950 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 relates to the technical field of intelligent questions and answers, and discloses a commercial real estate digital assistant system based on a large model and an intelligent agent, and the system comprises an original data obtaining module which is used for obtaining the original data of a commercial real estate; the data processing and knowledge base construction module is used for processing the original data to construct different types of knowledge bases; the data and tool application module is used for querying a corresponding knowledge base or calling a tool according to the target question, generating an answer and displaying the answer; the task disassembling and process execution module is used for disassembling the target problem into a plurality of task processes, generating an execution link, and combining the task processes and the execution link to execute the task; a domain large model training module; and a field large model management module. According to related application scenes and industry knowledge in the commercial real estate field, the large model in the training field is finely adjusted, the large model and the agent technology are combined, the digital assistant system oriented to the commercial real estate field is constructed, and complex tasks and requirements in the commercial real estate field are better coped with.

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 an enterprise-level knowledge management system based on a large language model. The enterprise-level knowledge management system comprises a knowledge storage module, a knowledge analysis module and a knowledge application module, the knowledge storage module is responsible for managing and storing knowledge from different sources, supporting uploading, updating, deleting and sharing of electronic documents and multimedia documents, and is provided with authority control; the knowledge analysis module is used for carrying out deep understanding and structured processing on text and multimedia contents through a special analyzer by utilizing an advanced large language model technology; the knowledge application module provides intelligent retrieval and intelligent question and answer functions and supports advanced applications such as behavior portrait analysis and standard process supervision. According to the method and the system, the acquisition, understanding and application efficiency of enterprise knowledge is remarkably improved by fusing a large language model technology and a traditional knowledge management method, an intelligent, automatic and safe knowledge management solution is provided for an enterprise, and value mining and innovative application of enterprise knowledge assets are effectively promoted.

The invention discloses a project-level visualization display method and system for highway multi-source data fusion, and the method comprises the following steps: 1, constructing a multi-source heterogeneous data exchange and integration frame, and carrying out the seamless connection and mapping of a BIM model, GIS space geographic information and an oblique photography three-dimensional point cloud model; 2, formulating a standardized interface specification, and performing specification processing on data from different sources through a data analysis and conversion engine; 3, dynamically and synchronously updating the multi-source engineering data and the project progress in real time; 4, gathering engineering data to construct a digital twinborn model of the expressway project to realize centralized management, multi-dimensional analysis and dynamic presentation in a visual scene; 5, carding and extracting management business logic to construct a flexibly expanded professional application module so as to realize intelligent analysis of the engineering project; and step 6, performing visual immersive display on the multi-source data fusion project by adopting visual man-machine interaction. According to the invention, visual comprehensive management and traceability of each element of the project are realized.

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 automatic driving platform for a long-distance pipe network heat supply system based on a large model and a knowledge graph, and the platform comprises an infrastructure layer which is used for connecting an actual heat source, a long-distance pipe network and a multi-stage pump station heat supply facility of the long-distance pipe network heat supply system into the platform; the data processing layer comprises a data acquisition unit and a multi-source data fusion unit; the digital twinborn layer is used for establishing a digital twinborn model of the long-distance pipe network heat supply system; the large language model-knowledge graph layer comprises a graph construction unit which is used for constructing each business knowledge graph; the system also comprises a large language model construction unit which is used for constructing and training large language models of all businesses and processing automatic driving related businesses of the long-distance pipe network heat supply system. The business application decision-making layer comprises a business application unit which is used for providing a business application module of the long-distance pipe network heat supply system; and the intelligent decision-making unit is used for carrying out simulation analysis according to the digital twin model and calling each business large model to output a corresponding business decision-making result.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

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 discloses a data access permission dynamic adjustment system, which belongs to the technical field of data management and comprises a data collection module, a feature extraction module, a risk assessment module, a strategy generation module, a decision optimization module, a verification adjustment module, a permission application module, a user feedback module, an anomaly detection module and a monitoring recording module. According to the method, the candidate permission strategies can be quickly generated, the real-time decision-making requirement can be met, meanwhile, more selection spaces are provided for subsequent selection, a user is helped to understand the basis for strategy generation more visually, the permission management strategies can be optimized and adjusted easily, the access path can be adjusted in real time, the flexibility of the system is improved, and the user experience is improved. Global search can be carried out in a complex permission space, local optimum is avoided, and access continuity and stability are guaranteed.

The invention discloses an intelligent modeling tool for a building structure and an MEP system and a working method of the intelligent modeling tool. The tool comprises an information extraction and storage module, an intelligent layout and optimization module, a modeling and simulation module, a model output and application module and an NLP and user interaction module. The invention belongs to the technical field of digitization and informatization management, and particularly provides an intelligent modeling tool for a building structure and an MEP system, which realizes full-process intelligentization from building drawing input to BIM model output through cooperative work of all functional modules and is used for a building structure and an MEP system.

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 discloses a highway tunnel lighting intelligent control method and system based on the Internet of Things, a sensing module is composed of a millimeter wave radar vehicle detector, a tunnel interior brightness sensor, a tunnel exterior brightness sensor and a vehicle speed detector, and a transmission module is a dual-mode communication module composed of a Zigbee wireless network module and a power line carrier communication module. A fuzzy control algorithm library is built in an edge computing gateway of the control module and is used for fusing data of the sensing module in real time and generating a dimming instruction, and each Internet of Things segmentation controller of the control module manages a plurality of lamps and is internally provided with a fault isolation circuit; and an intelligent management platform is arranged in the application module and is used for receiving an instruction of the control module and generating a dynamic dimming strategy. According to the invention, the millimeter-wave radar and video tracking data fusion, Zigbee + PLC dual-mode transmission and edge gateway self-learning algorithm are combined; according to the invention, the defects of the traditional scheme in the aspects of real-time performance, reliability, energy efficiency ratio and operation and maintenance cost can be systematically 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 belongs to the technical field of wind power generation, and discloses a wind power plant digital twin modeling system and a use method, the system comprises an equipment module, the equipment module comprises a biological bionic adaptive fan blade system, a quantum sensing network, a laser radar system, a high-resolution multi-modal imaging system and an intelligent autonomous flying robot group system; the network module comprises a 5G private network, a quantum communication network and an edge computing network; the data module comprises a distributed database system, a big data processing platform and an AI training platform; the AI module comprises a wind regime prediction model, a power prediction model, an equipment health state monitoring model and a self-adaptive control algorithm model; the application module comprises a digital twinborn visualization platform, a holographic mixed reality operation and maintenance system and an intelligent scheduling system. Modeling and intelligent management of the wind power plant can be realized, the power generation efficiency can be improved, the operation and maintenance cost can be reduced, and the service life of equipment can be prolonged.

The invention belongs to the technical field of computers, and particularly discloses a medical dialogue service device and system based on a large language model, and the device comprises a data set construction module, a model training module and a model application module. The data set construction module comprises a dialogue sample reconstruction unit, a knowledge graph question and answer pair generation unit and a dialogue sample rewriting unit; the model training module is used for training a large language model based on the reconstructed dialogue sample, the knowledge graph question and answer pair and the rewritten dialogue sample to obtain a trained large language model; and the model application module is used for inputting the question content carried by the dialogue request to the trained large language model to obtain the answer content. According to the method and the device, high-quality medical dialogue service can be provided for the user in an end-to-end dialogue mode aiming at an actual medical consultation scene.

Techniques for application deployment that include: determining an integrated platform application definition for an integrated platform application; determining an integrated platform application programming interface (API) for the integrated platform application; selecting a set of platform application modules to be integrated into the integrated platform application; generating, based on the application modules selected and the platform API, the integrated platform application, the integrated platform application operable to: obtain, for each of two or more platform applications corresponding to a platform application of the set of platform application modules selected, application results data for the user generated by the platform application; consolidate the application results data obtained to generate a consolidated results report for the user; and present, via a platform user device associated with the user, the consolidated results report for the user; deploying, in response to determining that the integrated platform application test results satisfy threshold testing criteria, the integrated platform application.

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 discloses a laser welding process intelligent real-time monitoring system based on deep learning, and belongs to the field of laser welding monitoring. Comprising an enhanced data analysis and feature extraction module, an anomaly detection and fault early warning module, a dynamic parameter optimization module, a multi-modal data fusion module, an adaptive learning and model updating module, a welding result prediction module, an application module and an integration and cloud computing support module. By means of accurate feature extraction, anomaly detection and dynamic parameter optimization, problems in the welding process can be found and corrected in time, and welding defects are reduced; on one hand, by predicting the welding result in advance, the trial and error cost is reduced; on the other hand, by optimizing welding parameters, welding efficiency is improved, and energy consumption is reduced; and the production efficiency is improved, the change in the welding process can be quickly responded through the real-time monitoring and automatic adjusting functions of the system, and the continuity and stability of the welding process are guaranteed.