710 results about "Application module" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention is suitable for the technical field of brushless motor control, and provides an intelligent brushless motor and a control method thereof, and the method is applied to a control system of the intelligent brushless motor. The system comprises a data acquisition module, a model construction module, a training tuning module, a model verification module, a parameter setting module, an operation application module, a wireless communication module, a memory, an alarm, a processing center and an intelligent mobile terminal. The data acquisition module, the model construction module, the training optimization module, the model verification module, the parameter setting module, the operation application module, the wireless communication module, the memory and the alarm are respectively connected with the processing center; the invention also provides an intelligent brushless motor. According to the method, the AI model is constructed and trained by collecting the daily motor operation quality data, and the operation control parameters are matched according to the model, so that various operation quality abnormalities are prevented, and accurate control of the motor is ensured.

The invention relates to the technical field of communication, in particular to a micro-segmentation-based multi-tenant zero-trust security system, which comprises a tenant network topology sensing module for acquiring network structures and service dependencies of tenants and generating a tenant-level network topology graph and a communication graph; a strategy rule automatic generation module generates a micro-segment access control strategy; the context-aware risk assessment module collects a user identity, an equipment state and a behavior track, and generates an access risk score; the strategy dynamic adjustment and application module updates the access control strategy; and the tenant isolation and zero-trust interaction module deploys a security sandbox to complete trust verification and isolation protection of cross-tenant requests. According to the method, the micro-segmentation strategy is generated by automatically identifying the service dependency boundary, the access control rule is dynamically adjusted based on the context, and refined isolation and risk-driven defense in a multi-tenant environment are realized in combination with the security sandbox and the zero-trust mechanism, so that the security and controllability of the system are remarkably improved.

The invention discloses a hydroelectric oil filter fault diagnosis system and method based on blind source separation, and relates to the technical field of fault diagnosis, and the system comprises a data acquisition module, a self-adaptive preprocessing module, a diagnosis engine module, a digital twin model library and an application module. The data acquisition module synchronously acquires multi-source heterogeneous observation signals; the self-adaptive preprocessing module carries out preprocessing by adopting self-adaptive variational mode decomposition based on an intelligent optimization algorithm; the diagnosis engine module comprises a multi-physical-quantity deep fusion unit, a dynamic source number estimation unit and an online blind source separation unit, the multi-physical-quantity deep fusion unit performs deep fusion on heterogeneous data through a physical information self-encoder to generate a high-dimensional feature matrix, and the dynamic source number estimation unit adopts a three-layer layered structure to perform online estimation on the number of source signals; an independent component analysis algorithm driven by the running state of the on-line blind source separation unit; and a complete diagnosis process is realized. The problem that a traditional method is low in diagnosis precision under strong noise, multi-source coupling and dynamic working conditions is solved.

The invention discloses a high-dynamic mobile ad hoc network dynamic time slot allocation method and device, equipment and a storage medium, and belongs to the technical field of wireless communication and networks. The device is deployed between an upper-layer application module and a bottom-layer communication module of a communication node, and comprises a topology sensing module, a time frame construction module, an idle time slot distribution module, a GNSS module and an information interaction module. According to the method, neighbor nodes are determined among nodes through interaction information, and node topology change indexes and link states are calculated; the coordination node performs idle time slot allocation according to a request sent by the node in the control time slot, and dynamically adjusts a sensing period according to the current network topology change; and the node sends data in the information time slot according to the time slot allocation table, and carries out information interaction and topology perception according to the perception period. According to the method, the packet loss rate in the link unstable environment is effectively reduced, the data transmission reliability is higher, the method can adapt to link and topology dynamic changes, and the network throughput and the resource utilization rate are improved.

The invention discloses an ultrasonic vibrator group intelligent prediction maintenance system and method, and relates to the technical field of industrial equipment fault diagnosis and predictive maintenance, the ultrasonic vibrator group intelligent prediction maintenance system is composed of a data acquisition module, a data transmission module, a data processing module, a model analysis module, an application module, a feedback optimization module and the like, and vibration, current, voltage, resistance, temperature, power and other data are acquired in real time through a multi-source sensor; the sampling frequency is dynamically adjusted under the abnormal working condition; multi-dimensional feature fusion and fault prediction are carried out by utilizing a pre-trained Transform model, mechanical, electrical, thermal and other fault types are automatically identified, maintenance suggestions are output, a user can obtain fault early warning, residual service life and maintenance guidance through a visual interface, and an actual maintenance result is transmitted back to a system to continuously optimize the model, and compared with a traditional mode, the method has the advantages that the operation is simple, and the efficiency is high. According to the invention, accidental shutdown can be effectively reduced, the maintenance cost is reduced, the service life of equipment is prolonged, the real-time monitoring and accurate prediction of the operation state of the equipment are realized, and the production efficiency is improved.

The invention relates to the technical field of charging stations, and discloses an intelligent charging station monitoring method based on digital twinning, which comprises the steps of 1, data acquisition and preprocessing, 2, digital twinning model construction and calibration, 3, real-time monitoring and intelligent decision making, and 4, full life cycle management. The invention also provides an intelligent charging station monitoring system based on digital twinning, which comprises a physical module, a virtual module, a data module, an application module, a connection module and a visualization module. The system has the beneficial effects that multi-source data are collected in real time through the Internet of Things, a digital twin model is constructed, a perfect 3D visual interface is provided, and information such as the equipment running state, the charging process and the energy flow of the charging station can be accurately displayed in real time. And meanwhile, remote monitoring and intelligent control of charging station equipment, such as functions of remotely adjusting the power of the charging pile, switching equipment and the like, are realized, and convenient and efficient operation experience is provided for a user.

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

The invention belongs to the technical field of pollution reduction and carbon reduction management, and particularly relates to a pollution reduction and carbon reduction platform which comprises a data acquisition module, a data analysis and prediction module, a data transmission optimization module, a business display module, three major application modules, a heterogeneous data fusion and security sharing module, a management decision support module, a public service module and a transformation and promotion module. According to the method, a mixed mode of edge calculation, double-queue synchronization and opportunistic synchronization is introduced, the idea of a time delay tolerant network is innovatively combined, accurate synchronization and transmission solutions for different real-time requirements are provided, and data guarantee is provided for efficient response and accurate decision of a park management system.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The present invention relates to the technical field of railway-locomotive diesel-engine fault diagnosis systems, and particularly relates to a railway-locomotive diesel-engine fault diagnosis system based on a knowledge graph. The system comprises a data collection module, a transmission module, a preprocessing module, an algorithm application module, a storage module and a front-end display module, wherein one end of the data collection module is used for performing signal collection by means of a sensor, and the other end thereof is connected to the preprocessing module by means of the transmission module; the preprocessing module performs diagnosis and positioning on a specific faulty part in combination with a fault classification algorithm of the algorithm application module; and an original signal collected by the data collection module and algorithm diagnosis information are scheduled and stored in the storage module by means of back-end management software, and display interaction materials are then provided for the front-end display module. The present invention retains the previous operation mode of an expert knowledge base in terms of fault diagnosis so as to ensure the basic accuracy of cause analysis, and a neural network intelligent algorithm is introduced to reduce the manual workload and individual difference of people in terms of professionalism.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

The invention relates to the technical field of safety production management, and discloses a digital twinborn visual monitoring and early warning system based on production management, which comprises a data acquisition module, a data processing module, a digital twinborn visual module, a safety monitoring and early warning module, an emergency management module and an application module, the data acquisition module acquires total factor data in the enterprise production process, and the digital twinborn visual monitoring and early warning system based on production management constructs an intelligent monitoring and early warning system covering production total factors through deep fusion of digital twinborn, big data, artificial intelligence and three-dimensional GIS technologies. According to the method, efficient integration and intelligent analysis of production data, full-process three-dimensional visual management and control, dynamic risk grading early warning and emergency disposal cooperative linkage are achieved, the intelligence and precision level of enterprise safety production is remarkably improved, the method can be widely applied to high-risk industries, the accident rate is effectively reduced, and remarkable economic benefits and social benefits are achieved.

The invention relates to the technical field of engineering, in particular to a geomembrane defect detection system and method under the earth and stone medium coverage condition, and the geomembrane defect detection system comprises a multi-mode adaptive sensing module, an edge calculation module, a data fusion module, a digital twinning module and a decision and application module; compared with the defects that single resistivity or geological radar detection is adopted in the prior art, interference of medium conductivity and metal impurities is likely to happen, and the deep resolution is insufficient, the scheme adopts multi-mode sensor network cooperative work, and a dynamic density layout and time-phased acquisition strategy is combined; through multi-mode complementation of an electrode array, an ultra-wideband radar, a distributed optical fiber and an MEMS vibration sensor, a dominant sensing mode is automatically switched in a conductive clay area, temperature gradient interference is avoided through night acquisition, the penetration depth is increased to 15 meters through multi-band radar fusion, and metal interference is inhibited; the detection precision and the anti-interference capability in a complex medium environment are remarkably improved, and the problems of high false alarm rate and high omission ratio of a traditional method are effectively solved.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

The invention discloses a multi-modal data fusion and intelligent analysis platform and method for an unmanned aerial vehicle inspection system for a wind power plant. The platform comprises a multi-modal data acquisition module, a feature extraction and standardization module, a multi-modal information fusion module, a joint learning and optimization module, a domain knowledge injection module and an intelligent decision and application module. The system processes multi-source heterogeneous data through an integrated learning and deep learning fusion strategy, projects features to a shared semantic space by using joint training and comparative learning to enhance the anomaly discrimination ability, and performs verification and semantic enhancement on a supervised retrieval result in combination with a knowledge base in the wind power field. And finally, outputting a high-reliability diagnosis report and a maintenance suggestion. According to the invention, accurate identification and positioning of the fan fault are realized, and the inspection efficiency and the system decision reliability are significantly improved.

The invention relates to a user power consumption behavior portraying method and system based on non-intrusive load monitoring, and belongs to the technical field of residential user power consumption behavior analysis and energy management. According to the technical scheme, a data acquisition module collects user intelligent electric meter data; the user load clustering analysis module adopts a clustering algorithm to classify user load curves; the equipment-level behavior analysis module is used for analyzing the energy consumption ratio, the single-equipment use period preference and the multi-equipment cooperation mode of the equipment type for the user; the user portrait construction module constructs a multi-dimensional user power consumption behavior portrait; and the system application module formulates a personalized energy efficiency optimization scheme. According to the method, user load curve clustering analysis and device-level power consumption data are fused, the types and start-stop time of power consumption devices are identified through the NILM technology, and the total load clustering result is combined, so that fine-grained modeling of user power consumption behaviors is realized, and multi-dimensional mining of the device type energy consumption ratio, the single-device use period preference and the multi-device cooperation mode is carried out.

The invention provides a tidal flat evolution prediction method and system based on a hybrid model, and relates to the field of dynamic geomorphology and artificial intelligence cross technology, and the method comprises the steps: constructing a tidal flat erosion and deposition evolution database under different scenes of a target coastal zone; constructing a landform prediction model through an artificial intelligence algorithm based on the database, and taking hydrodynamic characteristic data as input and seabed erosion and deposition variation as output; hybrid simulation is executed based on the physical mechanism model and the landform prediction model, and long-duration evolution prediction is realized by updating terrain boundary conditions through loop iteration; and the configuration service application module is used for receiving user input parameters through the interactive interface and outputting a visual result. According to the method, traditional hydrodynamic force-sediment-landform coupling calculation is decoupled into a physical mechanism and AI coordinated chain process, the calculation complexity is remarkably reduced, offline rapid calculation business of tidal flat evolution is achieved, and real-time technical support is provided for coastal zone planning and disaster prevention decision making.

The invention provides a building engineering data information intelligent management platform which comprises an intelligent sensing module, an edge calculation module, a data management module, an AI decision module, a business application module and a safety standard module. Through the intelligent sensing module, the edge calculation module, the data management module, the AI decision module, the service application module and the safety standard module, the position of a constructor and the equipment state can be tracked in real time, personnel are prevented from entering a dangerous area by mistake and the mechanical overload risk is prevented, operation safety is guaranteed, AI quality inspection and fault diagnosis are locally processed, cloud dependence is reduced, and the working efficiency is improved. According to the method, the system and the device, real-time early warning of potential safety hazards can still be ensured when the network is interrupted, BIM, IoT and service data can be integrated in a standardized manner, information islands are eliminated, and cross-stage quality tracing and responsibility definition are realized.

The invention discloses a fault analysis method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first burying point information corresponding to an abnormal event if the vehicle detects that the abnormal event of a burying point is triggered, the first burying point information comprises at least one of a subsystem type corresponding to the abnormal event, an application module corresponding to the abnormal event, an abnormal type corresponding to the abnormal event, an information domain corresponding to the abnormal event, a fault identifier corresponding to the abnormal event, an abnormal name corresponding to the abnormal event and an abnormal position corresponding to the abnormal event; and sending the first burying point information to a server, so that the server determines the fault information corresponding to the abnormal event according to the first burying point information, thereby improving the vehicle fault problem determination rate.

The invention discloses a cross-modal travel knowledge graph construction system based on a large language model. The cross-modal travel knowledge graph construction system comprises a data source module, a data access module, a change identification module, an incremental updating module, a verification feedback module, a core storage and application module and a technical base module. According to the method, optimization is carried out around dynamic updating accuracy, real-time efficiency and scene adaptability, and four dimensions of hidden conflict resolution, real-time processing efficiency, small and medium main body access and multi-modal deep fusion are improved.

The invention discloses an intelligent operation platform based on a power plant. The intelligent operation platform comprises a data acquisition module, a data processing module, an intelligent analysis module and an application module, and the data acquisition module is used for acquiring operation data and environment data of the power plant equipment through a sensor network and uploading the data to the data processing module to provide a data basis. And the data processing module is used for fusing and preprocessing the original data, wherein the preprocessing comprises data cleaning, feature extraction and data standardization. The intelligent analysis module is used for constructing an equipment operation state prediction model and a fault early warning model by utilizing a machine learning algorithm; the fault early warning model identifies potential fault risks through anomaly detection; and the equipment operation state prediction model analyzes the relevance between the energy consumption and the equipment operation parameters, and provides an energy-saving strategy. The invention relates to the technical field of power plant operation management, and specifically provides an intelligent operation platform based on a power plant.

The invention relates to the technical field of student management, in particular to a student management system based on big data. The system comprises a data acquisition module, a data storage and preprocessing module, a data analysis and mining module, a student portrait construction module, a management application module and the like. The data acquisition module acquires structured and unstructured data of students from multiple sources such as school educational administration and attendance; the storage and preprocessing module adopts a distributed storage architecture to guarantee the stability and reliability of the data, and performs preprocessing operations such as cleaning and standardization on the data; the data analysis and mining module deeply mines data values by using clustering analysis, association rule mining and time sequence analysis, and provides a basis for student portrait construction, and portraits cover dimensions such as basic information, learning and behavioral performance. According to the system, through comprehensive data integration, accurate analysis and multi-role collaborative application, the education management quality and efficiency are effectively improved, comprehensive development of students is assisted, and the system is suitable for student management scenes of various schools.

The invention discloses a real-time data receiving and transmitting system and method based on a kernel bypass, and the method comprises the steps: processing the received data when the data receiving and transmitting system receives the data, and achieving the high-level data with complete semantics; updating a write pointer through atomic operation, and writing high-level data into a lockless annular queue in the shared memory module; the upper-layer application module reads the lock-free annular queue in the shared memory; when the data receiving and transmitting system transmits data, upper-layer data to be transmitted is transmitted to the shared memory module, and a write pointer is updated; the upper layer data in the shared memory is read in a polling or interruption mode, a read pointer is updated, the upper layer semantic data is segmented according to the format, a UDP data packet is obtained, the UDP data packet is written into a network card in the polling or interruption mode, and data sending is achieved. Through the data receiving and transmitting system disclosed by the invention, microsecond-level ultra-low delay of data certainty is realized, and efficient decoupling and stable throughput are realized.

The invention relates to the technical field of chemical production monitoring, and discloses a cloud computing-based chemical production monitoring system, which is characterized in that an on-site data acquisition module is used for acquiring chemical production data in real time and carrying out filtering and denoising processing to obtain preprocessed data; the network transmission module is used for encrypting the transmitted data and reducing the data transmission quantity by adopting a data compression technology; the cloud computing platform module is used for dispersedly storing the data on a plurality of storage nodes by utilizing a distributed storage technology, classifying the data, analyzing the data in real time by adopting a streaming computing technology, detecting the abnormal condition of the data, and immediately triggering an alarm mechanism when the abnormal condition exceeds a preset safety threshold value; the monitoring application module is used for displaying various parameters and equipment operation states, and providing decision suggestions for production optimization in combination with a chemical production process and equipment; the production efficiency can be improved, the production cost can be reduced, the product quality can be improved, and the market competitiveness of enterprises can be enhanced.

The invention discloses a data right confirmation method based on data encapsulation abstract uplink. The method comprises the following steps: firstly, constructing a data encapsulation right confirmation system consisting of a server side, a client side and a block chain network; the client is provided with a data owner module and a data user module; the server side is provided with a data segmentation module, a data encapsulation module, a hash abstract module, an encapsulation body publishing module, an encapsulation body uplink module, an encapsulation body application module and an encapsulation body right confirmation module; the data segmentation module carries out data segmentation, the data encapsulation module encapsulates data segmentation bodies, and the hash abstract module generates an encapsulation body hash abstract; the package publishing module sends package basic information and the hash abstract to the data user module; the data user module displays basic information and a hash abstract of the packaging body; the packaging body chaining module is used for chaining the Hash abstract; the packaging body application module transfers the ownership of the packaging body, and the packaging body ownership confirmation module confirms the ownership of the packaging body. By adopting the method, the problems of single-point failure, data leakage, hostile attack, insufficient flexibility and the like can be solved.

The invention discloses a cloud-side collaborative household arrhythmia early warning AI agent, and relates to the technical field of AI agents. The system specifically comprises a data acquisition module, an edge computing module, a cloud computing module, an application module and a safety guarantee module, the data acquisition module acquires electrocardio and related physiological signals in real time through wearable equipment, and the edge computing module performs preprocessing and preliminary anomaly detection on data, quickly responds and screens key data and uploads the key data to a cloud; the cloud computing module carries out deep analysis and personalized model training; the application module converts a cloud result into real-time early warning and health suggestions and pushes the real-time early warning and health suggestions to the user and medical personnel; the security guarantee module runs through the whole process, and ensures the privacy and security of data in acquisition, transmission, storage and application. According to the invention, data processing is scientific and efficient, data can be comprehensively collected and preprocessed, meanwhile, real-time preliminary screening and secure transmission are realized, and the transmission pressure and privacy risk are reduced.

An application module for a control device, wherein the application module is configured to communicate and interact with further components of the control device as part of the control of a machine or system, where the application module is configured to generate an additional functionality for the control device through the use of a user application, where an automation engineering system is provided for establishing the control device and an application engineering system is provided for creating the user application, and where the application module is configured such that testing of the user application occurs or can be performed via an interaction of the application module with the application engineering system.

The invention belongs to the field of Web application technology, XML document processing technology and online editor technology, and discloses an XML semantic editing system based on an aviation knowledge graph. The system comprises a front-end application module, an XML processing engine module, a DTD rule verification module, a visual editing module, a plug-in extension module, a collaborative editing module and an aviation knowledge graph integration module. According to the method, professional and efficient editing of XML documents in the aviation field is achieved through the full-process design of front-end interaction, XML processing, DTD verification, visual editing, semantic assistance and collaborative management, and the Web technology, the XML processing technology, the DTD verification technology, the knowledge graph technology and the collaborative editing technology are integrated, so that the XML documents in the aviation field are edited in a professional and efficient mode. The problems that an existing XML editing tool is difficult to deploy, poor in compliance, low in efficiency and weak in cooperation in the aviation maintenance field are solved, and a specialized and efficient XML semantic editing scheme is provided for the aviation maintenance industry.

The invention provides an electromagnetic transformer state tracking and foresight verification method and system based on digital twinning. The system comprises an on-site lightweight acquisition module, a digital twin construction and calibration module, an intelligent analysis and prediction module and a visual application module, wherein the on-site lightweight acquisition module is responsible for acquiring on-site lightweight state data of a mutual inductor; the digital twin construction and calibration module constructs and dynamically updates a high-fidelity virtual model consistent with a physical entity based on a multi-physical field model and a parameter identification algorithm; the intelligent analysis and prediction module performs all-condition simulation and trend prediction by using the calibrated virtual model; and the visual application module is used for generating a diagnosis report and early warning information and providing decision support for the user. The invention aims to solve the problems of low efficiency and lack of deep diagnosis and prospective prediction capabilities of the existing electromagnetic transformer verification method, and realizes accurate mapping of the internal state and full-working-condition performance simulation and prediction of the electromagnetic transformer verification method.