fuzzy control algorithm is established, control over heat input in the welding process and the steel rail burning amount is achieved, the actual heat requirement is more effectively matched, and therefore the quality of a steel rail flash welding head is improved; and by comparing the actual deformation amount, the deformation resistance, the deformation speed and the predicted amount at the end of welding, the quality of the welding joint is evaluated by applying a machine learning or neural network algorithm, so that quantitative analysis of the quality of the joint is realized.

The invention discloses an outdoor box video monitoring method, device and system based on multi-dimensional fusion. The system has the advantages that the visible light camera adopted by the main lens collects the physical state of the surface of the box body and the peripheral visible dynamic state, and the short-wave infrared equipment is adopted by the auxiliary lens to collect invisible characteristics and temperature distribution; feature points are extracted to calculate similarity, pixel-level matching is realized through a constructed perspective transformation matrix, and a correlation database is constructed; based on the multispectral data, three types of interaction events of biological interference, natural action and environmental transaction are identified in combination with deep learning, a fault and event association data set is constructed, a weight is assigned, a baseline threshold is set, and early warning is triggered when the threshold is exceeded; after early warning, the starting time and position of an event are positioned, a risk network is constructed to calculate the contribution degree of the event to determine a core risk source, three levels of risks are divided, a traceability map is generated, a differentiated operation and maintenance scheme is generated, monitoring is optimized, and the accuracy and operation and maintenance efficiency of outdoor box monitoring are improved.

A system and method for predictive control of an algae reactor are disclosed. The method involves receiving correlation data from a correlation database, determining one or more correlations between parameters above a predetermined threshold, querying a parameter database to select associated data, training a machine-learning model based on the correlations and selected data, polling sensor data, predicting future values using the trained model, and instructing the algae reactor to perform required actions based on predicted values exceeding operational thresholds. The machine-learning model is trained to optimize predictions for improved control of the algae reactor.

The present invention relates to a system (1) which enables the time series of usage rates in the subsequent time interval to be estimated by determining the characteristics of the relevant database servers (V) by retrieving data in the form of usage rates, number of active queries, prolongation time of queries, total resource ratio parameters consumed by queries in database servers (V) in certain periods and processing them with an artificial intelligence model; the estimation results to be monitored by authorized persons through an interface (2); authorized persons to perform resource recommendations and, if needed, to perform resource (CPU and RAM) optimization on servers.

The invention discloses a water conservancy industry data index management method and device and a medium, and belongs to the technical field of big data. The method comprises the following steps: connecting a plurality of water conservancy related databases to obtain business requirements, business indexes and technical indexes in the plurality of water conservancy related databases; processing the business requirements, the business indexes and the technical indexes based on a preset natural language processing algorithm to generate a standard index database; wherein the standard index database comprises a business database and a technical database; obtaining a to-be-processed business demand, and processing the to-be-processed business demand and the standard index database based on a natural language processing algorithm to generate a to-be-processed technical index; managing the to-be-processed technical indexes based on a preset index management platform; and evaluating the to-be-processed technical index based on a preset index evaluation standard. According to the method, the water conservancy data among the multiple databases can be integrated, the index definition is unified, and the reusability of the water conservancy data is improved.

The application discloses a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, relates to the technical field of ocean dynamic disaster simulation, determines the ocean dynamic disaster characteristics of a nearshore research area, integrates multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructs a time-space correlation database; based on a three-dimensional ocean and a third-generation sea wave model, a nearshore storm surge-sea wave coupling numerical model is established and verified. Through the construction of the storm surge-sea wave coupling numerical model, the present application comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear models, and combines the real-time feedback of the three-dimensional dynamic model of the disaster-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

The application discloses a sea wave missing data prediction method based on a double-variable long short-term memory algorithm, and specifically comprises the following steps: firstly, acquiring buoy data A, using a machine learning method to interpolate the continuously lost buoy data, using a regression model method to interpolate the single lost buoy data, and forming a new buoy data set B; using a sea wave variable correlation database, finding out the variables matched with the prediction variables in the buoy data set B and constructing a double-variable set V, and simultaneously determining the training set and the verification set of each double variable; inputting the training set data of each double variable into the long short-term memory algorithm for training, obtaining the corresponding double-variable LSTM model, and finally testing the model results by using the verification set data and determining the optimal prediction model. The method can interpolate and correct the short-term missing data of the buoy, can also predict the long-term missing variables, and effectively improves the accuracy of the buoy data.

According to a molecular comparison system and a method thereof provided by the present disclosure, after a substance analysis result is obtained, on the basis of a molecular list and matching degree values as well as related questions and corresponding weights in a knowledge management system, a correlation database is used to determine and calculate by means of an AI LLM, so as to further determine the molecular species of a compound causing damage.

The application discloses a kind of based on digital twin and AI prediction's asphalt concrete mixing station dynamic hot regeneration regulation and control system and method, it is related to road engineering material preparation and intelligent control technical field.System includes multi-source heterogeneous data perception module, hot regeneration AI prediction model module, digital twin body construction module and multi-parameter collaborative dynamic regulation and control module;Data perception module is equipped with AI visual identification unit.Method is driven preheating temperature, regeneration agent addition amount and other AI prediction models by AI visual features and real-time sensing data, carries out multi-objective optimization simulation deduction in digital twin body, solves optimal process parameter and issues execution;While building " visual features-process parameters-product quality" ternary correlation database, standard scheme is marked reuse, non-standard scheme triggers model retraining, the application realizes the multi-parameter collaborative dynamic regulation and control and self-evolution optimization of plant mixing hot regeneration whole process, effectively improves the quality stability of regeneration mixture and energy utilization efficiency.

The automobile remote detection and fault diagnosis data intelligent processing system of the present application provides automobile remote data processing solutions from three aspects: data parsing, loading and storage. The first is data parsing: defining the parsing rules of ASC files, DBC‑c files, and Report‑c files, and proposing parsing methods for different signal types of evolutionary calculations, establishing the parsing process of corresponding messages, and orderly and efficient vehicle data parsing; the second is data loading: constructing a data loading method suitable for different data types, with less data loading delay and good real-time performance; the third is data storage: building a distributed cluster based on the database Greenplum architecture, and building a system module-related database system; using indexing, distribution, partitioning and marking field methods to improve query speed and system security. The data storage and extraction speed is fast, query retrieval is easy, and remote data processing is efficient, which strongly supports the development of automobile detection and fault diagnosis systems from the perspective of data processing.

This disclosure provides techniques for detecting / inhibiting corrosion of in distribution / recirculation service lines and for using tin dioxide as a surfactant to coat at-risk piping surfaces. For example, disclosed techniques can be used to correlate corrosive conditions with fluid type, source and other environmental parameters, and to treat piping so as to insulate corroding areas. A stannous dosing system, and optionally, associated downstream filtration, can be used to efficiently deliver tin throughout piping surfaces at interest, and thereby facility local tin dioxide surfactant buildup to target thicknesses, at difficult to reach locations. In one embodiment, disclosed systems can be used to build a correlation database that can be used in the automated control of systems taught herein.

The invention relates to a stability on-line analysis method and system for a power system, and the method comprises the steps: building the correlation between the operation parameters of a plurality of power devices in a target power system through the historical operation data of the target power system, so as to judge whether the power devices are in operation or not. The change of the operating parameter value can cause the change rule of the operating parameter value of other power equipment. And constructing a correlation graph and a correlation database by using the rule. And before the operation parameters of the power equipment at the current moment are utilized to perform system stability analysis, the association map and the association database are utilized to verify the values of the operation parameters of the multiple pieces of power equipment, and whether the values of the operation parameters meet the operation rules of the association map and the association database or not is checked. And if not, inferring that the sensor has a fault, and re-acquiring the operating parameters. Parameters collected by the sensor are verified according to the rule, so that wrong data are prevented from being input into the model, and a wrong analysis result is obtained.

The invention relates to a control method for a continuous casting process of stainless steel, which comprises the following steps of: acquiring first information of defects of a continuous casting billet and time sequence parameters of a casting process of the continuous casting billet, and establishing a correlation database of each defect and the corresponding time sequence parameters according to the first information and the time sequence parameters; according to the correlation database, obtaining a model in which each defect is correlated with the time sequence parameters; the model comprises the following second information: an influence weight of a time sequence parameter corresponding to each defect on the defect, a cause path of each defect and a confidence coefficient corresponding to the cause path; and controlling the continuous casting process of the stainless steel according to the model to reduce the generation of defects. The method is used for continuous casting production control, and the purposes of producing high-quality stainless steel continuous casting billets, reducing the grinding ratio of the continuous casting billets and reducing the stainless steel production cost can be achieved.

The application provides a kind of intelligent semiconductor wafer spray control method and system, comprising: according to the material and surface roughness of the wafer, obtain the recommended cleaning time range of the wafer from the preset spray time-wafer attribute correlation database;The sub-area spray parameters obtained in the first three steps are integrated to form a wafer surface cleaning scheme, the cleaning scheme includes the nozzle layout, nozzle quantity, spray liquid composition and temperature of each sub-area, and the cleaning scheme is transmitted to the spray control unit;After cleaning, compare the wafer surface image with the preset standard image, calculate the similarity of the two, if the similarity is lower than the preset threshold, the number and cleaning parameters of the wafer are recorded in the abnormal cleaning log, and the wafer is transferred to the unqualified product buffer area, the spray control of the application improves the cleaning efficiency and quality, reduces the waste of resources, and provides an advanced cleaning solution for semiconductor manufacturing.