6839 results about "Data transport" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

An Internet-of-Things-based intelligent safety monitoring method and system for power plant operation. The method comprises: deploying sensors and collecting monitoring data in real time; transmitting the collected data to a central server, performing data processing, integrating the monitoring data, calculating a comprehensive environmental index, and preliminarily assessing an environmental monitoring condition; and comprehensively determining a device fault condition, remotely monitoring device status, starting a device emergency response mechanism, and performing intelligent safety monitoring of power plant operation. Power plant devices and environment parameters are monitored in real time, so that device anomalies and potential safety hazards are promptly captured to achieve rapid response. The stability of the power plant is improved, and a video monitoring system is integrated in combination with image recognition technology to monitor anomalous events in the power plant area. The level of safety management is enhanced, and decisions can be quickly made in emergency situations, improving flexibility and efficiency and enhancing management effectiveness and the accuracy and stability of the monitoring system.

A system for multi-stage planning of construction processes and resource allocation, consisting of: a central planning engine configured to receive input data, including architectural design models, structural constraints, procurement schedules, and historical performance indicators; a task decomposition processor that is operationally connected to the central planning engine and configured to generate a hierarchical construction task graph by decomposing macro-level construction milestones into mid-level and micro-level subtasks, with each subtask having time estimates, location identifiers, resource requirements, and mutual dependencies; a hybrid planning processing unit configured to resolve time and resource constraints across the entire task diagram; a resource coordination controller that is operationally connected to the central planning engine, wherein the resource coordination controller includes a real-time database of work units, machines and material stocks, each resource being tagged with attributes such as availability, usage history, operating status and spatial location; a multitude of distributed execution units distributed across the construction zones, each distributed execution unit comprising an embedded controller, sensor interfaces, task status processing logic, and communication circuitry, each distributed execution unit being configured to receive planning instructions from the central planning machine, execute localized control logic for task confirmation and resource activation, and transmit task execution data back to the central planning machine; an adaptive conflict resolution processing unit that is operationally connected to the central planning engine and configured to detect conflicts in task execution or resource conflicts, simulate alternative task-resource allocation scenarios using a real-time multi-agent model, and autonomously update the task graph with revised task sequences and resource allocations; and A dashboard for the construction process, configured to visualize task progress, deviations from the planned schedule, and resource efficiency metrics, with the dashboard also being able to receive manual override inputs or approve automated conflict resolution proposals generated by the adaptive conflict resolution module.

The invention discloses an automatic code generation and optimization system based on multiple modes, which relates to the technical field of automatic programming, and comprises a task analysis module for analyzing task description and constraint conditions in combination with a CLIPS rule engine and a knowledge graph and original data, identifying task targets and requirements, and outputting a task risk assessment report and a task intention set; the optimization decision module is used for selecting an optimal modal data subset by using a particle swarm optimization algorithm and a path planning algorithm, performing optimization adjustment according to task requirements, and outputting a code generation strategy; and the test evaluation module is used for evaluating and improving the unit test and the integration test by utilizing the variation test, carrying out quality and performance evaluation on the code through continuous integration and continuous delivery, and outputting a code evaluation report and an optimization suggestion. According to the method, the optimal modal data subset is dynamically screened, and the data transmission path is optimized, so that the code performance is ensured, the computing resource consumption is reduced, and the global optimization of the code generation strategy is realized.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

An improved system and method of selectively transmitting asset data from one or more sensors associated with the vehicle to a backend server, which is configured to analyze the asset data and, if necessary for further analysis of the asset data (e.g., to determine whether a safety event has occurred) and / or to provide actionable data for review by a safety analyst, requests further asset data from a vehicle device.

The invention provides an intelligent workshop scheduling optimization system, method and device based on artificial intelligence and a storage medium. The system comprises a data acquisition module, a data fusion and preprocessing module, an intelligent scheduling model training module, a real-time scheduling decision module and an execution monitoring module. The data acquisition module collects production equipment operation parameters, material circulation information, production order details and other data, and transmits the data to the data fusion and preprocessing module for cleaning, feature extraction and labeling. And the intelligent scheduling model training module constructs an optimization model by adopting a deep reinforcement learning algorithm based on the annotation data. And the real-time scheduling decision module receives real-time data, inputs a model to generate a scheduling instruction, and performs risk assessment and adjustment. And the execution monitoring module controls workshop production according to the instruction, collects feedback data to form closed-loop control, and realizes production dynamic optimization. The method can improve the production efficiency, reduce the cost, and enhance the robustness and adaptability of the scheduling scheme.

The invention provides a method and system for monitoring the running state of a photovoltaic power station in real time, and relates to the technical field of photovoltaic power station monitoring, and the method comprises the steps: integrating a multi-mode sensor array in a photovoltaic module junction box, and collecting the sensor data of the photovoltaic power station in real time; performing localization preprocessing on the sensor data through an edge computing node, and transmitting different priority data to a cloud based on a dynamic hybrid communication protocol; sensor data are fused at the cloud, a four-dimensional digital twinborn model is constructed, and time-space continuous meteorological prediction, component aging and dust retention evolution data are fused; recognizing the fault mode of the photovoltaic power station, and dynamically optimizing the string topological structure of the photovoltaic power station in combination with the real-time operation state of the photovoltaic power station, the equipment health data and the fault mode recognition result; by integrating the distributed sensor, the edge calculation module and the intelligent alarm mechanism, the problems of delay, insufficient intelligent analysis and response lag in the prior art can be effectively solved.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

The invention belongs to the field of parallel computing architecture optimization, and relates to a matrix multiplication acceleration method and system based on dynamic computing resource mapping, and the method comprises the steps: constructing a dynamic topology model driven by tensor dimension features, and generating a thread block distribution mode according to matrix parameters and GPU hardware information; constructing a multi-dimensional resource scheduling strategy library, dynamically selecting an optimal thread block distribution strategy from the multi-dimensional resource scheduling strategy library, and generating a binding relationship between the thread blocks and the data blocks; calculating collaborative access logic of thread blocks and storage hierarchies based on block parameters and dynamic mapping function optimization; distributed calculation is carried out, calculation and data transmission are parallelized through pipelining and a double-buffering mechanism, and result aggregation across calculation units is completed synchronously through atomic operation and a barrier. According to the method, discontinuous memory access conflicts can be effectively reduced, the execution efficiency of the calculation instruction and the utilization rate of the cache space are improved, the parallel calculation process of accelerating and optimizing the general matrix multiplication is realized, and the data processing efficiency is improved.

The invention discloses a cloud edge cooperation method for AI intelligent Internet of Things equipment data processing, and relates to the technical field of data processing, and the method comprises the steps: S1, intelligent data collection, S2, edge side AI preprocessing, S3, edge-cloud end cooperation reasoning, S4, intelligent data transmission, S5, cloud end AI big data analysis, S6, real-time feedback and self-optimization, S7, adaptive resource scheduling, and S8, full-link visualization. Through AI-driven dynamic sampling and multi-modal data fusion, the efficiency and precision of data acquisition are optimized, redundancy or omission caused by fixed sampling is avoided, meanwhile, the transmission load is reduced, layered task dynamic unloading and intelligent transmission protocol optimization are achieved, the flexibility and stability of cloud edge collaboration are improved, and the cloud edge collaboration efficiency is improved. Manual intervention is reduced through a real-time feedback and self-optimization mechanism, the autonomy of the system is enhanced, and the interpretability and fault diagnosis capability of the system are remarkably improved through a visual panel and a causal reasoning model.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention relates to the technical field of environment intelligent management, and discloses a multi-community-oriented AI intelligent household garbage control processing system which comprises a multi-mode sensing module, a data processing and edge calculation module, a garbage quantity prediction and scheduling optimization module, an intelligent decision and multi-source data fusion module and a self-adaptive adjustment and feedback mechanism module. The multi-community-oriented AI intelligent household garbage control processing method comprises the following steps that S1, a multi-modal sensing module collects garbage data; s2, an edge calculation module performs preprocessing, classification and encrypted transmission; s3, optimizing transportation and scheduling; s4, generating an optimal processing decision; and S5, adaptively adjusting the strategy. Through the technical scheme of combining multi-modal perception and edge calculation, accurate garbage data acquisition and efficient processing in a multi-community environment are realized, the technical effects of reducing data transmission burden and improving garbage classification data quality are achieved, and garbage classification is more accurate and reliable.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention provides a dynamic security risk assessment and intelligent response system and method based on AI, relates to the field of dynamic security risk assessment and intelligent response, and improves the accuracy and response speed of security protection. The method comprises the following steps: firstly, collecting multi-dimensional security data, carrying out data preprocessing by utilizing an AI technology, and outputting a structured security data stream; then analyzing the data flow based on an AI model, identifying cross-dimension attack features, generating real-time threat intelligence, updating a risk judgment threshold value, generating a security assessment report, automatically generating and executing hierarchical response measures according to the assessment report, feeding back a response execution effect, performing dynamic adjustment, and generating a new security assessment report; and finally, automatically selecting a data transmission mode and rate according to the new security assessment report, and adjusting the acquisition frequency of the sensor. According to the method, through dynamic optimization and an intelligent response mechanism, the recognition and defense capability on complex attacks is remarkably improved.

The invention discloses a carbon emission monitoring method and system for a data center, and the method comprises the steps: collecting the power generation proportion data of different energy types in real time, predicting the power supply change trend of renewable energy in combination with meteorological information, calculating the power supply proportion of each energy type, and constructing a dynamic weight matrix; constructing a time sequence prediction model by using historical and real-time renewable energy data, and predicting the future renewable energy power supply capability; carbon emission factors and priorities of different data centers are calculated, a task migration feasibility matrix is constructed, calculation tasks are dynamically migrated to a low-carbon emission data center according to a scheduling revenue function, a task scheduling path is optimized, cross-regional data transmission time delay and energy consumption are reduced, and cross-regional optimization scheduling of computing power is achieved; a multi-dimensional time sequence prediction model is constructed, the carbon emission trend is predicted, and carbon emission abnormity is monitored in real time. According to the invention, the problem of matching of computing power load and renewable energy of a data center can be solved.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention discloses a human resource archive filing management system, and relates to the technical field of data management, the system comprises an acquisition module, a processing module and a management module, the acquisition module acquires original archive data of employees, transmits the data to the processing module, classifies the original archive data according to a preset first rule, and stores the classified data to the management module; the first archive data is obtained, controlled and managed, a calling permission and a distribution strategy are generated, the first archive data is recognized through a preset second rule, second archive data is generated, corresponding processing is conducted according to the processing priority, a processing log is reserved, third archive data are obtained and transmitted to the management module, and the management module is used for managing the first archive data. A distributed cloud storage cluster and a local storage node are constructed, an overall storage architecture and a retrieval path of third archive data are optimized, archive management is refined through three-dimensional classification, data integrity is guaranteed through field-level verification and automatic completion, scene-based dynamic authority control safety is achieved, compliance risks are intelligently cleared and avoided, and efficiency is improved through mixed storage. And active management of archive management is promoted.

The invention discloses an intelligent storage equipment health state assessment and predictive maintenance method and system, and the method comprises the steps: arranging a multi-mode sensor network on equipment, collecting vibration, stress, temperature, current, voltage and image data in real time, carrying out the collection and preprocessing of edge data, and transmitting the data to an analysis layer; and the analysis layer performs online self-learning calibration by using a digital twin model, fuses the data in combination with a fuzzy neural network for dynamic weight adjustment, and calculates a health index and a residual life prediction value of the equipment. The system can give out an early warning before a fault symptom occurs, and automatically generates a maintenance decision and a task scheduling scheme, thereby achieving the real-time monitoring and predictive maintenance of the equipment state, and remarkably improving the operation efficiency of a warehousing system and the reliability of the equipment.

The invention discloses a data fragment storage index optimization method and system, and relates to the technical field of data fragment storage, and the method comprises the steps: determining an observation time period according to a data access mode and short-term fluctuation characteristics; monitoring data performance, and performing log data analysis; and performing data resource adjustment, and adjusting task allocation. According to the method, data loading is efficiently managed and adjusted, the user experience and reliability of a system are improved, the importance of a dynamic data management method based on the observation time period is highlighted, hot spot fragments can be recognized in time when the load is too high through setting of a dynamic threshold value, and therefore effective measures are taken, and the workload of a user is reduced. According to the method and the device, the waste of system resources is reduced, the use efficiency of the resources is improved, data operation can be completed under the condition that a main thread is not interfered, the data transmission efficiency and the storage utilization rate are improved, and the overall processing capacity and fluency of the system are improved.

The invention discloses a solid state disk management system and a data processing method, and relates to the technical field of solid state disk management. Flexible task scheduling and resource management are provided through a lightweight operating system module, and a computing task program defined by a user is supported to be dynamically loaded; edge computing or machine learning operators are efficiently executed in combination with a programmable hardware processing unit and a DMA channel of the computing acceleration engine module, the data preloading module is utilized to predict and preload data to DDR based on LBA access history, access delay is reduced, an NVMe protocol is expanded by means of the task unloading interface module, host task issuing and result returning are achieved, and the data processing efficiency is improved. And hardware-level memory protection is ensured through the security isolation module, so that the data calculation processing capacity of the solid state disk is remarkably improved, localized calculation tasks such as edge calculation and machine learning are supported, mass data transmission is effectively reduced, and bus and network loads are relieved.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The invention discloses a fire-fighting early warning system and method based on artificial intelligence, and relates to the technical field of intelligent fire-fighting early warning, and the system comprises an environment sensing module which comprises a multispectral sensor, a temperature sensor, a smoke sensor and a high-definition camera, and is used for collecting fire characteristic spectral data and transmitting the data to a data analysis module; the data analysis module comprises an edge calculation unit and a data fusion unit, and is used for performing feature extraction and anomaly detection on the fire feature spectrum data through a deep learning model to generate a fire risk assessment result; the early warning decision module comprises a risk assessment unit, an early warning grading unit and a communication unit, and is used for determining an early warning grade according to a fire risk assessment result and triggering equipment of the execution feedback module; and the execution feedback module comprises a spraying system, an emergency lighting system and an alarm and is used for executing fire emergency measures according to the instruction of the early warning decision module. According to the invention, comprehensive improvement of fire prevention and control efficiency is realized through cooperative work of multiple modules.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent charging and endurance optimization method for an unmanned aerial vehicle nest, and the method comprises the steps: collecting flight, battery and environment data through an unmanned aerial vehicle, transmitting the data to a nest, and precisely evaluating the remaining endurance of the unmanned aerial vehicle through the nest in combination with historical tasks and geographic information; performing dynamic task planning based on reinforcement learning, and generating an optimal charging strategy by using a dynamic planning algorithm; by means of a multi-mode sensor and a self-adaptive charging control model, intelligent regulation and control of the charging process are achieved; and through solar auxiliary power supply and multi-machine cooperative charging, the endurance is enhanced. The problems of low charging efficiency, poor endurance management, insufficient system reliability and the like in a traditional method are effectively solved, the utilization rate of nest charging resources is remarkably improved, the service life of a battery is prolonged, the endurance of the unmanned aerial vehicle and the system stability are enhanced, and the method has important significance in promoting efficient application of the unmanned aerial vehicle in multiple fields.

The invention discloses an online fault detection method and system for a charging pile, and relates to the technical field of intelligent monitoring of the charging pile, and the system monitors the filtering performance data of the charging pile in real time through an installed monitoring device, and transmits the data to a central control system for preprocessing through the Internet. The method comprises the following steps: calculating a ripple dynamic evolution index ahf, carrying out filter health assessment, calculating a higher harmonic comprehensive energy index xnl and a high-frequency energy distribution response index xpb when the filter health assessment shows that an aging sign exists, then comprehensively calculating a comprehensive health evolution index FHI to carry out comprehensive filter performance assessment, and when the filter has a failure risk, carrying out comprehensive filter performance assessment. And stopping the operation of the charging pile and notifying related personnel to carry out maintenance. And finally, storing the data through the cloud platform, and generating a broken line graph and a pie chart report. The system can efficiently and accurately monitor and evaluate the health state of the filter of the charging pile, and ensures stable operation and timely maintenance of the charging pile.

The invention provides an intelligent building construction management system and method based on BIM scene construction, and relates to the technical field of building construction management. In the construction process, the management system collects mass data in real time by means of sensing layers spread all over key nodes of a construction site; the method comprises the following steps of: transmitting operation parameters of construction equipment, operation dynamics of constructors, consumption rate of materials and change of a field environment to a data processing center, accurately positioning a low-efficiency waiting problem existing in a material transfer link through deep analysis of a big data processing engine and an artificial intelligence algorithm library, recommending an optimization scheme, and improving the efficiency of material transfer. According to the method, the hoisting sequence is adjusted, unnecessary working hour loss is reduced, by means of a BIM model platform, a construction plan is presented in a visual mode, and the management system can rapidly generate and recommend and flexibly deploy construction team personnel, optimize the construction process sequence, ensure construction coherence, reduce construction delay and shorten the construction period.

Provided in the present disclosure are a control system and method for a welding process, and a device, a medium and a program. The control system comprises: a sensor module, which is configured to collect data during a welding process; a data analysis module, which is configured to preprocess the data during the welding process, extract key features from the preprocessed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification on the basis of the comprehensive features by means of a machine learning model to identify and obtain defect features; a knowledge graph module, which is configured to use a knowledge graph to obtain, on the basis of the defect features, a repair policy corresponding to the defect features; a control module, which is configured to transmit to the data analysis module the data in the welding process that is received from the sensor module, receive the repair policy, and generate a repair instruction on the basis of the repair policy; and a repair module, which is configured to automatically adjust welding parameters or control an actuation mechanism to perform a repair operation on the basis of the repair instruction.