5271 results about "Data management" patented technology

The present invention relates to a method for constructing a digital twin system for an additive manufacturing process with lifecycle data management. The digital twin system for an additive manufacturing process constructed by the method includes a data acquisition and supervisory control system, a data management and storage system, a manufacturing executing system, a configuration model, a simulation system, and a multi-physics simulation system. The present invention can fully and accurately characterize the molding quality of parts, thereby saving a lot of time required for offline data processing, and achieving real-time acquisition and updating of data.

The invention discloses a teaching data management method and system based on artificial intelligence, and the method comprises the steps: obtaining a standardized time series data stream according to a heterogeneous data stream generated by a multi-source teaching platform in real time; based on the standardized time sequence data stream, performing classified encryption on the teaching data through a dynamic hierarchical storage strategy based on attribute-based encryption to obtain a security hierarchical storage topological structure; according to a user query request and a teaching scene label, extracting a target data set from the security hierarchical storage topological structure to obtain an enhanced multi-modal teaching data set; based on the enhanced multi-modal teaching data set, generating an interpretable teaching mode graph through a dynamic sub-graph evolution algorithm; and according to the teaching mode map and the real-time teaching feedback data, generating a personalized teaching recommendation strategy through a course-learner dual-channel adaptive recommendation model. According to the embodiment of the invention, the utilization efficiency of teaching resources can be improved, and personalized and intelligent teaching recommendation and decision can be realized.

The invention relates to the technical field of e-commerce data processing, in particular to an e-commerce sales platform background data management method and system. The method comprises the following steps: S1, collecting a commodity dynamic data stream, a user behavior event stream and a promotion strategy stream in real time, and generating a standardized data stream through time sequence alignment; s2, constructing a dynamic coupling data cube; s3, executing a real-time decision: in response to the payment request, selecting an inventory distribution or replacement commodity pushing strategy based on a commodity-user coupling matrix value; in response to the resource overload state, triggering a resource scheduling strategy; and S4, dynamically adjusting calculation parameters of the coupling matrix according to decision execution feedback. By solving the problems of real-time standardization processing of multi-source heterogeneous data and real-time coupling of dynamic inventory and user behaviors, the data processing capacity, inventory distribution efficiency and recommendation accuracy of the platform are remarkably improved.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention discloses intelligent knowledge service electronic equipment for information retrieval, and the equipment comprises a data management layer which is responsible for providing original data for a cognitive calculation layer, and carrying out the closed-loop optimization through continuously monitoring the feedback of a man-machine interaction layer; the cognitive calculation layer is responsible for converting original data into a semantic tag system and a dynamic knowledge graph, applying BERT + GNN hybrid analysis, BERT processing text context and GNN analysis entity relationship in the knowledge graph, and dynamically adjusting a reasoning path according to user field preference by combining an SWRL rule engine and reinforcement learning; the service arrangement layer is responsible for extracting data from the multi-modal data pool, solidifying and arranging a complex query stream to perform logic decoupling and efficient multiplexing, and providing a visual interface to support non-technical personnel to configure a new scene; and the man-machine interaction layer is used for constructing a natural interaction bridge through the structured answers and the visual maps output by the service arrangement layer, carrying out personalized interface adaptation based on historical dialogues, and establishing a user feedback closed loop.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention discloses a multi-source data fusion type intelligent data management system, which comprises a data preprocessing module used for accessing a heterogeneous data source and generating a standardized data frame; the cognitive sub-graph construction module is used for generating cognitive sub-graphs and forming a cross-source evolutionary multi-cognitive hypergraph; the event aggregation module is used for aggregating entities and relationships according to event fingerprints and outputting a multi-source observation chain; the causal checking module is used for performing causal comparison and conflict detection and outputting a checking result, a positioning report and a treatment suggestion; the fusion updating module is used for distributing weights and performing weighted fusion, outputting fusion representation and confidence score, recording conflicts and updating the hypergraph; and the memory pool module is used for storing the hypergraph, the check result and the fusion representation, executing weight adjustment, forgetting and elimination, and outputting a service interface. According to the method, intelligent fusion and dynamic management of multi-source heterogeneous data are realized by constructing a cross-source self-evolution multi-cognitive hypergraph and an active evolution type fusion memory pool.

The invention discloses a railway vehicle test data management and analysis system, and the system comprises a multi-modal data collection module which collects the heterogeneous data of tests such as airtightness and weighing in real time; the block chain credible evidence storage module is used for performing time-space stamp marking and hash encryption on the data and verifying the integrity; the space-time atlas analysis module is used for constructing a space-time heterogeneous atlas and generating vehicle-level feature vectors through graph convolutional network fusion data; the predictive maintenance decision module is used for predicting subsystem performance degradation and fault risks and generating a hierarchical maintenance strategy; and the adaptive visual platform integrates a large screen and a mobile terminal to display data and decisions. According to the system, efficient integration and credible evidence storage of multi-source data are realized, the data utilization efficiency and analysis depth are improved, accurate maintenance decision is supported, and safe operation of railway vehicles is guaranteed.

The invention discloses a road-oriented full-process digital collaborative management system and method, particularly relates to the technical field of road data management, and is used for solving the problem of abnormal multi-role collaborative conflict recognition. According to the method, dynamic linkage management of task states and role behaviors in the whole highway design process is achieved by building the task-driven atlas and the multi-role scheduling model, and the dynamic linkage management of the task states and the role behaviors in the whole highway design process is achieved by extracting access behaviors, task records and data version states, calculating role collaborative offset and recognizing and controlling the write-in permission of task conflict nodes. A priority factor is constructed based on a stage index, data dependence and a time urgency degree, a nonlinear model is adopted to generate scores, automatic reconstruction of a scheduling sequence and data ownership is driven, role permission and a collaborative view are synchronously updated, a data access control closed loop based on task state evolution is formed, and a three-dimensional responsibility chain is constructed by whole-process behavior traces. The traceable management of collaborative operation is realized, and the intelligence of collaborative scheduling and the accuracy of data management are improved.

The invention relates to the technical field of environmental governance and remediation, in particular to a combined contaminated site soil remediation data management method, which comprises the following steps of: firstly, integrating multi-source heterogeneous data through a unified space-time coordinate system to generate a fusion data pool, and then driving a pollution assessment model to generate a multi-dimensional space-time pollution map; and then intelligently matching a candidate repair process chain based on an associated knowledge base, performing iteration deduction through a dynamic multi-objective optimization engine to obtain an optimal repair scheme, converting the optimal repair scheme into a digital regulation and control instruction, and finally monitoring an execution state and environmental response data in real time and feeding back and updating a data pool to realize closed-loop optimization. According to the method, the data quality and the pollution evaluation accuracy are improved, the adaptability of the repair process and the cost performance of the scheme are optimized, the repair effect is ensured to be stable and reach the standard, and the efficiency and reliability of the repair project are improved.

The invention discloses a mold and formwork design system based on virtual simulation, and particularly relates to the field of mold design, the mold and formwork design system comprises a multi-mode perception fusion module, a digital twin modeling module, a multi-physics field coupling simulation module, a hybrid intelligent optimization module, a digital twin closed loop verification module and a data center module, and each module forms a design closed loop through a data center. The multi-modal sensing fusion module adaptively collects and fuses multi-source signals to generate high-credibility data; the digital twin modeling module constructs and iteratively corrects a model based on the data; the multi-physics field coupling simulation module realizes multi-solver co-simulation under a dynamic boundary; the hybrid intelligent optimization module accelerates to generate an optimal solution through secondary optimization and an agent model; the digital twin closed-loop verification module detects defects and generates a correction instruction; the data center module is responsible for data management, cross-module scheduling and precision control; the system improves the design precision and efficiency of the mold frame, reduces the physical mold testing cost, and is suitable for a high-precision mold development scene.

The invention relates to an aviation equipment manufacturing digital main line engine system supporting multi-source heterogeneous data access, and belongs to the technical field of aviation manufacturing data management. The system comprises a multi-source heterogeneous data access adaptation module, a data fusion processing module and a full-process data link construction module. The multi-source heterogeneous data access adaptation module passes through a data interface protocol and format conversion component; the data standardization and fusion processing module completes association fusion through an aviation heterogeneous data collaborative computing framework by means of an aviation manufacturing field data element standard library, a unified heterogeneous data model is generated, and the framework comprises a data fragmentation layer, a parallel node layer and a result aggregation layer; and the full-process data link construction module constructs a data link covering the full life cycle of the product based on a time sequence association algorithm and a product unique identifier mapping mechanism. The system can effectively solve the integration and management problems of multi-source heterogeneous data in aviation equipment manufacturing, and improves the data processing efficiency and the whole-process data tracing capability.

The invention discloses an archive data management labeling method and device based on computer application, and relates to the technical field of archive information management and intelligent labeling, and the device comprises a semantic analysis module, an allogenic character normalization module, a context modeling module, a historical semantic alignment module, a context feedback module, a processing module and a model optimization module. The device can automatically monitor file input, clean and segment texts, quickly and normatively replace foreign characters and intelligently convert simple and traditional Chinese characters; a semantic vector is generated based on a Transform model, semantic consistency is detected, and if semantic drift is found, rollback adjustment is carried out; the processing module is combined with a rule engine and a depth model to complete entity labeling and multi-label reasoning; and the optimization module dynamically records and feeds back annotation data, and supports self-learning and continuous improvement of the model. By continuously optimizing the judgment logic, the system has self-learning and evolutionary capabilities, so that the archive labeling process is more and more intelligent, and the labeling result is more and more accurate.

The invention belongs to the technical field of nerve regulation and control, and discloses a portable noninvasive brain deep electrical stimulation system based on time interference, and the system comprises an electroencephalogram signal collection unit which is used for obtaining an electroencephalogram signal in a high-quality resting state, and carrying out the signal preprocessing and feature extraction, thereby obtaining an optimal stimulation envelope frequency; the personalized finite element modeling unit is used for constructing a head personalized model and outputting optimal electrode configuration and stimulation parameters for a target brain region target spot; the electrode positioning and stimulation execution unit is used for realizing accurate positioning of an electrode position and implementing time interference electrical stimulation; the MCU unit is used for realizing data management, task scheduling and remote communication; the human-computer interaction unit is used for realizing visual treatment flow and parameter setting; and the power supply management unit provides stable working voltage for the system. According to the invention, the portable, intelligent and personalized modeling of the electrical stimulation treatment equipment is realized, and the precision and efficiency of nerve regulation and control are remarkably improved.

The invention provides a carbon emission digital management system based on a cloud platform. The carbon emission digital management system comprises a data acquisition layer, a hardware resource layer, a data management layer, a platform algorithm layer, an application layer and a display layer, according to the system, through multi-source data acquisition and data analysis based on an AI large model, artificial filling errors are avoided, meanwhile, carbon emission factors are dynamically adjusted through introduction of machine learning and automatic calculation, and the carbon emission accounting precision is comprehensively improved; secondly, the system is combined with a digital twinning technology to help enterprises to realize multi-level carbon emission trend analysis of groups, enterprises, processes, equipment and the like, comprehensive strategy optimization is carried out through multi-level data arrangement, and wider application services are provided; besides, the system is in butt joint with international standards such as the international carbon market and CBAM, can help enterprises to monitor carbon price fluctuation and predict in real time, intelligently adjust carbon tax and provide a compliant carbon transaction scheme, and helps the enterprises to reduce cost, thereby realizing comprehensive upgrading of carbon management from static statistics to intelligent prediction and optimization.

The invention relates to the technical field of industrial platform data analysis, in particular to a renewable resource recovery data management system based on the Internet of Things, which comprises the steps of synchronously acquiring weight, spectrum and microwave characteristic data of a tested resource through a data acquisition module, executing moisture weight decoupling operation by utilizing a resource matching module, and performing data analysis; then, an inventory evolution module tracks performance loss of resources in real time by using an evolution model of an integrated long and short-term memory network, generates a resource attenuation weight, and constructs a two-dimensional decision matrix containing scheduling priority and preprocessing strength grade instructions in combination with real-time inventory saturation; and finally, the clearance scheduling module executes dynamic pruning and bidirectional optimization through a resource scheduling model, accurately allocates the loading share and the access sequence of each node, and generates a dynamic instruction set containing a delivery sequence. According to the method, feeding homogenization is realized through industrial data multi-dimensional collaborative analysis.

According to the intelligent rolling construction method and system for the concrete face rockfill dam and the computer readable storage medium, self-adaptive adjustment of the rolling speed and frequency is achieved through three-dimensional digital elevation model generation, optimal rolling path planning, rolling parameter dynamic optimization model and real-time compaction degree prediction. The system integrates satellite positioning, BIM model verification, cloud data management and an abnormal protection mechanism, can correct path deviation in real time, generates a pressure supplementing instruction and stores construction data, provides an intelligent solution for high-precision and high-reliability rock-fill dam construction, and is suitable for popularization and application. The technical problems that in traditional rolling construction, manual experience is relied on, parameters are static, monitoring lags behind, and the rework rate is high can be solved.

The invention discloses a smart mine mining method and device based on an industrial cloud platform, and a medium, and relates to the technical field of smart mining, and the method comprises the steps: enabling a digital twinborn analysis module to communicate with a data governance fusion module through a data service bus, and receiving mine comprehensive state data outputted by the data governance fusion module, constructing a three-dimensional geologic model, carrying out abnormal behavior detection through a deep learning algorithm, and outputting an abnormal detection result; and the intelligent decision optimization module is associated with the digital twinborn analysis module through an algorithm cooperation interface, and is used for establishing a disaster prediction model and performing disaster risk assessment based on an abnormal detection result and mine comprehensive state data, and optimizing a mining strategy by using a reinforcement learning algorithm to obtain an optimal mining scheme. And the data is transmitted back to the data acquisition upper cloud module through a feedback control link to guide field acquisition and scheduling. And the overall safety, the operation efficiency and the intelligent level of a mine system are improved.

The invention discloses a content creation and multi-mode display platform based on AIGC. The system comprises six modules of user interaction, content creation intelligent collaboration, AIGC auxiliary creation, data management, multi-modal deep fusion display and user feedback. The user interaction module supports various inputs and provides a creation template; the content creation intelligent collaboration module analyzes the intention and intelligently distributes tasks; the AIGC auxiliary creation module provides multi-mode creation assistance; the data management module stores various data; the multi-modal deep fusion display module is used for mining modal connection to realize deep fusion display; and the user feedback module collects and feeds back optimization contents and algorithms. Through cooperative work of the modules, the problems that an existing platform is difficult in creation cooperation, single in content generation, insufficient in multi-mode display and the like are effectively solved, creation efficiency and content quality are improved, immersive experience is brought to users, and innovative development of the field of content creation and display is promoted.

The invention relates to the field of data management, in particular to a VOCs pollution working condition data treatment method based on artificial intelligence. The method comprises the following steps: firstly, constructing a three-dimensional model of a factory, a pipeline and treatment equipment based on a BIM tool and a GIS technology, and deploying an Internet of Things sensor to collect VOCs pollution data; collecting original infrared absorption spectrum data of VOCs, and performing component identification by using a non-negative matrix factorization algorithm; a machine learning model is trained in combination with historical data, and short-term emission prediction is carried out; cooperative scheduling is carried out on the governance equipment through a multi-agent game optimization algorithm, and an optimal operation scheme is generated; constructing volume cloud modeling, and dynamically displaying an optimization effect; and if the predicted emission exceeds the standard, performing emission abnormity traceability analysis by using a graph neural network, and outputting a fault diagnosis report. According to the method, the VOCs emission prediction precision and the treatment efficiency are effectively improved.

An apparatus includes at least one processing device including a processor coupled to a memory, wherein the at least one processing device is configured to modify first data obtained from one or more sources, wherein the modifying includes adding user context data to the first data to generate second data, the second data representing the first data supplemented with a per-user context and, in response to receipt of a query, generate a response to the query using at least one generative language model supplemented by a retrieval augmented generation process based on at least a portion of the second data.

The invention provides an engineering drawing intelligent processing system based on a ComfyUI, and the system comprises an algorithm service module which integrates a plurality of drawing processing algorithm assemblies, such as drawing layout analysis, OCR recognition, drawing direction detection, semantic understanding, drawing frame disassembly, and axis net recognition, and provides services for the outside through a standardized interface; the workflow arrangement module is used for packaging the algorithm components into parameterized drawing processing nodes based on a visual node configuration framework of a ComfyUI, and supporting flexible combination, real-time preview and batch execution of drawing processing flows; and the workflow management module is used for importing, analyzing and publishing the workflow configuration file in the ComfyUI format, and realizing metadata management, publishing verification, API document generation, interface registration and call authority control of the workflow. The method supports multi-type input of PDF and CAD drawings, is suitable for engineering drawing processing tasks such as drawing label extraction, sub-drawing recognition, drawing frame segmentation and drawing filing, and is high in intelligent degree, flexible in process configuration, easy to integrate and high in expansibility.

The invention discloses an intelligent data storage method based on a cloud platform, relates to the technical field of data storage management, and is used for solving the problem that cold and hot data identification is lagged. According to the method, the access behavior graph is constructed, the concurrent access coupling weight is calculated, the data object with high access frequency and deep path hierarchy is identified, the structure adjustment instruction is dynamically generated, the access link complexity and index hierarchy redundancy are reduced, the response delay and the popularity prediction error before and after the structure adjustment are combined, and the access efficiency is improved. According to the method, the scheduling efficiency of the hotspot data is improved, the adaptability to the change of a storage access mode is enhanced, the dynamic adjustment capability and the structure response performance are good, and the method can be used for realizing the closed-loop linkage of the access behavior perception, the structure reconstruction and the model optimization. Therefore, the method is more suitable for a large-scale data intelligent management scene under a cloud platform.

The invention discloses a geological exploration service management method and system based on cloud intelligence, and relates to the technical field of geological exploration management, and the system comprises a standard specification system, a base layer, a data layer, a platform layer, an application layer and a safety guarantee system. Data collaborative acquisition, processing and sharing are realized through a multi-level architecture design, project approval analysis, work layout, task distribution auditing and data management are realized, multi-specialty collaborative acquisition and safety management of a field data acquisition APP are supported, rapid mapping and standardized filing of an interior work data processing tool are supported, a standardized process is formed, and data processing efficiency is improved. The working efficiency and the data quality are improved through indoor and outdoor real-time collaboration, multi-source data verification, edge calculation and the like, the data safety is guaranteed, the team collaboration and achievement customization ability is enhanced, meanwhile, integrated data sharing is achieved through the cloud computing pool, and the efficiency of geological exploration work is improved.

The invention discloses an electric power measurement data management system based on cloud edge collaboration and an electric quantity distributed acquisition method, and relates to the technical field of electric power information acquisition and processing. The system comprises an intelligent electric meter terminal layer, an edge computing node layer, a regional collaborative gateway layer and a cloud data center. The intelligent terminal can dynamically adjust the sampling frequency according to the load change rate and the historical volatility, and identifies abnormity through an LSTM-ATT model; the edge nodes realize prediction caching based on Kalman filtering and RMSE judgment; the regional gateway executes dynamic time warping and consistency arbitration of topological weighting; and the cloud end completes digital twinborn modeling and exception evidence storage. According to the method, the real-time performance and accuracy of electric power data acquisition are improved, the communication bandwidth occupation is reduced, the system abnormity sensing capability and the operation stability are enhanced, and the method is suitable for an intelligent power distribution network and a distributed energy metering scene.

The invention relates to the technical field of concrete pool heavy anti-corrosion coating construction, and discloses a concrete pool heavy anti-corrosion coating multi-layer spraying intelligent planning construction method which comprises the steps that spraying time sequence parameters, material combination and a basic spraying parameter set of all coatings are determined on the basis of pool structure parameters and anti-corrosion grades; generating a spraying path planning set in combination with equipment attributes and environment data, and defining an execution priority; a spraying parameter correction list and a quality verification scheme list are generated according to the construction period, the drying rate and the like; and associating real-time data to form an intelligent construction overall scheme. The method also covers regional process boundary determination, construction mode switching, path optimization, quality verification, construction data management and the like. According to the method, accurate control and quality optimization of multi-layer spraying are achieved through multi-factor fusion and an intelligent algorithm, the construction efficiency and the anti-corrosion performance are improved, and the method is suitable for heavy-duty anti-corrosion construction of the concrete pool.

The invention provides a project cost big data management and analysis system, and relates to the technical field of data management, and the system comprises a data collection and preprocessing module which is used for collecting original cost data from a heterogeneous data source, and carrying out the preprocessing of the original cost data, and obtaining the preprocessed cost data; the semantic feature extraction module is used for converting the preprocessed cost data into a multi-dimensional feature vector based on a multi-level feature extraction system; the similarity calculation module is used for calculating similarities among different cost data based on the multi-dimensional feature vectors to obtain a similarity matrix; and the data storage and management module is used for storing the cost data, the multi-dimensional feature vector and the similarity matrix by adopting a mixed storage architecture, and providing retrieval and recommendation functions of cost projects based on a multi-level feature space index structure. According to the method, the limitation that a traditional method only depends on keyword matching is solved, and the system can recognize the deep incidence relation between the items.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.