814 results about "Data Adjustments" patented technology

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The invention discloses a converged communication system and method for emergency command and dispatch, and the method comprises the following steps: S1, constructing a converged communication network environment, and configuring a self-adaptive network switching module; s2, collecting and analyzing task demand data in an emergency scene, and generating a task demand prediction matrix; s3, generating a task scheduling rule; s4, acquiring current network state data, and generating task scheduling constraint conditions; s5, constructing and training a multi-agent reinforcement learning model; s6, optimizing a communication path decision strategy and a task scheduling rule by adopting a self-adaptive multi-objective optimization algorithm; s7, calculating an optimal communication path, and executing intelligent adaptive network switching; and S8, collecting task execution feedback data, and adjusting training parameters of the multi-agent reinforcement learning model. According to the method, multi-agent reinforcement learning and dynamic entropy regulation and control optimization are combined, emergency communication task scheduling and path optimization are achieved, and the method has the advantages of being high in adaptability, high in communication stability and excellent in task execution efficiency.

The invention discloses a bridge structure optimization method based on BIM and a finite element method, and relates to the technical field of bridge structure optimization. According to the method, the finite element analysis model can be more accurately established, and the design precision is improved. Through finite element analysis and a digital simulation technology, structure response data is rapidly obtained, and design optimization is accelerated; the optimization of the final scheme is ensured by a multi-iteration optimization strategy, the extreme load response is predicted by introducing a machine learning algorithm, a high-risk area is identified in advance, and the overall performance is improved; based on a multi-scale analysis method, the durability and safety of the structure are comprehensively evaluated, weak links are identified, and long-term performance is guaranteed; meanwhile, the influence of environmental factors on material characteristics is considered, adjustment is performed according to climate data, the design scheme is closer to the actual environment, and durability is enhanced.

The invention provides a data processing system for cooperative operation of an intelligent combined fleet, and relates to the technical field of data processing, and the system comprises an acquisition module which is used for acquiring basic state data of a ship; the prediction module is used for generating state trend data in combination with the historical motion trail and the near-time data sequence, and the state trend data comprises a speed change rate and a course angle change trend in a future short time period; the sensing fusion module is used for receiving the state trend data of other collaborative ships, aligning the state trend data with the state trend data of the ship, and performing synchronous calculation to generate fleet collaborative prediction data; the adjusting module is used for recognizing an included angle error between the current ship thrust applying direction and the future resultant force direction of the target structure and calculating the attitude adjusting amount; the control output module is used for mapping into a thrust adjusting instruction and sending an execution instruction to a power system of the ship; according to the invention, autonomy and accuracy of fleet collaborative operation are improved.

The invention discloses a real-time behavior monitoring and dynamic optimization system for an advertisement putting strategy, and relates to the technical field of advertisement putting and user behavior analysis. A real-time multi-source user behavior data acquisition module acquires data; the distributed storage and indexing module supports fragmented storage, capacity expansion and multi-dimensional indexing; the behavior feature extraction and analysis module extracts features and identifies a time sequence rule; the putting effect evaluation module compares data through an index system and positions an effect short board; the user portrait dynamic updating module adjusts the label weight according to the latest data; the strategy optimization decision module optimizes parameters in combination with the portrait and the evaluation result; the execution scheduling module issues an instruction and monitors a state; the historical data mining and model training module is used for mining rules and iteratively training the model; and the visual interaction configuration module provides an interface and supports parameter configuration and data, effect and optimization suggestion display. According to the invention, full-scene real-time monitoring and cross-platform identity association of user behaviors are realized, and support is provided for refined advertisement marketing.

The invention relates to a Bluetooth connection method and system, and the method comprises the steps: obtaining Bluetooth identifiers of an earphone and a source device, and extracting a multi-connection strategy from a database; and calculating equipment positions, signal intensities and interference factors among all strategy steps, executing a three-dimensional verification matrix, comparing protocol units, and monitoring a frequency hopping sequence to realize dynamic sequence adjustment. Combining the signal intensity, the success rate and the interference risk to calculate a comprehensive weight, generating an optimized sequence, then dividing multiple subsequences for cross validation, and utilizing historical data to adjust the interference weight and reconstruct a step sequence. Collecting intensity mean values and success rate variances of multiple rounds of tests to construct a normalized fitness function, and selecting an optimal scheme after scoring each strategy. Through a multi-dimensional parameter fusion and iteration verification mechanism, traditional single-path limitation is broken through, and finally, Bluetooth connection is completed by adopting a highest score strategy, so that the problem of equipment interconnection in a complex electromagnetic environment is effectively solved.

The invention relates to the technical field of safety early warning, and discloses a safety early warning method and system based on multi-modal data fusion and a storage medium. The method comprises the following steps: deploying data acquisition equipment in a target environment, acquiring multi-modal data, and preprocessing the multi-modal data; respectively extracting features of each modal data; calculating correlation among different modal features, calculating an attention weight based on a correlation score, and introducing a gating mechanism to adjust the attention weight in combination with historical data; multiplying the adjusted attention weight by each modal data to obtain weighted modal data, and splicing to obtain fused modal data; and generating a training set and a test set based on the fusion modal data, training a safety early warning model, and outputting an early warning result through the test set. And calculating a prediction error according to the early warning result, and updating parameters of the safety early warning model according to the prediction error. The accuracy and reliability of the safety early warning model can be improved, and the false alarm rate is effectively reduced.

The invention discloses an integrated water quality monitoring and processing system for aquaculture, and the system comprises a data collection module which is used for obtaining dissolved oxygen, ammonia nitrogen, pH value, temperature and flow data from a plurality of spatial distributed sensing nodes; the data preprocessing module is used for completing noise removal, correction and time sequence synchronization; the water quality state estimation module is used for outputting real-time multi-point water quality states based on a distributed unscented Kalman filtering algorithm; the space-time fluid modeling module is used for constructing a Lagrange model to extract a flow velocity field and a diffusion coefficient; the scheduling optimization module is used for generating an aeration and dosing scheme by adopting an improved multi-target Pareto optimization algorithm based on a space-time sensitive boundary; the execution module is used for controlling an air blower, an aeration pipe and a dosing pump to complete oxygenation and dosing operations; and the self-adaptive updating module is used for collecting feedback data to adjust filtering gain and optimizing parameters so as to realize closed-loop control of the system.

The invention discloses a training sample data generation method and device, a readable storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: inputting a seed topic and a cue word of a programming language processing task into a sample data generation model; and modifying the seed topic in the modification range boundary condition by using the cue word based on the same algorithm thinking, and generating a test case set covering the constraint condition of the new topic according to the type of the new topic and the case generation condition. Answering the new question passing the verification, and outputting an answer passing the verification of the test case set; taking the new question, the test case set and the answer as training sample data; and adjusting and modifying the range boundary condition and / or the cue word and / or the use case generation condition, and generating new training sample data by utilizing the new question. The problem that training data generated by related technologies cannot meet model training requirements can be solved, and high-quality training sample data can be efficiently generated for programming language processing tasks.

A system for generating a projection structure using a graphical user interface, wherein the system comprises: a display device configured to display a graphical user interface (GUI); a computing device comprising a memory; and a processor, wherein the memory contains instructions configuring the processor to: receive a plurality of datasets, wherein each dataset comprises a plurality of parameters; generate a first projection structure as a function of the datasets; display the first projection structure through the GUI; receive natural language query data corresponding to the first projection structure using a chatbot interface; generate response data as a function of the query data; map, as a function of a look-up table, each categorization to a database entry index; obtain response data from the database; adjust the parameters as a function of the response data; generate a second projection structure; and display it in the GUI.

The invention relates to the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention relates to the technical field of mine equipment monitoring and maintenance, and discloses a mine equipment state monitoring and maintenance method based on the Internet of Things, which comprises the following steps: S1, multi-source data acquisition: synchronously acquiring equipment operation parameters and environmental parameters through vibration, temperature and pressure sensors and environmental sensors deployed on mine equipment; s2, edge calculation processing: performing filtering and anomaly detection on the sensor data at a mine edge gateway to generate a compressed data packet; s3, online learning and model dynamic updating; s4, new equipment is accessed; s5, maintenance decision and resource scheduling; and S6, performing closed-loop feedback optimization. High-error historical sub-trees are dynamically eliminated, new sub-trees are generated, model parameters are adjusted in combination with maintenance feedback data, on the basis that historical effective knowledge is reserved, equipment dynamic features are extracted, accurate matching of prediction results and maintenance strategies is ensured, and the self-learning ability and maintenance decision reliability of the model are remarkably improved.

The invention relates to the technical field of parameter control, and discloses a vibrating screen parameter self-adaptive control method and system based on machine learning. The method comprises the following steps: collecting coal characteristic parameters through an image device and a sensor; calculating a load coefficient and a difficulty index according to the parameters, and adjusting the parameters of the dual-motor vibration exciter to control vibration; the spring pre-tightening force and the screen surface inclination angle are adjusted according to vibration feedback, and the screening track is optimized; and the feeding speed and the baffle height are adjusted based on screening efficiency data, and self-adaptive control is achieved. The technical problems that traditional vibrating screen fixed parameter control cannot adapt to multi-coal-type changes, a vibrating screen control system lacks coal material characteristic real-time sensing and analyzing capacity, and a precise mapping and dynamic optimization mechanism is lacked between vibrating parameters and screening efficiency are solved.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The present application relates to a BIM-based hanging basket design method, computer device and computer-readable storage medium, wherein the design method comprises following steps: a parameter acquisition step; a hanging basket modularization step: decomposing structures of hanging baskets into a plurality of design modules; a selection step; a calculation step: calculating parameters of the design modules using a structural mechanics calculation method and / or a finite element calculation method; preliminary BIM model building step: building models of the design modules and a preliminary BIM model of the hanging basket; an optimization iteration step: converting the preliminary BIM model of the hanging basket into a finite element model for calculation, and feeding calculation results back to the preliminary BIM model for structural adjustment, repeatedly performing iterative calculations to obtain optimized hanging basket parameter data; final BIM model acquisition step: the BIM model of the hanging basket adjusted according to the optimized hanging basket parameter data is a final BIM model of the hanging basket. The design method enables autonomous calculation and design of the hanging basket with good versatility.

The invention discloses a power data security transmission strategy dynamic optimization method based on gray measurement, which comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data in a power system, and generating a time sequence data set and a structure attribute data set; s2, performing privacy protection and noise addition on the time sequence data set and the structure attribute data set by applying a differential privacy mechanism; s3, performing evaluation by adopting a gray scale measurement and calculation method, calculating a gray scale weight and generating a transmission strategy; s4, according to the gray scale weight classification data, adjusting a transmission path and a time sequence; s5, dynamically adjusting the differential privacy noise intensity and the gray scale weight according to the network bandwidth, the transmission delay and the data requirement; and S6, continuously monitoring the transmission efficiency and the privacy effect, and adjusting a transmission strategy and a protection mechanism in real time according to feedback. According to the method, the dynamic collaborative optimization of privacy protection and scheduling efficiency of the power data in the transmission process is realized by fusing a differential privacy mechanism and a gray calculation method.

The invention discloses a microgrid stability control method and system based on output characteristic simulation. The method comprises the following steps: acquiring fluctuation characteristic data, meteorological condition data, operation data, load demand data and environmental parameter data; separating the fluctuation characteristic data to obtain a periodic track and a random disturbance component; performing correlation analysis according to the periodic trajectory, the random disturbance component and the meteorological condition data to obtain potential influence data; according to the potential influence data and the operation data, obtaining an adaptability index; if the adaptability index is lower than a preset index threshold value, the energy capacity and the frequency range are adjusted according to the random disturbance component, the operation data and the load demand data, and dynamic response configuration is obtained; according to the dynamic response configuration, in combination with a preset state switching loss parameter and a preset environment adaptability parameter, generating an initial adjustment instruction template; and generating a control instruction set based on the initial adjustment instruction template in combination with the environmental parameter data, the adjustment frequency and the power parameter.

A method may include obtaining, from a camera device, thermal image data for a steam area of a plant facility. The steam area may include a steam trap for a steam network. The method may further include obtaining plant steam data regarding the steam trap. The method may further include determining pixel data regarding the steam trap using the thermal image data and an image segmentation process. The method further includes determining various temperature values across the steam trap using the pixel data. The method may further include determining predicted steam trap data using the temperature values, the plant steam data, and a machine-learning model. The method may further include transmitting a command that adjusts one or more parameters of the steam network based on the predicted steam trap data.

The invention relates to the technical field of data processing, in particular to a vCPE resource allocation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining system operation data of each monitoring point, inputting the system operation data into a preset anomaly detection model, outputting an abnormal data point, determining a vCPE resource allocation strategy according to the abnormal data point, inputting the system operation data into a preset target prediction model, and obtaining a vCPE resource allocation result. And outputting predicted operation data in a future time period, adjusting a resource allocation strategy according to the data, and adjusting vCPE resource configuration according to the adjusted strategy. According to the method and the device, the limitation that traditional vCPE resource allocation is based on static configuration or simple rule scheduling is broken through, the resource allocation strategy can be dynamically adjusted according to the real-time network condition and the user service requirement, the vCPE resource utilization rate is effectively improved, the network performance is optimized, and the problems of low resource utilization rate and limited network performance caused by an existing resource allocation mode are solved.

The invention discloses an inductor assembling method, which relates to the technical field of electronic components and comprises the following steps: arranging a positioning disc with array holes on a positioning tray bracket, filling magnetic cores into the array holes and then fixing the magnetic cores through a magnetic plate, and detecting the positions of the magnetic cores by a visual system; a glue scraping screen plate covers the positioning disc and is aligned with the positioning disc through a three-point positioning mechanism, and multi-area synchronous gluing is conducted through an intelligent gluing control system; the intelligent gluing control system adjusts gluing parameters according to detection data of the visual system; a pressure sensor array and an elastic pressure mechanism are adopted to synchronously press the glued magnetic core, the pressure sensor array collects pressure distribution data, and the pressure distribution data are processed through a pressure equalization algorithm to control pressure distribution of batch assembly. The invention also discloses a method.

The invention provides a method for improving a cache hit strategy and a response speed, and relates to the technical field of cache management policies, the method comprises the following steps: monitoring the use state of a cache in real time, obtaining a cache hit rate, a cache capacity and a data validity index, and generating a real-time monitoring report of the use state of the cache; according to the real-time monitoring report of the cache use state, a cache management strategy is determined from a preset strategy library, and cache data updating is triggered; and when a data request is received, adjusting cache data according to a cache management strategy, including replacing old data and updating data layout, and recording a final access record and a cache state. According to the method, the cache hit rate and the response speed are improved by monitoring the cache state in real time, intelligently selecting the cache strategy, optimizing data access and prefetching, concurrent processing and dynamically adjusting the cache hierarchy, so that the overall performance of the computer is enhanced.

The invention belongs to the technical field of robot navigation and path planning, and particularly discloses and provides a dynamic navigation planning method and system for a service robot, which overcomes the limitation of single-dimensional analysis in a complex dynamic scene by constructing a dynamic social thermodynamic diagram and deducing group dynamic intention data. The perception precision of the environment structure and the people stream situation is improved; a linkage mechanism among a dynamic social thermodynamic diagram, individual behavior characteristics and path planning is established, and a social cost function is generated to optimize a navigation path according to the traffic efficiency and social etiquette of group future dynamic vectorization evaluation; the actual influence of the navigation behavior of the robot on the environment is monitored in real time, and a planning strategy is adjusted in combination with feedback data, so that the adaptability of the navigation decision and the continuous improvement of the environment acceptance degree are realized.

A system for vendor deduplication. The system creates embeddings of entity names and creates an initial entity graph comprising entities whose embeddings are related. The initial entity graph includes nodes representing the entity names linked together by edge weights indicating their similarity. The system adjusts the edge weights between the entity names according to transactional data related to the entities to create a final entity graph and merges the entity names as a common entity based on the adjusted edge weights in the final entity graph.

The invention relates to the technical field of road engineering detection, in particular to a road disease recognition method and system based on electromagnetic waves and photographic imaging, and the method comprises the steps: firstly obtaining electromagnetic wave detection data and photographic imaging data of a road region, then synchronously detecting the distribution position and thickness of a road thin water film, generating a rasterized water film feature map, and finally obtaining a road disease recognition result. Then, according to the water film characteristic pattern, dynamically correcting bimodal data interference, adjusting a reflected wave amplitude value and a time domain delay parameter for electromagnetic wave detection data, compensating a gray difference and an edge gradient value for photographic imaging data by applying a local reflection suppression algorithm, and finally, fusing the corrected data and associating the state of the water film characteristic pattern to eliminate coupling misjudgment. The system comprises a data acquisition unit, a water film detection unit, a double-domain correction unit and a fusion decision unit, the problem of insufficient identification precision caused by a thin water film is solved, the road disease identification precision is improved, and a reliable basis is provided for road maintenance.

An apparatus including a policy acquisition circuit structured to interpret a vehicle policy data value including vehicle status data; a policy processing circuit structured to generate parsed policy data that includes a vehicle data collection description; a policy execution circuit structured to collect vehicle data from one or more end points of at least one network zone of a vehicle; a vehicle data transmission circuit structured to transmit at least a portion of the collected vehicle data; and a vehicle status data adjustment circuit structured to interpret a vehicle status data collection change value.

The invention relates to the technical field of production safety and intelligent monitoring, in particular to a cosmetic production line safety monitoring method and system based on data analysis, and the method comprises the steps: obtaining real-time operation parameters through a multi-source data collection module, and generating a production environment dynamic mapping graph; extracting a fluctuation trend and abnormal distribution characteristics, and generating a region data adjustment instruction set; adjusting a signal acquisition strategy, and constructing a regional signal alignment state table; identifying the signal intensity variation, and forming a regional signal adjustment mark graph; and finally outputting a monitoring area state evaluation value list. According to the method, the signal fluctuation expression precision and the dynamic regulation and control capability can be remarkably improved, the adaptability and the response sensitivity of safety monitoring in a complex production environment are enhanced, and high-precision local compensation and equipment state quantitative judgment are realized.

The invention relates to the technical field of friction welding machines, and discloses a friction welding machine sliding table control method and device.The method comprises the following steps that movement data of a friction welding machine stirring head are collected in real time, and a stirring head kinetic model is established; based on the mobile data and the dynamic model, outputting a preliminary control strategy through an optimal linear quadratic regulation control algorithm; on the basis of the movement data, future movement data of the stirring head are predicted through a prediction control algorithm, and a preliminary control strategy is optimized; a control signal is sent to a servo motor of the sliding table to control the servo motor; and the difference between the real-time movement data of the stirring head and the prediction result is used as feedback data, and control strategy parameters are adjusted. According to the method, the difference between the real-time movement data of the stirring head and the prediction result is used as feedback, a closed-loop control mechanism is formed, and control parameters are continuously monitored and optimized. The position control precision of the stirring head is improved, the problem of response lag in high-speed welding is effectively solved, the consistency of welding line tracks is ensured, and position deviation is avoided.

The invention relates to the technical field of automatic feed distribution, in particular to an automatic feed production regulation and control method and system.The method comprises the following steps that healthy and growth rate data are collected and input into a data stream, the number of dynamic Bayesian network nodes and a connection mode are adjusted, real-time data are analyzed, and the learning rate and the transition probability are adjusted; and a prediction error matching feedback mechanism is established, key indexes are extracted, a distribution instruction is generated through a fuzzy logic controller, the feed ratio is adjusted to balance cost nutrition and environmental influence, and an optimal configuration result is obtained. According to the method, health and growth data are comprehensively utilized, feed feeding is more personalized and accurate, a feedback mechanism is analyzed and optimized in real time, it is ensured that the feed ratio responds to specific nutritional requirements of animals in real time, accordingly, resource waste is reduced, healthy growth of the animals is enhanced, feeding instructions are finely regulated and controlled through fuzzy logic, and the feeding efficiency is improved. The method adapts to different stages of animal growth, and a particle swarm optimization algorithm is adopted to balance the cost, nutrition and environmental influence.