2370 results about "Data integration" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention provides a task scheduling method and system based on multi-agent collaboration, and the method comprises the steps: firstly receiving an externally input target task demand text, carrying out the structural analysis processing, obtaining a task element set, calling a task planning module of a main agent to carry out the planning disassembly of the task element set, generating a sub-task sequence set, and carrying out the calculation of the sub-task sequence set; the sub-task units are in one-to-one correspondence with preset professional agent types, the sub-task units are allocated to matched professional agents for execution according to professional attribute identifiers of the sub-task units, a sub-task execution instruction is generated, and each professional agent executes sub-task operation based on the instruction, generates a sub-task execution result set and feeds back the sub-task execution result set to the main agent; and finally, the main agent performs multi-source data integration processing on the sub-task execution result set to generate a final task result file meeting task output requirements, so that task scheduling efficiency, quality and flexibility can be improved, and complex task requirements are met.

The invention relates to a medical information processing and intelligent data management technology, and discloses a hospital multi-source heterogeneous data integration management system and method based on an EMPI, and the system carries out the real-time subscription and batch extraction of the data of an HIS, an EMR, an LIS, a PACS, a DRG and a pre-hospital first-aid system through an interface adapter, and generates a unified main index identifier through the matching of the EMPI, and carries out the real-time subscription and batch extraction of the data of the HIS, the EMR, the LIS, the PACS, the DRG and the pre-hospital first-aid system. Field-level cleaning, standardization and semantic mapping are carried out on the free text and the structured data, an intermediate standardized record set is formed, an event graph is constructed, event cluster recognition is carried out in combination with time, clinical entities, doctor seeing scenes and department similarity, consistency judgment and EM iterative fusion are carried out on the basis of data source reliability, and event cluster recognition is carried out. And outputting a unified patient file set and a downstream service interface. According to the invention, high-precision integration, standardized management and traceable application of multi-source data are realized, and a reliable data basis is provided for clinical decision support, medical insurance management and scientific research.

The invention discloses an energy consumption model intelligent construction system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the system comprises an Internet of Things multi-source data collection module, a data cleaning and space-time calibration module, a multi-source data semantic fusion module, a dynamic energy consumption relation graph construction module, an intelligent decision engine module and an edge-cloud collaborative deployment module. According to the method, multi-source data are fused through a Transform multi-head self-attention mechanism, a dynamic energy consumption relation graph is constructed by using a graph neural network, and dynamic modeling and intelligent regulation and control of energy consumption are realized in combination with an edge-cloud hierarchical decision architecture; the method comprises the steps of data acquisition and standardization, cleaning calibration, semantic fusion, graph modeling, hierarchical decision making and collaborative execution. According to the method, the problems of insufficient data integration and model staticization of a traditional system are solved, the accuracy, real-time performance and global optimization capability of energy consumption management are improved, the method is suitable for scenes such as intelligent buildings, the energy efficiency is remarkably improved, and the data security is guaranteed.

A wearable system can include an electronic device configured to measure one or more physiological parameters of a patient and a wearable device configured to operably position the electronic device. The electronic device can have at least one light emitter and at least one light detector and be configured to measure at least a pulse oximetry measurement. The wearable device can have a main body with a cavity configured to position the electronic device, a securement portion connected to the main body and configured to secure the main body to the patient, and storage component configured to store patient identification data associated with the patient. When the electronic device and the wearable device are secured to one another, the patient identification data can be transferred from the wearable device to the electronic device.

A system configured to facilitate monitoring a patient when the patient transitions between environments. The system can have an in-room display terminal configured to display indicia of a health of the patient for electronically monitoring the health of the patient within a healthcare environment. The system can receive, via the in-room display terminal, a request to initiate monitoring the patient with the in-room display terminal at the healthcare environment; access historical physiological data associated with the patient and generated by a home monitoring device before the patient enters the healthcare environment; access real-time physiological data associated with the patient and originating from a physiological monitoring device coupled to the patient within the healthcare environment; generate one or more physiological parameters from the real-time physiological data and the historical physiological data; and cause the in-room display terminal to display indicia of the one or more physiological parameters.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention discloses a data import and field intelligent matching method for a low-code platform, and particularly relates to the technical field of low-code platform data processing. Constructing a field initial feature vector, extracting a standard field and historical matching features thereof from the target data model, and calculating a semantic matching score matrix by using a multi-source feature fusion model; screening the initial mapping table based on a dynamic threshold value, performing statistical deviation analysis in combination with a field value distribution difference measurement feature delta, and when delta exceeds a preset threshold value, calling a value mapping correction model to complete rematching to obtain an optimized mapping table; if it is detected that user history adjustment exists in the same source file type, re-correction is carried out based on a user behavior log, and a final field mapping table MP3 is output; finally, the MP3 is applied to data import, and automatic matching and binding are achieved. The method improves the accuracy and automation level of field matching, and is suitable for a multi-source heterogeneous data integration scene.

The invention relates to the technical field of data processing and storage management, and discloses a multi-source index data intelligent storage management method and system, and the method comprises the steps: building a unified data integration and processing channel, and forming a standardized index data set; meanwhile, an enterprise-level index unified management system is established, and a credible index data set is generated; the credible index data set is stored through an optimized storage strategy and a data organization model, a data consanguinity association map is constructed based on data full-link processing, and visual and traceable index data assets are obtained; according to the method, a real-time quality evaluation engine is used for carrying out continuous monitoring and intelligent warning on all links of synchronization, processing, storage and service of an index data set so as to construct a data anomaly detection and repair mechanism, and quality controllable supervision is carried out on the whole processing process of the index data set through a data quality billboard and an anomaly processing workflow; therefore, the intelligent management level and the data quality guarantee capability of multi-source index data processing and storage can be improved.

The invention relates to the technical field of transformer substation intelligent management, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with a multi-source data integration and collaborative management function. Comprising a multi-source data acquisition unit; the edge intelligent processing unit is used for carrying out local preprocessing, feature extraction and abnormal early warning on the acquired multi-source heterogeneous data of the transformer substation, and realizing data noise reduction, abnormal recognition and equipment health degree evaluation through a lightweight AI algorithm and a hybrid communication protocol carried by the edge computing terminal module; and a data integration collaboration unit. According to the invention, through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multi-dimensional data such as electric power parameters, power environment, fire protection and security protection and the like are incorporated into a monitoring system, feature layer association analysis is realized by depending on a Jousseme distance and a Dempster synthesis rule, the assessment limitation of single equipment and single-dimensional data in a traditional scheme is broken through, and the method has the advantages of high reliability and high reliability. And a more comprehensive state basis is provided for equipment health degree research and judgment.

The invention relates to the technical field of coal mine exploration, in particular to a coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, which comprises the steps of S1, integrating multi-source geological data; s2, data processing and feature enhancement; s3, machine learning modeling; s4, target spot optimization and dynamic verification; and S5, geological modeling and risk grading. According to the coal field geological anomalous body accurate positioning and intelligent prediction method based on machine learning, a depth domain joint data set is constructed, multi-dimensional features such as seismic amplitude, lithology coding, fracture index and fault distance field are integrated, and multi-source data fusion and physical constraint machine learning are realized through a feature enhancement technology; locking a high-uncertainty region based on the prediction variance, and dynamically updating a target spot through a Gaussian process proxy model to realize targeted drilling and dynamic closed-loop optimization; a multi-attribute risk fusion model is constructed, a four-level risk map is output through the risk factors, grouting resources are guided to be preferentially put into a high-risk area, and risk grading early warning processing is achieved.

The invention discloses a multi-disaster disaster risk grade coupling evaluation method. The method comprises the following steps: establishing a multi-dimensional database covering multiple disaster types; key features related to the disaster risk are extracted from the multi-dimensional database; establishing a risk assessment model according to the key features, and determining a plurality of single-disaster risk levels; identifying association rules, causal relationships or space-time coupling modes among different disasters by using a big data mining algorithm; coupling weights among different disasters are determined, and a coupling model is constructed to convert a coupling relation into a quantitative risk superposition effect; based on the coupling model, a single-disaster risk and a coupling effect are integrated, a multi-disaster comprehensive risk level is generated, through multi-dimensional data integration, quantitative coupling modeling and a dynamic adjustment mechanism, advantages are formed in evaluation precision, social and economic adaptability and model adaptability, and the method is more suitable for scenes needing accurate risk grading and decision support.

The invention relates to the field of environment monitoring, discloses an ecological protection red line holographic dynamic monitoring and early warning method and system, and aims to solve the defects of an existing monitoring and early warning mechanism in the aspects of timeliness, data integration and intelligent analysis. According to the method and the system, multi-source heterogeneous data are acquired and fused, a space bottom line digital model is constructed, space-time situation awareness analysis and change abnormity identification are performed, risk assessment early warning triggering is performed, and early warning issuing and processing feedback are managed. According to the invention, high-precision holographic acquisition and fusion of multi-source data can be realized, the accuracy and timeliness of change abnormity identification are improved, passive early warning is changed into active early warning, a monitoring disposal closed loop is formed, supervision intelligence and treatment efficiency are improved, and ecological safety is guaranteed.

The invention discloses a plot pollution monitoring, predicting and early warning system and method, and relates to the technical field of environment monitoring and pollution early warning, and the method comprises the steps: obtaining multi-source data of a target plot; performing preprocessing, feature extraction and fusion on the multi-source data; inputting the multi-source data into the dynamic pollution diffusion model to obtain a pollutant concentration field of the target plot; jointly inputting the fusion data and the pollutant concentration field into a land parcel pollutant concentration prediction model for prediction to obtain a pollutant concentration prediction value of the target land parcel in a future target time period; comparing the pollutant concentration predicted value of the target land parcel in the future target time period with the early warning threshold values of all levels, and generating an early warning instruction of the corresponding level according to a comparison result; generating corresponding early warning information according to the early warning instruction; and executing corresponding early warning measures according to the early warning information. According to the invention, through multi-dimensional data integration and model optimization, scientific assessment and hierarchical management and control of pollution risks can be realized.

The invention relates to the technical field of power transmission and distribution production, and discloses a data integration and multi-modal diagnosis method based on a power transmission and distribution scene, and the method comprises the following steps: S1, enhanced integration of multi-source heterogeneous data: collecting time sequence monitoring data, text procedures and image data of power transmission and distribution equipment, constructing an equipment topological correlation graph through a graph attention neural network, and carrying out the enhanced integration of the multi-source heterogeneous data; node feature embedding is optimized through self-supervised comparative learning, an adversarial variational auto-encoder is designed for edge data, and an enhanced sample is generated in combination with physical constraints of equipment. According to the data integration and multi-modal diagnosis method based on the power transmission and distribution scene, the field adaptability and reliability of a diagnosis result are improved while the model fine tuning cost is reduced, and the knowledge migration problem of a general model in the power transmission and distribution scene is solved; the introduction of a dynamic knowledge graph and a multi-dimensional evaluation system realizes the real-time integration of new regulation knowledge and the comprehensive evaluation of model performance, and ensures the sustainable evolution ability and decision transparency of the diagnosis model.

The invention relates to the technical field of oil and gas equipment operation control, in particular to an oil and gas equipment operation parameter self-adaptive fine tuning method and system, and the method comprises the steps: constructing an operation parameter optimization library driven by reinforcement learning, defining an action space containing equipment types, working condition modes and key parameter combinations, predicting accuracy, response efficiency and safety compliance are optimized in combination with a multi-target reward function, an optimized instruction set is generated, safety perception mixing precision fine adjustment is carried out in parallel, calculation precision is configured in a layered mode, errors are monitored in real time, a high-precision mode is switched if the precision exceeds the limit, and a reservoir engineering equation mechanism constraint matrix is embedded to guide parameter updating. A generative adversarial network is utilized to synthesize fault data, a Darcy seepage causal engine is integrated to correct a loss function, the system comprises an action space construction unit, a precision dynamic scheduling unit, a mechanism constraint embedding unit and a data causal cooperation unit, and the method and the system realize data-mechanism fusion, balance precision and real-time performance, and improve equipment operation safety and adaptability.

The invention discloses a project performance evaluation and risk early warning method for multi-source heterogeneous scientific research data, and relates to the technical field of scientific research project management, and the method comprises the steps: collecting the multi-source heterogeneous scientific research data, cleaning, converting, filling missing values, and storing in a unified format; extracting performance and risk features, and fusing to obtain a fused feature set; combining an analytic hierarchy process, an entropy weight method and the like to construct a dynamic weight performance evaluation model; constructing a multi-modal risk early warning model by adopting multiple algorithms and optimizing a threshold value; collecting data in real time to update a feature set, and dynamically adjusting the model; and visually outputting a result, generating an improvement suggestion, and forming closed-loop feedback. According to the method, the multi-source data processing efficiency and evaluation accuracy are improved, the risk early warning timeliness and adaptability are enhanced, a management closed loop is formed, and the problems of difficult data integration, evaluation lagging, insufficient early warning and the like of a traditional method are solved.

The invention relates to the technical field of industrial internet, and discloses a multi-dimensional data integration and dynamic risk assessment method for enterprise purchase anomaly detection, which comprises the following steps: establishing a reliability verification matrix of multi-dimensional purchase data, and extracting abnormal data points; identifying a cross correlation mode of the abnormal data points, and constructing a purchase behavior interaction graph of the target enterprise; analyzing a risk evolution path of the purchase risk factor, and calculating a risk conduction entropy value; and according to the risk conduction entropy value, generating a risk grading label of the purchase risk factor so as to output a purchase anomaly detection report of the target enterprise. According to the invention, the accuracy of enterprise purchase anomaly detection can be improved.

The invention provides a financial statement generation method and system based on cloud computing, and the method comprises the steps: collecting multi-source data of an internal business system of an enterprise through a centralized data integration platform based on a cloud computing environment, carrying out the classified storage, and constructing a data set; verifying the data set, and obtaining key index fields related to financial statement generation in the verified data set; performing grouping calculation on the key index fields by adopting a data aggregation algorithm to form a financial data summarization result; dynamically mapping the financial data summarization result by adopting a report generation engine, and matching with a preset financial report template to obtain a report data framework; and performing secondary verification and encryption on the report data framework to obtain a final financial report. According to the technical scheme, automation, real-time performance and unmanned performance of financial statement generation are realized through full-link innovation of edge intelligent acquisition, streaming processing, incremental aggregation and zero-trust security.

The invention discloses an intelligent monitoring method and system for the construction progress of a building decoration curtain wall. The method comprises the steps that multi-source construction data of a construction site are collected through Internet of Things equipment and a high-definition camera; constructing a multi-dimensional tensor model and extracting data association features by applying a local reversible mapping algorithm to generate a standardized feature data set; performing parallel computing through shared memory hierarchical process mapping by utilizing the standardized feature data set, and outputting a project progress state evaluation result; constructing a decision diagram based on the project progress state evaluation result, and generating a construction progress deviation report; predicting a construction progress trend by adopting a sample optimal private regression algorithm and generating an optimization scheme; and visually displaying the construction progress prediction report and carrying out early warning. According to the invention, the problems of low data integration efficiency, delayed decision response and weak multi-dimensional data association analysis capability of traditional curtain wall construction monitoring are solved, and intelligent and efficient monitoring of the construction progress of the building decoration curtain wall is realized.

The invention relates to the technical field of ecological restoration, discloses an ecological restoration planning monitoring method and system based on a multi-source data technology, and aims to solve the problems that in existing ecological restoration monitoring, dynamic change perception of an ecological system is insufficient and restoration planning management is lagged due to static discretization and multi-source heterogeneous data integration deficiency of a traditional means. According to the invention, through constructing a multi-source heterogeneous data acquisition and preprocessing module, a multi-modal data fusion feature extraction module, an ecological system dynamic state modeling evaluation module, an ecological restoration planning dynamic optimization module and a real-time monitoring and effect evaluation module, closed-loop management is realized through feedback. According to the technical scheme, traditional monitoring limitation can be overcome, deep data fusion is achieved, planning dynamic optimization and decision accuracy are improved, and scientificity and effectiveness of ecological restoration are remarkably improved.

The embodiment of the invention provides an equipment fault prediction management method and device based on a large model, and the method and device achieve the accurate evaluation of a state through the innovative design of a multi-modal feature fusion model, data integration and feature learning. A fault diagnosis system is constructed, and a reliable root cause analysis mechanism is established in combination with semantic analysis and case retrieval. Maintenance guidance is introduced, and the feasibility of a maintenance scheme is ensured through experience precipitation and priority evaluation. According to the method, the defects of the traditional technology in the aspects of feature extraction, fault diagnosis, maintenance guidance and the like are effectively overcome, and technical guarantee is provided for equipment management.

The invention relates to the technical field of land parcel management, in particular to a land parcel full life cycle compliance management and operation monitoring system, which comprises a multi-source data acquisition module for acquiring multi-dimensional land parcel data such as spatial geographic ownership planning and approval from a plurality of heterogeneous data sources in real time; the data fusion and storage module performs data cleaning and standardized fusion, adopts a distributed cloud storage architecture and introduces data version management to support historical tracing; the intelligent compliance judgment engine generates a compliance state judgment result and a compliance risk report based on the dynamic compliance rule base and machine learning; the interactive visual monitoring module is used for dynamically displaying the compliance state of the land parcel space information life cycle stage and the key index trend; and the automatic early warning and intervention module is used for performing graded early warning and triggering a rectification or emergency intervention process. According to the invention, the problems of difficulty in compliance judgment, low efficiency, non-visual monitoring and slow risk response of existing land parcel management data integration are solved, and intelligent compliance management and operation monitoring are realized.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention relates to the technical field of energy storage control, and discloses a demand control method and system based on industrial and commercial energy storage, and the method comprises the steps: obtaining and carrying out the standardized processing of a real-time load demand, an energy storage state and power grid dispatching data; establishing a load prediction model to generate demand baseline parameters, and constructing a dynamic adjustment rule base; the energy storage output is verified in real time, and the dynamic response trend of the power difference value is analyzed; and generating a demand control index to determine a charging and discharging strategy. The system comprises a load monitoring module, a strategy generation module, a dynamic execution module and a control judgment module which are respectively used for realizing data acquisition and processing, model construction, output verification analysis and strategy judgment functions. According to the scheme, through multi-dimensional data integration, dynamic rule base optimization and multi-constraint coordination technologies, the precision and economical efficiency of industrial and commercial energy storage demand control are improved, the demand electric charge can be effectively reduced, the electric energy utilization rate is improved, and the method is suitable for energy storage management and power grid collaborative optimization of industrial and commercial users.

The invention belongs to the technical field of transformer substation fault diagnosis, and relates to a transformer substation equipment health diagnosis and operation and maintenance suggestion generation method, in the invention, a dynamic portrait of an equipment state is constructed through acquisition and preprocessing of multi-source heterogeneous data, comprehensive extraction and fusion modeling of equipment operation characteristics are realized, and on the basis, the equipment health diagnosis and operation and maintenance suggestion generation method is provided. A dynamic equipment association diagram is introduced, the health state of equipment can be comprehensively evaluated and a risk propagation path can be identified by describing an association relationship between the equipment and combining a risk propagation model, and finally, an interpretable operation and maintenance suggestion is automatically generated based on a case reasoning and strategy optimization method, so that the problems of insufficient data integration capability and high risk propagation efficiency of a traditional method are effectively solved. The risk assessment dimension is single; and the operation and maintenance suggestion generation lacks intelligent support.

The invention relates to the technical field of data integration, and particularly discloses a perfluoro / polyfluoroalkyl substance multi-source exposure data integration risk assessment system, which is characterized in that environmental exposure data of perfluoro / polyfluoroalkyl substances is acquired through a multi-source environmental sensor array, and a PFAS multi-mode exposure feature database is established; carrying out pollution source isotope fingerprint analysis, and obtaining source contribution rate distribution maps of three pollution sources of industrial emission, surface runoff and atmospheric settlement through a nonlinear source analysis algorithm; constructing a three-dimensional geographic information dynamic migration model according to the source contribution rate distribution map, and generating a multi-medium dynamic migration flux matrix; a composite risk assessment model is established based on the multi-medium dynamic migration flux matrix, probability risk assessment is executed in combination with an ecological toxicity threshold database, and a space gridding risk grade map is output; the method not only fills the blank of the prior art in the aspects of multi-medium dynamic modeling and nonlinear source analysis, but also provides powerful technical support for environmental pollution control and ecological risk prevention and control.

The invention relates to the technical field of power plant operation monitoring, in particular to an intelligent power plant operation data monitoring method based on artificial intelligence assistance. The method comprises the following steps: extracting multi-source power plant operation data, detecting an abnormal value, obtaining optimized power plant operation characteristics through interpolation optimization processing, analyzing the multi-source data by using an artificial intelligence technology, predicting aging state characteristics of equipment, carrying out difference comparison, identifying potential hardware operation faults, and carrying out fault diagnosis. The method comprises the following steps: identifying a cooperative connection path of equipment through operation data, predicting a cooperative fault risk node, mapping a risk physical connection relationship of the equipment, performing fault transfer simulation based on the risk physical connection relationship, generating hardware fault transfer data, and determining hidden danger data when each hardware has an operation fault; and integrating the hardware operation fault information and the corresponding data into operation fault monitoring data, and carrying out fault alarm. According to the invention, intelligent and systematized power plant operation monitoring is realized, and construction and development of an intelligent power plant are promoted.