2121 results about "Data accuracy" patented technology

The invention discloses an intelligent control system for a current transformer, and particularly relates to the technical field of power system monitoring, which comprises a fluctuation identification unit, a redundancy acquisition unit, an error fitting unit, a protection delay unit and a fault response unit, realizes real-time identification and classification of current fluctuation, and has standby path high-speed sampling and fusion signal construction capabilities. The fluctuation trend is judged through error regression, a protection strategy is dynamically set, response is delayed to avoid misoperation, finally, a high-level protection instruction is triggered and a fault event is output when offset overruns or alignment fails, and the data accuracy and the system response stability in the power supply switching process are guaranteed; according to the method, accurate identification of current fluctuation is realized through multi-dimensional feature extraction, data reliability is enhanced by using redundant sampling and error fitting, and by combining delay control and a hierarchical response mechanism, the risk of misoperation is effectively reduced, the fault judgment accuracy and the system protection stability are improved, and the method is suitable for a complex power supply switching scene.

The invention discloses a hybrid energy storage system optimization scheduling method based on AI intelligent regulation and control, and relates to the technical field of hybrid energy storage, and the method comprises the following steps: collecting real-time data, and carrying out the cross verification of data consistency through a multi-source data fusion technology; and identifying data abnormity caused by sensor faults, communication delay and environmental interference by combining adaptive threshold detection with a statistical analysis method, and eliminating abnormal data. According to the method, energy storage scheduling is optimized through data cleaning, time sequence prediction and reinforcement learning, and the intelligent management and dynamic adaptive capacity is improved. Multi-source data fusion and anomaly detection are adopted to ensure data accuracy, energy consumption is predicted by means of LSTM and Transform, and an energy storage strategy is optimized in advance. By combining reinforcement learning and system dynamic adjustment charging and discharging, the photovoltaic consumption rate is improved, the electricity purchasing cost is reduced, the SOC is intelligently controlled to be 40%-80%, and the service life of the battery is prolonged. Meanwhile, the system stability is improved through anomaly detection and correction, and the maintenance cost is reduced.

The invention relates to the technical field of data processing, provides a multi-node carbon emission intelligent contract verification method based on a block chain, and aims to solve the problems that data verification in an existing carbon asset transaction system is non-transparent, high in trust cost and difficult to adapt to policy changes. Distributed storage and dynamic verification of data are realized by constructing a hierarchical block chain architecture, the data are collected by using edge computing nodes, and third-party verification data are obtained through an oracle machine node cluster. The dynamic rule engine monitors data changes in real time, triggers a multi-stage verification process, and improves the data accuracy. And meanwhile, an improved Byzantine fault-tolerant mechanism is adopted to reach a consensus, and the system has the functions of strategy dynamic updating and security enhancement, so that the flexibility and security of the system are improved. According to the method, multi-party consensus and real-time verification of the carbon emission data are realized, trust cost of cooperation among enterprises is reduced, and healthy development of a carbon emission permit trading market is promoted.

The invention discloses an intelligent robot control method based on multi-sensor cooperation, and relates to the field of intelligent robots, and the method comprises the following operation steps: S1, carrying out the architecture deployment of sensors; s2, establishing communication; s3, edge intelligent calculation enhancement; s4, a dynamic adaptive fusion strategy; s5, carrying out group intelligent decision making; and S6, carrying out sensor role dynamic conversion. According to the intelligent robot control method based on multi-sensor cooperation, the sensor data credibility can be adjusted in real time according to the environment condition, the fusion weight can be dynamically allocated, the data accuracy is ensured through the cross check of the adjacent sensor data, and meanwhile, the intelligent robot control method based on multi-sensor cooperation can be realized through the ant colony cooperation algorithm in combination with the self-adaptive pheromone volatilization regulation and control. The path exploration strategy can be flexibly adjusted according to task complexity and environment dynamic change, the search efficiency can be effectively improved, various environments can be more quickly adapted, and the optimal action path can be quickly found.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention discloses a natural resource engineering monitoring method and system based on cloud computing, and relates to the technical field of engineering monitoring. A cloud access layer is constructed, a standardized monitoring data set is formed, a monitoring quality abnormity label set is established, and an engineering state vector set is generated based on a space-time alignment monitoring data set; according to the method, standardization and unified management of multi-source monitoring data are achieved through cloud access, the data accuracy is guaranteed in combination with time synchronization, space mapping and a quality label mechanism, the method constructs an engineering state profile and generates consistent snapshots, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Historical comparison and tracing are supported, a risk event set and a risk evidence chain are generated through causal atlas inference, risk full-chain tracing is achieved, a scheduling instruction is further generated and combined with an execution receipt to form closed-loop management, differential evaluation and dynamic adjustment are supported, and therefore linkage and continuous optimization of monitoring, risk identification and engineering scheduling are achieved.

Computer-implemented methods and systems are disclosed for Information Technology Service Management (ITSM). The pioneering AI-driven system revolutionizes IT service management by uniquely validating, suggesting, and inferencing incident and problem data. At its core are cutting-edge generative AI techniques like GANs and LLMs, requiring intricate training and iterative refinement on varied data sets, showcasing a depth of expertise in database structures and AI concepts. It bridges critical gaps in incident resolution and classification through cognitive computing, AI, NLP, and deep learning, applied to both historical and current data. The system comprises modules for Incident Validation & Classification, Resolution Validation, Generative Intelligence, Problem Probability Calculation, and Prevention Recommendation, each employing AI to enhance standard compliance, predictive analysis, and proactive management, thereby setting new standards for IT service management efficiency and effectiveness.

The invention discloses an indoor water tank sediment starting speed measurement system and method, and aims to solve the technical problem that data accuracy is affected due to the fact that a sensor disturbs a flow field in an existing measurement method. The core of the technical scheme is that the non-contact measurement characteristic of the PIV technology is applied to the whole process from flow field construction to data acquisition. The device is mainly composed of an imaging unit, a laser lighting unit, a movable sliding frame, a water flow adjusting unit and a buffer plate. Optical measurement is carried out through the imaging unit and the laser illumination unit, a physical sensor does not need to be arranged in a flow field, and interference of a measuring instrument on a water flow structure and a sediment starting process is fundamentally avoided. The movable sliding frame which is integrally designed integrates the functions of sediment laying, particle throwing and measuring, so that a test area can be quickly positioned; and the buffer plate and the water inlet and drainage adjusting unit are combined, so that a gradually-changed flow field from weak to strong can be accurately and stably simulated.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of data processing, and discloses a knowledge graph incremental updating and consistency checking method and system. The method and the device are used for solving the problem of low incremental updating and consistency checking efficiency of a large-scale knowledge graph. The method comprises the following steps: collecting a to-be-updated data source, sorting and separately storing data, and marking priority labels; performing change detection on the marked data source, identifying change items by comparing entities and relationships, and generating a log; incremental updating is executed based on the log, and nodes and edges are processed in a hierarchical fusion mode; preliminary consistency verification is carried out, and attribute uniqueness and relation directivity are checked; expanding a verification range, traversing an association path through cascade check, and recording problems; and optimizing storage according to the record, merging the update area, updating the index and cleaning the log. The method solves the problem of low efficiency of incremental updating and consistency verification of the large-scale knowledge graph, improves the response speed and the data accuracy of the system, and is suitable for a high-frequency dynamic data environment.

The invention relates to the technical field of artificial intelligence, in particular to a government work report generation system and method based on multi-path retrieval and a large model, and the system comprises a data engineering module, a database system module, a multi-path retrieval module, a cue word engineering module and a report generation system module. The system has the beneficial effects that database management and work report generation capabilities are considered, a user not only can store, manage and check own government affair data, but also can be linked to a report generation module to generate a government work report based on a knowledge base constructed by a client, and the data accuracy and personalization of report content are greatly improved.

The invention provides a gas data collection and analysis system. The gas data collection and analysis system comprises a data acquisition module, a data transmission module, a data storage module, a data analysis module and an alarm and visualization module. According to the system, gas operation parameters are collected in real time through flow, pressure, temperature and gas concentration sensors, the data accuracy is improved through a correction algorithm, data are transmitted to a cloud or a local server for storage based on the wireless communication technology, and data safety and efficient storage are guaranteed through a distributed architecture. According to the gas data collection and analysis system, gas demand prediction and anomaly detection are achieved through a time sequence prediction model and a clustering algorithm, leakage or abnormal operation conditions are accurately recognized, when gas leakage, flow anomaly or equipment faults are detected, an alarm is automatically triggered, related personnel are remotely notified, meanwhile, a data visualization interface is provided, and the system is convenient to use. And real-time monitoring and trend analysis are supported.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention discloses a water conservancy facility monitoring method and system based on digital twinning, and relates to the technical field of water conservancy monitoring. Historical operation data of a water conservancy facility are obtained based on a digital twinning model for analogue simulation, and full life cycle data of the water conservancy facility are obtained; and importing the standard operation data into the digital twin model, carrying out data comparison and data compensation on the standard operation data and the full life cycle data, verifying the accuracy of the standard operation data, and generating output verification data after comparison is completed. The verification data is generated by comparing the operation data with the data simulated by the digital twin model and combining cross verification of multiple operation data in the same area, so that data accuracy confirmation and data compensation can be carried out on the data distortion problem caused by abnormal data acquisition of the water conservancy facility operation data acquisition equipment; the reliability of the collected operation data is ensured, and the accuracy of evaluation and decision-making of the subsequent water conservancy facility monitoring operation state is guaranteed.

The invention discloses a water conservancy water regimen monitoring management system based on Beidou short message communication, which relates to the technical field of water regimen monitoring and comprises a scheduling module, a data compression module, a data preprocessing module and an early warning module. The scheduling module determines a transmission queue by marking a water level state, calculating various scores and correction parameters, and implements a preemption mechanism and setting optimal transmission in a silent period; the data compression module classifies and processes data, adjusts the compression rate according to channel signals, and adopts forward error correction coding and the like to guarantee the data accuracy; the data preprocessing module filters the original data, detects abnormity by using space-time correlation analysis, and fills missing values; the system effectively realizes water conservancy water regimen monitoring management, has significant advantages in the aspects of data transmission, processing, early warning and the like, improves the accuracy and timeliness of water regimen monitoring, and provides powerful support for water conservancy management.

The invention relates to the technical field of earthquake prediction, in particular to a submarine seismic oscillation prediction method and device based on earthquake exploration, and the method comprises the following steps: selecting a monitoring region, collecting the submarine topographic data of the monitoring region, and drawing a topographic map; selecting a plurality of monitoring points according to the topographic map; geomagnetic field information, electric wave information, micro-seismic activity information and seabed deformation information of each monitoring point are collected and recorded; converting the collected information into acoustic signals and transmitting the acoustic signals to a sea surface buoy, and converting the acoustic signals into electric signals by the sea surface buoy and transmitting the electric signals to a ground data center; by optimizing site selection of monitoring points, integrating geomagnetic-electric wave-microseismic-deformation multi-source data, realizing high-efficiency data transmission by using an acoustic-electric staged communication chain, and finally realizing early recognition of earthquake precursor in combination with threshold analysis, the method can significantly improve the coverage range, data precision and early warning timeliness of seafloor earthquake monitoring, and has good application prospects. And a technical support is provided for marine geological disaster prevention and control.

The invention relates to the technical field of remote sensing imaging, in particular to a marine organism monitoring method and system based on a remote sensing technology, and the method comprises the following steps: constructing an environment layer based on a remote sensing image and sensing data, analyzing the relation between a spectrum and an environment, monitoring changes, recognizing the spatial distribution of species, extracting a migration path, and superposing environment factors to analyze ecological response. And outputting a remote sensing indication species ecological response interaction layer. According to the method, the remote sensing image and environmental data are sorted and denoised, multi-dimensional factors are effectively integrated, data precision is enhanced, noise interference is eliminated, spectral change and water temperature gradient deviation are combined, abnormal pixels are accurately screened, sea area change trend is insight in advance, key wave bands and hot salt structures are extracted, target organisms are accurately positioned, and identification efficiency is improved. Analyzing a space offset track, restoring a migration path, constructing a dynamic evolution view angle, superposing water temperature and algae changes, analyzing time sequence synchronization characteristics, revealing ecological response turning points, and strengthening early warning and dynamic monitoring capabilities.

The invention provides a field NL2SQL data automatic synthesis method and system based on a large language model, and the method comprises the steps: building an SQL template library through collecting a public data set and real business query data, carrying out the grading according to the SQL complexity, and forming an SQL template set covering different difficulties; specific SQL queries are generated based on an SQL template library and a large language model, verification is carried out through multiple mechanisms such as grammar check, execution verification and result dimension consistency check, and correctness and performability of the SQL queries are ensured. Generating a plurality of candidate natural language questions according to the verified SQL query and the large language model, calculating a semantic consistency score of each candidate question and the SQL query through a cross consistency evaluation mechanism, screening out the question with the most consistent semantics as a final question-answer pair sample, and meanwhile, eliminating low-quality samples by setting a consistency threshold value, so as to obtain a final question-answer pair sample; and the data accuracy and consistency are further improved.

The invention discloses a soil heavy metal content estimation method and device, and relates to the technical field of soil heavy metal estimation.The method comprises the steps of sample collection and data acquisition, specifically, based on geographic information of a research area, soil samples are collected through a layered and partitioned sampling method; according to the method, comprehensive and accurate collection and data acquisition of soil samples are realized through a layered and partitioned sampling method in combination with a high-precision positioning and measuring instrument, the sample representativeness and the data accuracy are ensured, and through an improved correlation analysis algorithm and a depth model based on feature fusion, the soil sampling accuracy is improved. Accurate analysis and efficient modeling of correlation between environmental magnetic parameters and heavy metal content are realized, prediction precision is improved, dynamic optimization of model parameters is realized by applying a self-adaptive step length strategy in model verification and optimization, model stability and generalization ability are guaranteed, and all modules of the device work cooperatively, so that the reliability of the device is improved. And deep analysis of heavy metal sources and migration and transformation rules is realized by using a geochemical analysis module and related models.

The invention discloses a liquid cooling charging module health state prediction system, and relates to the technical field of electrochemical detection, and the system comprises a multi-parameter collection unit which collects the pressure and flow data of a cooling liquid, and is provided with an independent collection node at each branch of a double-gun charging system, and provides basic data for monitoring; the dynamic calibration unit communicates with the acquisition unit, compensates data drift based on a temperature-vibration interference model of machine learning training, executes baseline calibration by using an idle period to update a reference value, and guarantees data accuracy; the leakage detection unit is used for calculating a data change rate after compensation, outputting an early warning according to a preset condition and positioning a leakage branch; and the health state evaluation unit fuses the early warning signal and the historical operation data to generate a prediction result. The problems that tiny leakage detection is difficult and a sensor is prone to interference can be solved, manual inspection is reduced, the maintenance cost is reduced, and the requirement of a high-power charging scene is met.

The invention discloses a three-dimensional target detection and fusion method based on multi-sensor fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining multidirectional two-dimensional image data through a camera, synchronously employing a laser radar to obtain point cloud data, constructing a two-dimensional bounding box and a three-dimensional bounding box respectively, and screening out a preferable two-dimensional image and the point cloud data through matching, and carrying out feature fusion to obtain a two-dimensional fusion image to be detected. And then the recognition accuracy is accurately obtained, sorting is carried out according to a descending order, high-accuracy fusion images are sequentially extracted to be combined with point cloud data to determine a preliminary detection result, target geometric information is scientifically set based on the result, and finally a standard three-dimensional target detection result is determined by integrating the three-dimensional geometric information, fusion image details and point cloud data precision. The method has the advantages that multi-sensor heterogeneous data are deeply fused, and the accuracy and reliability of three-dimensional target detection in a complex scene are remarkably improved through multi-stage fine processing and intelligent analysis.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention discloses a power distribution network fault identification method and device based on artificial intelligence, and the method comprises the steps: obtaining the multi-source fault characterization information of a power distribution network, and the multi-source fault characterization information comprises telemetering data and telecommand data; performing expert database-based normalization processing on the multi-source fault representation information to generate a standardized fault data sample; matching and checking the standardized fault data sample to generate checked fault data; performing graph data conversion processing on the checked fault data according to a power distribution network graph model to generate fault graph data; and based on the fault graph data, reasoning the fault of the power distribution network through the fault reasoning model to obtain the speculated fault of the power distribution network. The multi-source information is obtained, the limitation of a single channel is broken, data differences are eliminated through expert database normalization, data accuracy is guaranteed through matching checking, graph data conversion is associated with topology, artificial intelligence reasoning gets rid of manual dependence, and efficiency and accuracy are improved.

The invention relates to the technical field of sewage treatment and automatic control thereof, in particular to an automatic monitoring system and method for sewage treatment of a vibrating membrane bioreactor. The method comprises the following steps: monitoring key indexes in real time, and carrying out standardization processing and interpolation on sensor data to ensure the accuracy of the data; an information code generation and verification mechanism is adopted to ensure data integrity and security; a physical model and a deep learning model are combined to predict sewage treatment efficiency, and space-time relevance is analyzed through a space-time convolutional network; operating parameters of the system are optimized through a deep reinforcement learning algorithm, and the frequency, the amplitude, the aeration power and the medicament dosage of a vibrating membrane are adjusted to achieve an optimal treatment effect; through real-time adjustment of the vibrating membrane and the aeration equipment, the sewage treatment efficiency is improved; the real-time treatment state is displayed, the sewage removal rate is monitored, and an alarm mechanism is triggered under the abnormal condition. According to the method, the data security and the system optimization are considered while the processing effect is ensured.

The invention belongs to the field of air pollution prevention and control, and particularly discloses an urban air pollution transmission path and source contribution evaluation system and method based on machine learning and a numerical mode, and the evaluation system comprises a meteorological simulation module, a pollution emission list module, a numerical traceability module and a machine learning transmission path evaluation module. According to the method, machine learning and a numerical mode are combined, the automation degree is high, automatic downloading, automatic running and online data analysis can be achieved without manual data statistics and analysis, operation is convenient, data accuracy is high, the response speed is high, and important support is provided for work such as atmospheric pollutant cause tracing and atmospheric pollution control scheme making.

The invention relates to the technical field of deformation prediction, in particular to a lateral deformation prediction system and method based on a deep foundation pit wall body, and the system comprises a deformation data collection module which collects a ground surface settlement monitoring value and a lateral displacement monitoring value of a corresponding measuring point of the deep foundation pit wall body, carries out the time sequence alignment processing of data points, removes an abnormal monitoring value, and obtains a deformation data collection module; and establishing calibration deformation sequence data. According to the invention, through real-time acquisition and strict execution of data time sequence alignment processing, the consistency of ground surface settlement data and wall lateral displacement data is ensured, abnormal monitoring data is effectively eliminated, and the data accuracy and consistency are improved; in the spatial interpolation process, width measurement of a settlement area is introduced, the width characteristic of a settlement groove is determined, and the earth surface deformation trend is captured according to a deformation index; and extracting the maximum lateral displacement of each time section of the monitoring section, establishing a dynamic corresponding relation between the maximum displacement and the width of the settling tank, and identifying the regular influence of the change of the construction process on the lateral deformation.

The present invention relates to the technical field of energy meter electrical parameter measurement. Disclosed is an intelligent anti-interference system for a power line carrier energy meter. The system comprises: collecting voltage and current signals of a power line, and constructing a sample database; performing time-frequency feature classification by means of a preset model, and establishing feature and scenario mapping by synchronizing scenario recognition results; building a scenario interference recognition network on the basis of the mapping result; collecting real-time data of N communication channels of a power line carrier energy meter, and performing channel coordination decision and anti-interference enhancement; performing communication data evaluation on the basis of a comprehensive scoring function; and performing anti-interference management on the basis of channel access decision, anti-interference enhancement, and a parallel access result. The present invention solves the technical problem of poor accuracy and reliability of metering data of existing power line carrier energy meters caused by interference signals and noise when the power line carrier energy meters are in complex electromagnetic environments, and achieves the technical effect of improving the anti-interference capability and the reliability of the power line carrier energy meters.

An integrated document portfolio management and governance system and method are disclosed. The system includes a computing unit having an application interface adapted to present and / or formulate at least one input query. The system further includes aa central controller having a backend server communicably connected to the application interface of the computing unit. The backend server includes a data receiving component adapted to receive document dataset, each comprising a plurality of data elements, from a plurality of data sources in one or more formats. The backend server further includes a data ingestion module adapted to detect, normalize, and aggregate the plurality of data elements of the document dataset and subsequently store them within a central data repository. Furthermore, the backend server includes an ontology generator module adapted to create and maintain a dynamic ontology for the ingested datasets in real-time, wherein the plurality of data elements is categorized and contextualized in accordance with the dynamic ontology. Additionally, the backend server includes a governance module adapted to enforce & monitor data compliance policies and a data analysis module adapted to analyze the ingested data and generate actionable insights, wherein the actionable insights include one or more predictive analysis, data accuracy status, governance status, operational inefficiency, risk indicators, compliance gaps, and risk lineage and integrity. In operation, a user formulates an input query towards the central controller which in response is configured to automatically manage, govern & monitor the received data and subsequently visualize one or more actionable insights and / or compliance gaps onto the application interface of the computing unit.

System and methods are provided for automated SKU management. Embodiments include a user interface for receiving diverse catalog files, a Catalog Transformation module, a Real-Time Data Mesh (RTDM) module, a Master Data Governance (MDG) module, a Global Data Repository (GDR), and a Search Platform. The Catalog Transformation module, through iterative learning, transforms catalog files to a standard format and predicts categorization and attribute mapping. The RTDM module is configured to perform real-time data exchange. The MDG module validates the transformed catalogs. The GDR stores validated catalogs. Embodiments can include a Dynamic SKU Creation module and a Global Pricing Engine for real-time pricing. Embodiments improve data accuracy and SKU management, facilitating integrated order processing and fulfillment.