780 results about "Data normalisation" patented technology

The invention relates to a multi-data standard definition conflict resolution method based on a large model and a knowledge graph, and belongs to the field of public data governance and data standardization. The method comprises the following steps: extracting and preprocessing a multi-data standard definition; defining semantic representation and large model analysis: performing semantic analysis on the definition of each data item by using a large model, and generating a semantic embedding vector and a key feature; knowledge graph construction and entity alignment: constructing a knowledge graph containing all data items, and connecting data item nodes with similar semantics in different standards by adopting an entity alignment algorithm to form a candidate alignment relationship; conflict detection and type identification: aiming at the aligned data item definition, comparing attributes and values, identifying definition conflict points, and classifying and marking conflict types; and carrying out conflict resolution and unified definition generation by using a globally optimized conflict resolution algorithm. According to the method, the problems of data islands and semantic conflicts caused by inconsistent data standard definitions in the prior art are solved.

The invention provides a non-performing asset cross-scene question and answer framework based on a knowledge graph, and relates to the field of artificial intelligence and legal science and technology, and the framework comprises a framework, a knowledge fusion layer is provided with a multi-source data access module which is used for obtaining heterogeneous data of a law and regulation database, a judicial case library, an enterprise financial statement system and an asset transaction platform; configuring a legal entity analysis engine, and converting the unstructured legal text into an entity-relationship-attribute triple which can be represented by a knowledge graph based on an ontology mapping technology; setting a financial data standardization component, and converting the financial data in different formats into unified semantic representation through a predefined financial index mapping rule; the knowledge fusion layer of the framework can integrate multi-source heterogeneous data such as laws and regulations, financial data, business processes and the like through a multi-source data access module, a legal entity analysis engine, a financial data standardization component and the like.

The invention provides a financial data risk analysis method and system based on a large model, and relates to the technical field of intelligent risk control, and the method comprises the steps: obtaining financial data from a participant, carrying out the standardization and aggregation through a federated learning framework, generating a federated feature vector, constructing a bidirectional knowledge distillation model, receiving a vector, and carrying out the risk analysis of the financial data. A teacher model and a student model are deployed, a risk conduction map is constructed, a generative adversarial network is utilized to monitor changes of an enterprise association network, a risk conduction edge weight is adjusted, a financial report time sequence and a transaction event sequence are aligned through a space-time coupling encoder based on an LSTM and Transform architecture, and a multi-modal fusion feature vector is generated and input to a bidirectional knowledge distillation model; and a final risk score is obtained by combining the atlas, a grading early warning mechanism is triggered, and a risk disposal suggestion and a conduction path are generated, so that the data standardization and aggregation efficiency can be improved, and the dynamic coupling capability of a multi-modal fusion mechanism and the adaptability of a risk conduction model can be enhanced.

The invention discloses a digital financial data sharing system and method, and belongs to the technical field of digital financial data analysis, and the system comprises a data standardization management module, a data access conversion interface module, a cross-mechanism authentication authority management module, a safety audit risk control module and an intelligent analysis cooperation platform module. The data standardization management module is used for unifying a data format to be XBRL, metadata definition and cleaning rules and eliminating data semantic differences between mechanisms, and the data access conversion interface module is used for supporting multi-source heterogeneous data access and providing a standardized output interface. The cross-institution authentication authority management module is used for realizing decentralized identity authentication based on a block chain technology, zero-knowledge proof and a federated learning technology, and controlling a data access range in a hierarchical manner in combination with role authority, and the security audit risk control module is used for monitoring data flow in real time. According to the invention, on the basis of realizing digital financial data sharing, the problem of data islands can be solved, and cross-mechanism data authentication can be facilitated.

The invention belongs to the technical field of data storage, and discloses a multi-source marine environment data storage method and system based on HDF5. According to the method, a hierarchical storage framework is constructed, a domain-type-source three-level directory architecture is constructed, a space-time dimension dynamic extension mechanism is executed, and space-time composite coding is performed; designing an HDF5 nested packet storage architecture, wherein grid topological structure definition, dynamic resolution self-adaptive storage, multi-source data processing and metadata management are carried out; constructing distributed data association and indexes, including data standardization preprocessing and cross-source data association; the storage performance is optimized and expanded, wherein density sensitive blocking and layered compression are carried out. According to the method, redundant storage of a high-latitude region is greatly reduced, and dynamic adaptation of a local encryption grid is supported. According to the invention, open circulation and collaborative utilization of interdisciplinary data resources are facilitated.

The invention discloses a metadata real-time self-adaptive standardization system for multi-field data sharing. The metadata real-time self-adaptive standardization system comprises a metadata information layering module, an intelligent information acquisition module, a real-time self-adaptive standardization module and a visual management module, the metadata information layering module identifies sources of various metadata, and sequentially performs cleaning, classification and layering preprocessing on the metadata; the intelligent information acquisition module uniformly acquires metadata information and converts the acquired data into standardized intermediate representation; the real-time adaptive standardization module is used for realizing rule dynamic adaptation and real-time optimization in a metadata standardization process, is internally provided with a dynamic rule library, is used for storing dynamically generated metadata mapping rules and supports automatic loading, updating and semantic extension of the rules; and the visual management module realizes management, analysis and dynamic adjustment of metadata through a visual interface. The method is high in accuracy of processing unknown data, can adapt to different data scenes in real time, and is high in semantic interoperation capability.

The invention discloses an intelligent building safety management system based on big data, and belongs to the technical field of intelligent buildings. Comprising the following modules: a multi-source data fusion and preprocessing module, which adopts intelligent data standardization and feature extraction technologies to realize efficient integration and semantic conversion of cross-source heterogeneous data and ensure data quality and consistency; the security risk dynamic knowledge graph construction module is used for constructing a semantic association network of building security elements through a graph neural network so as to continuously self-learn and dynamically update a risk feature relationship; the intelligent risk assessment and early warning module is used for carrying out comprehensive modeling, accurate positioning, layered assessment and intelligent early warning on building safety risks; the safety decision support module is used for providing an executable risk assessment report and an emergency decision suggestion; and the safety management visualization module adopts an interactive multi-dimensional visualization technology to intuitively present the dynamic evolution process of the building safety risk, so that the risk perception and management efficiency is remarkably improved.

The invention discloses a science and technology fast message intelligence perception system based on a large language model, and belongs to the field of intelligence perception, the system comprises an information collection module, a data standardization module and a database, the information collection module obtains multi-mode science and technology fast message data; the data standardization module is used for receiving the multi-modal data output by the information collection module and executing data cleaning, structured processing and semantic standardization operation; after standardization processing, storing the data into a database; the system further comprises an intelligence value screening module, an evaluation system module and a visualization module. And the intelligence value screening module is combined with the evaluation system to identify the intelligence value, and multi-dimensional visual presentation is realized through the visualization module. The method improves the efficiency and accuracy of information recognition and perception, and has significant advantages in the aspects of processing mass information and enhancing the information perception ability.

The invention discloses an AI-based medical and health care integrated data management method and system and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data preprocessing and data standardization of collected multi-source medical and health care data, and obtaining a standardized medical data set; performing classification and semantic annotation on the data of the standardized medical data set by using a preset medical ontology, and constructing a structured medical knowledge graph; performing virtual mapping based on the structured medical knowledge graph by adopting a digital twinborn model to form a virtual medical twinborn body; based on the virtual medical twins, combining real-time monitoring data and historical patient flow data, through multi-dimensional feature extraction and pension mode recognition, dynamically optimizing and presetting a personalized medical scheme and a resource scheduling strategy, and outputting a medical care service optimization scheme; generating a target service list in combination with the analyzed doctor-patient dialogue text, and distributing the target service list to a preset pension service cooperation terminal; the method has the effect of improving the real-time performance and the accuracy of the old people medical care service strategy.

The invention relates to the technical field of data processing, and discloses a project cost prediction method and system based on a large model, and a storage medium. The method comprises the following steps: standardizing historical cost data to construct a dynamic cost feature knowledge base; inputting the knowledge base into a Transform pre-training large model, and performing fine tuning to obtain a predicted value; obtaining an adjustment coefficient matrix through item similarity clustering and error learning; the design parameters are matched with a knowledge base, and a hierarchical cost table is calculated by using a heterogeneous graph network; and performing time correction on the cost table based on the adjustment coefficient to obtain a prediction result. According to the method, accurate prediction and dynamic adjustment of the project cost are realized, the problem of insufficient element association expression in traditional cost prediction is solved, targeted correction can be carried out according to time and project features, and the prediction accuracy and adaptability are remarkably improved.

The invention belongs to the field of bioinformatics, and relates to a molecular property prediction method based on multi-modal gating and comparative learning, which comprises the technologies of comparative learning, graph neural network, cross-modal alignment, gating attention and the like. Firstly, data standardization and graph construction are carried out, and molecular fingerprint embedding is extracted; secondly, a heterogeneous dual-channel graph coding architecture is adopted, one path captures atom short-range interaction through an attention mechanism, the other path integrates a molecular global structure and long-range dependence, and complementary molecular representation is generated; then, a cross-modal attention mechanism is introduced, bidirectional association of graph and fingerprint features is achieved, and modal weights are adaptively and dynamically distributed through a gating fusion module; and finally, a comparison pre-training strategy is adopted, a molecular graph and fingerprints are utilized to construct a sample pair, and discriminative molecular representation is learned on unlabeled data. According to the method, the accuracy of molecular property prediction is remarkably improved, and an efficient and reliable calculation tool is provided for virtual drug screening and lead compound optimization.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The invention discloses a submarine cable insulation multi-field aging prediction method and system, and relates to the technical field of material science and engineering. An existing model has the problems that parameter interaction effect analysis is insufficient, feature weight distribution is not scientific, a data standardization method is insufficient, and the capacity of capturing dynamic response and nonlinear aging behaviors is weak. The method comprises the steps of multi-physics field accelerated aging experiment design and data acquisition, key characteristic parameter screening through grey correlation analysis, support vector machine modeling and optimization, dynamic aging behavior evaluation and service life prediction and feedback adjustment. According to the technical scheme, aging behaviors are comprehensively captured through a multi-physics field cooperation experiment, key parameters are scientifically screened in combination with grey correlation analysis, a nonlinear relation is optimized through support vector machine modeling, long-term prediction precision is improved through dynamic feedback adjustment, and reliability is enhanced through quantitative life evaluation; the aging characteristics of the submarine cable insulating material in multiple physical fields are comprehensively and accurately reflected, and a scientific basis is provided for multi-dimensional evaluation of the aging performance of a high polymer material.

The invention relates to the technical field of atmospheric environment monitoring, in particular to an atmospheric pollution source tracking and early warning system based on deep learning, which comprises a multi-source data acquisition layer for acquiring multi-dimensional data of atmospheric pollutant concentration, weather, geography and the like through multi-element equipment such as satellite remote sensing and ground sensors; the data preprocessing layer completes data cleaning by using a statistical method and a machine learning algorithm, and realizes heterogeneous data standardization processing through feature extraction, multi-source integration and space-time alignment, and the deep learning model layer realizes heterogeneous data standardization processing by means of a space-time convolutional neural network, a graph neural network and a long-short term memory network in combination with a space-time attention mechanism. The traceability analysis layer is combined with a back diffusion algorithm and a geographic information system to position a pollution source and generate a thermodynamic diagram. And the early warning decision-making layer sets threshold early warning through transfer learning of the adaptive area, and generates an optimal pollution control scheme based on reinforcement learning.

The invention discloses an enterprise data link management and value management method and system, and the method comprises the steps: carrying out the real-time capturing of multi-source heterogeneous data of all business systems in an enterprise through a distributed data collection engine, and recognizing the types of original data in different formats through a preset data source adapter; if structured data is detected, a relational database connector is adopted for extraction, and if the structured data is recognized as unstructured data, a document analysis module is started for content extraction, and an initial data set containing metadata tags is obtained; performing format conversion and field mapping on the initial data set according to a pre-established data standardization rule base, eliminating duplicate records and abnormal values through a data cleaning algorithm, performing automatic evaluation on a data quality grade by adopting a naive Bayes classifier, if a data quality score is lower than a preset threshold value, triggering a data recovery process, and if the data quality score is lower than the preset threshold value, performing data recovery. And standardized data meeting a unified standard is obtained. The normativity and value utilization efficiency of data management are effectively improved.

The invention provides a water conservancy industry electronic dark bidding document enterprise internal examination method and system based on artificial intelligence, and belongs to the field of water conservancy industry bidding. Performing automatic pre-auditing on the bidding file: performing compliance inspection and integrity inspection, identifying problems existing in the bidding file, and providing improvement suggestions to ensure that the problems conform to the basic requirements of electronic dark label review; carrying out data standardization and cleaning on the bidding file: carrying out semantic extension and ambiguity elimination, identifying images, tables and handwritten contents in the bidding file, identifying a main body structure of the water conservancy design drawing, extracting engineering quantity list data, and carrying out compliance verification; identifying an abnormal behavior in the bidding file through a machine learning model; and an internal examination decision tree is constructed according to preset key indexes and score weights of the water conservancy project, an interpretable artificial intelligence algorithm is utilized to carry out internal examination decision making on the bidding document, an internal examination score result is fed back, and specific score deduction reasons are explained. And the standardization and the accuracy of internal examination of the bidding document are improved.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention discloses an intelligent control method and system for an iron wire rust-proof treatment process, and relates to the technical field of intelligent iron wire rust prevention. The intelligent control method and system for the iron wire rust-proof treatment process comprise the steps that S1, a detection terminal is deployed to collect batch characteristics, process environment and treatment process data, standardized preprocessing is conducted, and a database is constructed; s2, performing comparative analysis on the personalized antirust process parameters based on a database, and dynamically adjusting parameter binding weights and fine tuning priorities; s3, analyzing the deviation degree of the technological parameter execution value and the reference value, evaluating the matching degree, and updating the adjustment amplitude and the correction strategy; and S4, integrating parameter comparison and deviation analysis results, combining the characteristic matching degree of the previous batch, and optimizing rapid identification and self-adaptive adjustment of antirust process parameters. The problems that in the existing iron wire manufacturing and anti-rust treatment process, the capacity of rapidly recognizing and adaptively regulating and controlling technological parameters based on batch characteristics is lacked, and personalized matching and quality consistency improvement of the anti-rust technology are difficult to achieve are solved.

The invention relates to a cargo transportation track real-time tracking method based on the Internet of Things, and the method comprises the steps: carrying out the node collection and multi-source fusion of sensor data (including GPS, RFID, timestamps and the like) of all links of logistics, and employing data standardization and time sequence verification, thereby achieving the consistency processing of different devices and node data. The method is characterized in that standardized trajectory data and breakpoint detection data are stored in a distributed manner by using a block chain non-tampering and multi-party consensus mechanism, third-party multi-source data and a fuzzy reasoning model are fused, and confidence completion is performed on trajectory breakpoints. The system can output a complete and traceable trajectory path, anomaly recognition and responsibility attribution result, and provides a visual and third-party notarization interface. According to the method, the authenticity, the integrity, the space-time consistency and the responsibility traceability of the logistics track data flow are remarkably improved, and the logistics full-link monitoring and risk management capabilities are enhanced.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention discloses a data acquisition method and device for an energy management system, equipment and a medium. The energy management system is used for managing multiple power plants. The method comprises the following steps: creating a to-be-constructed big data platform which comprises a data acquisition layer, a data processing layer and a data storage layer; deploying a multi-protocol adapter cluster on a data acquisition layer; constructing a data standardization pipeline in a data processing layer, and generating a multi-level security label in combination with a data source security level, a service association degree and an updating frequency; a hierarchical storage framework is designed in a data storage layer and used for constructing a redundant index table crossing storage nodes and a data blood relationship graph based on a graph database; and deploying a dynamic security gateway between the data acquisition layer and the data processing layer, dynamically selecting a corresponding encryption algorithm according to the data security label, and completing the construction of a big data platform, so as to complete the data acquisition of the plurality of power plants based on the big data platform. And high-reliability, traceable and safe data support is provided for cooperative management of multiple power plants.

The invention relates to the technical field of anti-terrorism early warning, in particular to an intelligent anti-terrorism early warning system based on image recognition and big data analysis, and the system comprises a multi-source data collection module which is used for collecting video streams, image data and environment parameters through a monitoring camera, a sensor and an Internet of Things device in a deployment region, executing target detection and desensitization operation at the edge computing node, and caching original data to a distributed message queue; and the data standardization processing module is connected with the message queue and is used for carrying out space-time alignment and semantic mapping on the multi-modal heterogeneous data. According to the invention, monitoring videos, sensor data, Internet of Things information and the like are preprocessed and desensitized at edge nodes, space-time alignment and semantic unification are completed through a data standardization processing module, and cross-modal feature fusion is realized through a multi-modal fusion analysis module, so that barriers among different types of data are broken through, and the accuracy of data fusion is improved. Scattered information forms an organic whole, and a comprehensive and consistent data basis is provided for abnormal behavior recognition.

The invention relates to the technical field of geothermal exploitation, and discloses a geothermal well group intelligent cooperative regulation and control method for geothermal exploitation, which comprises the following steps: collecting multi-source heterogeneous data in real time through multiple types of sensors deployed in a geothermal well group and a surrounding environment, data standardization, time sequence alignment and noise filtering are carried out through a uniform interface and a communication protocol, and a multi-dimensional monitoring database of the whole geothermal exploitation process is constructed; on the basis of multi-source heterogeneous data, historical mining data in a database is monitored in combination with a geologic structure of a geothermal reservoir, a seepage-heat conduction coupling mechanism and multiple dimensions. According to the invention, by deploying multiple types of sensors and constructing a multi-dimensional monitoring database, real-time acquisition and standardized processing of high-precision and multi-source heterogeneous data in the whole geothermal exploitation process are realized, a reliable data basis is provided for subsequent intelligent decision making, and the sensing ability of the system to complex geology and exploitation environments is significantly improved.

The invention relates to an intelligent continuous rigid frame bridge construction period deformation prediction system. The system comprises a construction data acquisition module, a data primary processing module, a feature extraction model construction module, a mode special model construction module and a deformation prediction module. According to the invention, original data is obtained through data acquisition; a data primary processing method of data standardization, weighted time sequence alignment, effect decoupling and data set segmentation is adopted; a multi-branch deep learning model is adopted as a feature extraction model, the time dynamics, the spatial topological relation and the physical mechanics priori of bridge structure response are integrated, and the completeness and the discrimination ability of feature expression are remarkably improved; a progressive strategy of firstly extracting and clustering features and then grouping and independently modeling is adopted, so that not only is the specificity of different construction stages fully considered, but also the prediction precision is improved through targeted model optimization, and the problem of mode diversity in a complex construction system is solved.

The invention relates to the technical field of big data analysis, in particular to an operation management system and method based on financial marketing big data, and the system comprises a data standardization module, a customer portrait disambiguation module, a strategy matching module, a real-time transmission module and an operation strategy generation module. According to the method, a data table with a unified structure is constructed through combined judgment of field semantics and numerical rules, the data consistency is improved, customer information integration is executed based on field conflict time, credibility level and logic verification, the recognition accuracy is enhanced, and the matching relation is judged by combining the transaction amount, the product association degree and the interaction frequency with strategy attributes; according to the method, strategy dynamic screening is achieved, pushing content is screened according to the strategy emergency degree and the channel state, the transmission efficiency is improved, customer clicking, conversion and feedback participate in behavior trend judgment, strategy updating linkage is supported, an overall processing chain is of a closed-loop structure, and the fusion accuracy and the strategy response timeliness are improved under the conditions of data fragmentation and multi-source heterogeneous.

The invention relates to the technical field of liquid caustic soda concentration regulation and control, and discloses a liquid caustic soda concentration monitoring and automatic regulating system based on Internet of Things and data fusion. A liquid caustic soda concentration data acquisition module of the system is connected with a plurality of sensors through the Internet of Things to acquire spatio-temporal information such as liquid caustic soda concentration, temperature, flow velocity and the like; the edge node operation and maintenance analysis module is used for monitoring operation and maintenance information such as memory, bandwidth and CPU utilization rate of the edge computing node; the data fusion preprocessing module performs standardization processing on the sensor data to generate a data matrix; the liquid caustic soda concentration anomaly detection module is used for extracting fusion features based on the data matrix and identifying abnormal nodes; the concentration adjustment optimization module is used for generating an optimization adjustment parameter set according to the abnormal nodes; the automatic adjusting control module is used for adjusting the valve opening and the pump speed according to the parameter set; and the distributed storage management module is used for storing sensor data and operation and maintenance information. The system realizes comprehensive monitoring and accurate adjustment of the concentration of the liquid caustic soda, and improves the operation stability and reliability of the system.

The invention provides a supplier service quality assessment management system and method based on dynamic weight optimization, and belongs to the technical field of weight adjustment. The total index module is used for determining quality assessment indexes and sub-indexes thereof according to supplier service contents and contract requirements; the data acquisition module is used for acquiring historical performance data of a target supplier and standardizing the historical performance data to obtain standard performance data; the first scoring module is used for determining a first score of the quality assessment index according to the initial weight of the quality assessment index and the quantification method in combination with the standard performance data; the dynamic weight module is used for generating a weight adjustment strategy according to a business scene of the standard expression data, and obtaining a first weight of the quality assessment index after adjusting an initial weight; and the scoring module is used for calculating a quality assessment score of the target supplier according to the first weight and the first score. The accuracy and the dynamic adaptability of supplier service assessment are improved, and quality assessment and decision support are optimized.

The invention relates to the technical field of intelligent regulation and control, and discloses a data-driven workshop production equipment flow intelligent regulation and control method. Comprising the steps of S1, equipment state data acquisition in a data acquisition stage, S2, data cleaning abnormal value processing in a data preprocessing stage, S3, equipment health degree analysis model in a data analysis and modeling stage, S4, equipment scheduling decision in an intelligent regulation and control decision stage, and S5, regulation and control effect feedback and effect evaluation index production efficiency in an iteration stage. According to the data-driven workshop production equipment flow intelligent regulation and control method, the data acquisition and processing efficiency is improved, the integrity of equipment state data is improved to 98% through multi-dimensional sensor deployment (vibration 100Hz sampling and temperature 30s / time), the time consumed for automatic correction of an abnormal value is shortened from 30min to 1min, and the efficiency of automatic correction of the abnormal value is improved. And after data standardization, the multi-dimensional fusion analysis efficiency is improved by 4 times, and a foundation is laid for accurate regulation and control.

The invention relates to the technical field of computers, and discloses a livelihood appeal trend pre-judgment method and system based on quantitative analysis, and the method comprises the steps: obtaining multi-source heterogeneous livelihood appeal data, carrying out the standardization processing, generating an integrated data set, extracting trend features and fluctuation features, and determining an evolution mode of livelihood appeals. Constructing a saturation model based on the dynamic variation amplitude, identifying a distribution interval exceeding a risk judgment threshold, and generating a critical point trigger signal when the dynamic variation amplitude exceeds a risk fluctuation judgment threshold; calculating a correlation strength index variable quantity, when the correlation strength index variable quantity exceeds a correlation deviation judgment threshold value, triggering a parameter optimization mechanism of a saturation model, generating prediction distribution of a time window and a risk level, and updating extraction parameters of volatility characteristics based on the prediction distribution and historical time sequence data; and generating a final livelihood appeal trend prediction report according to the updated extraction parameters. According to the method, accurate resource allocation driven by data can be realized, and the immediate response capability of emergencies is enhanced.

System and methods are provided for achieving data standardization and normalization through an Agnostic Data Format (ADF) architecture. ADFs systems and processes provide a transformative bridge, enabling disparate data sources to converge into a unified and standardized format within the Real-Time Data Mesh (RTDM) framework. This dynamic process utilizes Artificial Intelligence (AI) and Machine Learning (ML) algorithms to interpret and align diverse data attributes. The ADF management system, integrated into a dynamic event-driven architecture, allows vendors to interact with RTDM by translating and standardizing their data. The synchronized data integrates canonically, incorporating real-time updates and collaborative decision-making across the distribution platform. This innovative approach enhances operational efficiency, enables data-driven decision-making, and provides users improved ability to use data within the distribution ecosystem.