5264 results about "Data quality" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses an archive data security integration management system, which relates to the technical field of knowledge maps and comprises an archive data acquisition module, a knowledge intelligent analysis module, a knowledge map dynamic construction module, a security situation evaluation module and a security communication center module. The data acquisition module structurally acquires metadata and operation behaviors, the intelligent analysis module dynamically governs data quality, the map construction module constructs a three-dimensional security model, the situation evaluation module quantifies security influence and generates a strategy, and the communication center module realizes dynamic identity authentication and bandwidth allocation. Through the knowledge graph technology, comprehensive integration and safety management of the archive data are achieved, the data quality and utilization efficiency are improved, dynamic evaluation and coping with safety threats are achieved, and the safety and integrity of the archive data are ensured. Meanwhile, through dynamic identity authentication and bandwidth allocation, the safety and efficiency of cross-domain communication are improved.

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention discloses a customer service data quality inspection method and device based on dynamic reasoning, equipment and a medium, and the method comprises the steps: analyzing customer service dialogue data, and generating structured dialogue data containing an intention label and a key problem node; according to an intention label in the structured dialogue data, a framework is generated through retrieval enhancement, and matched domain knowledge is recalled in real time from a standard knowledge base so as to construct a dynamic context knowledge graph; taking the dynamic context knowledge graph as input, utilizing a thinking chain prompt template to guide a large model to carry out step-by-step logical reasoning, and outputting a preliminary quality inspection conclusion chain; performing retrieval verification on assertion nodes in the initial quality inspection conclusion chain to generate a traceable final quality inspection conclusion; and based on the final quality inspection conclusion, calculating a quantitative score of the customer service dialogue data and generating a visual thinking chain report. And through dynamic retrieval of the knowledge base and logical reasoning, the accuracy, interpretability and adaptability of customer service data quality inspection are improved.

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention belongs to the technical field of medical data processing, and discloses a medical data multi-source fusion verification and correction method and system, and the method comprises the steps: obtaining multi-source data; constructing a dynamic baseline model of the emergency department, the ICU and the common ward, responding to and verifying the abnormal condition of the patient data, and performing early warning; establishing a term library, carrying out multi-source fusion on the patient data obtained from multiple sources, and carrying out data correction; and generating a multi-source data quality evaluation table of different departments based on the whole-process data, and feeding back and updating the term library and fusing the correction weight based on the quality evaluation table. Through a real-time monitoring and intelligent early warning mechanism, quality problems in the data can be found and corrected in time, human errors are reduced, and the data management cost is greatly reduced. Through multi-source data fusion and consistency verification, the system can ensure seamless connection of data from different data sources, and cross-system and cross-platform data consistency and cooperative work are realized, so that the accuracy of medical decision and the reliability of data are improved.

The invention provides an intelligent data quality monitoring method and system. The method comprises the following steps: performing data processing on multi-source heterogeneous data by utilizing a unified data model; inputting historical data as a training set into the pre-training model for training optimization to obtain an optimized pre-training model, and performing threshold determination on the standardized distribution data stream by using the optimized pre-training model in combination with an adaptive threshold module; inputting the processed abnormal event list into a context awareness and dynamic priority management module to carry out de-duplication optimization transmission; carrying out root analysis on the abnormal event list after the de-weighting optimization by utilizing a root analysis tool; generating a repair strategy for the root analysis result by using the assistance of a repair suggestion engine; and inputting the repair strategy and the visual report into a cross-platform monitoring interface, and generating a repair suggestion and an early warning notification. According to the method, the data consanguinity map is constructed based on the graph database, minute-level traceability of abnormal events is achieved, and the troubleshooting time is shortened by 50%.

The invention belongs to the technical field of electromechanical equipment intelligent monitoring and operation and maintenance management, and discloses an electromechanical system health monitoring and optimization method based on artificial intelligence, and the method comprises the steps: building a digital twin architecture based on a simulation model, reasonably arranging a multi-source sensor, and carrying out the optimization of the health of an electromechanical system; key equipment is fully covered; the system acquires operation data at high frequency and synchronously transmits the operation data to the cloud through multiple channels to realize real-time monitoring; the collected data is cleaned and preprocessed to remove abnormities so as to ensure the data quality; inputting the cleaned data into a neural network, extracting features, and identifying an operation trend and a fault mode; establishing a prediction model in combination with historical data, and constructing a maintenance knowledge base; the equipment state is predicted in real time, and early warning is dynamically generated; through virtual-real synchronization, health and carbon emission states are mapped in real time, equipment operation is intelligently optimized, and cloud closed-loop monitoring is continuously iterated.

The invention discloses a power distribution fusion terminal real-time video reasoning method based on an SDNN, relates to the technical field of intelligent power grid monitoring, and aims to solve the problems of high delay, high bandwidth consumption, low power consumption and the like caused by insufficient utilization of computing power resources of an existing power distribution terminal and dependence on a cloud on video processing in the prior art. And the problem of poor adaptability of the model in a complex power grid scene is solved. Firstly, a power grid special sample library is constructed, and data quality is optimized through time domain key frame extraction and a stratified sampling mechanism; carrying out enhanced training on the lightweight model by adopting transfer learning and multi-modal confrontation; designing an intermediate representation conversion and hierarchical caching mechanism to realize efficient model compiling and heterogeneous hardware deployment; edge-side real-time reasoning is realized based on dynamic frame sampling and statistical threshold detection; finally, a self-adaptive feedback closed loop is driven through reinforcement learning, and incremental updating of the model and dynamic allocation of resources are achieved. The real-time performance of power distribution equipment anomaly detection is remarkably improved, and meanwhile the model memory occupancy rate is reduced.

The invention discloses a data quality intelligent auditing system and method based on a dynamic rule base, and belongs to the technical field of data auditing, and the system comprises a rule base construction module which is used for analyzing business scene parameters through a scene analysis unit according to business scene demands and data type features to generate a rule configuration instruction; the multi-source monitoring engine module is connected to the rule base construction module and is used for collecting multi-source data in real time and loading corresponding checking rules; the automatic verification execution module is used for executing normalized quality verification on the multi-source data based on the verification rule base; and the feedback optimization module analyzes a rule hit rate and a false alarm rate in a verification result through a reinforcement learning algorithm, and dynamically iteratively updates a rule threshold value and a logic combination in the rule base. By constructing a full-automatic process of rule generation, execution, feedback and updating, the problems that a traditional system depends on manual intervention, response is slow, the industry average rule updating period is 3-7 days, and real-time updating is achieved through the scheme are solved.

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention discloses an AI-based data asset quality assessment and automatic repair method and system, and the method comprises the steps: receiving a to-be-processed target data asset, inputting the target data asset into a multi-dimensional joint quality scoring engine, and generating a comprehensive quality assessment report containing an integrity defect map, a distribution abnormality thermodynamic diagram and a semantic conflict link; based on the comprehensive quality evaluation report, utilizing a rule and learning fused strategy generator to analyze repair requirements and constraint conditions contained in a defect map, an abnormal thermodynamic diagram and a conflict link, and dynamically generating repair operation instruction sets and priority sequences for different types of defects; and inputting the target data assets, the repair operation instruction set and the priority sequence into a multi-expert collaborative repair execution module, repairing defects in the target data assets according to the priority sequence, and outputting the repaired data assets. According to the embodiment of the invention, comprehensive and accurate data quality evaluation can be realized, and the automation level and accuracy of data restoration are improved.

The present invention relates to the technical field of large models, and relates to a general text quality evaluation method based on a large language model. The method comprises the following steps: 1) using a large language model to construct a general text quality evaluation model; 2) constructing training data, the training data having inputs of a prompt word and an evaluation input and an output of an evaluation result, the prompt word comprising an instruction, an evaluation rule, and an output format, and the constructing the training data comprising constructing training data containing reference answers and constructing training data containing no reference answers; 3) using the training data to train the general text quality evaluation model; and 4) using the trained text quality evaluation model to perform quality evaluation on a general text. The present invention can obtain high-quality training data containing reference answers and high-quality training data containing no reference answers at the same time, improving the data quality and performance under the setting containing no reference answers.

The invention discloses a report chart automatic generation method based on natural language analysis and data query, which comprises the following steps: acquiring a natural language text, and constructing a slot set and an initial semantic fragment; executing consistency verification and ambiguity identification, and constructing a semantic framework; executing field standardization and mode alignment, generating a data query statement and establishing a traceability chain identifier; querying a target database to obtain an original data set, completing data quality self-correction, and generating a cleaned data set; calling an improved Mamba model to construct a constraint judgment matrix and outputting a target chart subset; generating a chart description grammar and an image annotation template, and executing image rendering, feature point recognition and image sidebar construction; and performing layout optimization and double compilation, and outputting a structured report file. According to the method, the chart generation automation level and the report compiling efficiency are improved, and the accuracy, traceability and specialty of the output content are ensured.

The invention discloses a territorial space planning index dynamic evaluation model, and particularly relates to the field of territorial planning, and the model comprises a multi-source heterogeneous data fusion module, an index system construction module, a dynamic adjustment model construction module, a scheme evaluation and optimization module, and a model application and dynamic updating module. According to the method, various spatial data are integrated through a multi-source heterogeneous data fusion technology, and the data quality is ensured by adopting a three-stage cleaning assembly line and an intelligent correction algorithm; the method comprises the following steps: constructing an index system based on six core contents of territorial space planning, and determining an index weight by applying a Delphi method and an analytic hierarchy process to realize adaptive adjustment of an index threshold; the model adopts time sequence analysis, regression analysis and a machine learning algorithm to predict a development trend, a dynamic feedback mechanism is established in combination with a system dynamics method, and an evaluation scheme is quantified through an analytic hierarchy process; the model performance is evaluated by adopting indexes such as accuracy rate and precision rate, and parameters are continuously optimized based on an evaluation result; finally, the model is integrated to a service system to realize real-time monitoring, and dynamic updating is realized through three mechanisms of data driving, performance triggering and regular maintenance.

The invention discloses a cooperative monitoring device and method for a large deep foundation pit complex supporting system, and relates to the technical field of foundation pit supporting. The device comprises a multi-dimensional sensor network module, a BIM-GIS digital twin platform module, an intelligent analysis and early warning module, a construction collaborative decision module, a data backup and recovery module and a remote monitoring and management module. The method comprises the following steps: step 1, carrying out multi-source data space-time registration; step 2, dynamically constructing a digital twinborn model; step 3, coupling risk assessment; step 4, construction collaborative optimization; according to the technical scheme, the method comprises the following steps of data processing, data quality control and system performance evaluation, through three innovations of deep coupling of multi-source data, dynamic model correction and intelligent collaborative decision making, a'monitoring-analysis-decision-execution 'full closed loop is constructed, technical breakthroughs are achieved in complex working condition adaptability, early warning real-time performance and construction safety, and the method has remarkable engineering application value.

SOLUTION: Provided is a complex environment GNSS data quality-oriented high-precision data processing method which includes the steps of: preprocessing complex environment GNSS data (S1); searching for large errors in the complex environment GNSS data (S2); searching for the complex environment GNSS data (S21); identifying large errors in GNSS data (S22); processing large errors in the complex environment GNSS data (S3); removing the large errors in the complex environment GNSS data (S31); processing the GNSS data by an interpolation method (S32); analyzing and calculating ambiguities (S4); evaluating the quality of the complex environment GNSS data (S5); fusing multi-source data (S6); performing adaptive adjustment of parameters (S7); handling abnormal situations (S8); and performing real-time monitoring and correction of the complex environment GNSS data (S9).EFFECT: When processing GNSS observation values of complex environments such as a narrow-space environment, strong reflections, and multi-frequency multi-systems, the combination of the mean-shift and dispersion-broadening concepts represents a significant improvement in large-error search and data processing.SELECTED DRAWING: Figure 1

The invention discloses a multi-modal data fusion method and system based on a large-model intelligent agent, belongs to the technical field of artificial intelligence, big data processing and intelligent agents, and aims to solve the technical problem of how to effectively integrate data of different modalities such as texts, images and voices by using the large-model intelligent agent. According to the technical scheme, the method comprises the steps that data collection and preprocessing are conducted, specifically, various modal data of texts, images and voice are collected through a web crawler, an API interface, a camera and microphone equipment, the collected data are preprocessed, the preprocessed multi-modal data are obtained, and the data quality is ensured; feature extraction and mapping: extracting corresponding modal features from the preprocessed multi-modal data through CNN and Transform models, mapping the different modal features to the same space, and combining the aligned features to form comprehensive feature representation; carrying out multi-modal fusion processing; and performing intelligent decision and feedback.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention relates to the technical field of unmanned aerial vehicle-unmanned ship data fusion, and discloses an unmanned aerial vehicle-unmanned ship multi-source heterogeneous big data fusion system and method, and the system comprises a task scheduling module which is used for executing the path planning and task distribution of operation tasks; the control and data acquisition module is used for controlling the unmanned aerial vehicle and the unmanned ship to execute a synchronous measurement task; the space-time reference unification module is used for performing dynamic synchronization and consistency alignment processing; the multi-source data fusion module is used for carrying out fusion processing on the aligned heterogeneous sensing information data; the self-adaptive robust enhancement module is used for monitoring the noise level and the change condition of the signal-to-noise ratio of the sensor in the fusion process; and the intelligent analysis and visualization module is used for executing environment modeling, target identification and geographic information expression processing. According to the method, the fusion weight self-adjusting mechanism driven by the maximum entropy is introduced, so that the internal structure can be intelligently adjusted when the data quality fluctuates. According to the technical effect, the target that the weight is dynamically adjusted along with signal noise is achieved.

The invention relates to the technical field of environment intelligent management, and discloses a multi-community-oriented AI intelligent household garbage control processing system which comprises a multi-mode sensing module, a data processing and edge calculation module, a garbage quantity prediction and scheduling optimization module, an intelligent decision and multi-source data fusion module and a self-adaptive adjustment and feedback mechanism module. The multi-community-oriented AI intelligent household garbage control processing method comprises the following steps that S1, a multi-modal sensing module collects garbage data; s2, an edge calculation module performs preprocessing, classification and encrypted transmission; s3, optimizing transportation and scheduling; s4, generating an optimal processing decision; and S5, adaptively adjusting the strategy. Through the technical scheme of combining multi-modal perception and edge calculation, accurate garbage data acquisition and efficient processing in a multi-community environment are realized, the technical effects of reducing data transmission burden and improving garbage classification data quality are achieved, and garbage classification is more accurate and reliable.

The invention discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention belongs to the technical field of three-dimensional measurement, and discloses a 3D laser line scanning system based on a point cloud positioning algorithm and a control method. The method comprises the following steps: acquiring environment initial point cloud data and scanning target information; carrying out feature extraction, constructing an environment feature description model, carrying out three-dimensional space partitioning processing, carrying out regional reflectivity feature analysis, and constructing a space reflection feature model; adaptive optimization configuration is carried out on the laser scanning parameters, and a scanning strategy is generated; scanning path dynamic planning is carried out on the scanning target information, scanning density distribution calculation is carried out, and a scanning path is generated; performing laser line scanning control by using the scanning path and the scanning strategy, and performing scanning data quality real-time monitoring to obtain scanning quality evaluation data; scanning parameter iterative optimization is carried out on the scanning quality evaluation data, point cloud data real-time fusion reconstruction is carried out, a high-precision three-dimensional model is generated, the modeling precision is improved, and meanwhile the system resource utilization efficiency is optimized.

The invention relates to the technical field of industrial equipment health monitoring and fault diagnosis, and discloses a centrifugal pump intelligent detection method and system based on multi-source data. The centrifugal pump intelligent detection method based on the multi-source data comprises the steps that centrifugal pump multi-source heterogeneous data are collected and preprocessed; extracting multi-scale features and constructing an optimal feature subset through hierarchical feature selection; constructing a multi-level dynamic Bayesian network to establish a probabilistic reasoning framework; calculating a feature credibility weight based on the signal quality, the feature stability and the diagnosis correlation; executing multi-source evidence fusion and Bayesian state reasoning; confidence self-calibration and parameter self-adaptive adjustment are realized through historical diagnosis result feedback; the method can effectively cope with the complex and changeable environment of an industrial site, can still keep stable diagnosis performance under the condition that the sensor part loses efficacy or the data quality is uneven, improves the accuracy and reliability of fault diagnosis, and provides powerful technical support for predictive maintenance of industrial equipment.

The invention discloses a massive multi-source and multi-modal data fusion method, which is a technology for efficiently fusing and processing various types of data from different sources, realizes effective integration, utilization and seamless integration of multi-source and multi-modal data, and improves the breadth and depth of data analysis. According to the multi-modal feature extraction and fusion model based on deep learning, the deep features of all modal data can be automatically learned and extracted, and efficient fusion is carried out in the model. Besides, a data quality evaluation and self-adaptive adjustment mechanism is introduced, parameters and strategies in the data fusion process are dynamically adjusted according to the quality and distribution condition of the data so as to ensure the stability and reliability of the fusion result, and the method can be widely applied to multiple fields such as big data analysis, artificial intelligence, social media analysis, medical health and smart cities.