7696results about How to "Reduce downtime" patented technology

The invention discloses an equipment fault intelligent question-answering method and system based on knowledge graph enhancement. The method comprises the following steps: constructing a knowledge graph of a professional field; developing a natural language processing module; designing an enhanced generation model; designing an interactive question and answer interface; the system comprises a user interaction layer, a language understanding processing layer, a knowledge graph query reasoning layer, a question and answer generation layer and a data storage management layer. Through the natural language processing technology, the system can quickly understand the query intention of a user, and the manual information input and interpretation time in traditional fault diagnosis is shortened, so that the overall fault response speed is increased; the large language model has deep learning and generalization capabilities, various complex and unknown fault conditions can be processed, accurate diagnosis and operation suggestions are provided, and the potential capability of the system for processing new fault types is enhanced.

The invention discloses an equipment operation evaluation method based on multi-source data fusion, and relates to the technical field of equipment operation evaluation, and the method comprises the following steps: S1, data collection: collecting multi-source data of equipment in a real-time operation state, the multi-source data comprising sensor data, operation logs, environment data and maintenance history of the equipment; according to the equipment operation evaluation method based on multi-source data fusion, accurate equipment state monitoring and fault prediction can be provided, and the performance and accuracy of the equipment can be continuously optimized according to actual equipment operation data and environmental conditions of the equipment, so that efficient and intelligent equipment management and maintenance strategies are realized; according to the method, specific steps and features of the equipment operation evaluation method based on multi-source data fusion are clearly defined, comprehensiveness and high efficiency of the method are ensured, and the method is suitable for various complex industrial environments so as to improve reliability and maintenance efficiency of equipment.

The invention provides a crane balance beam crack defect detection method and system, and relates to the technical field of defect detection.The crane balance beam crack defect detection method comprises the steps that a plurality of ultrasonic guided wave sensor arrays are arranged on the surface of a crane balance beam to be detected, ultrasonic guided wave signals in the balance beam are collected, and ultrasonic guided wave time-frequency domain feature vectors of the balance beam are constructed; constructing a three-dimensional finite element simulation model of the crane balance beam to obtain multiple groups of balance beam crack defect expansion strain field data, and constructing a crack defect deformation ultrasonic-strain field fusion feature vector; training a crack defect deformation detection intelligent discrimination model by using the crack defect deformation ultrasonic-strain field fusion feature vector, obtaining a time-frequency domain feature vector and a strain distribution feature vector of the crane balance beam to be detected, and inputting the time-frequency domain feature vector and the strain distribution feature vector into the trained crack defect deformation detection intelligent discrimination model for intelligent discrimination screening. And outputting a crack defect detection result of the balance beam to be detected, wherein the crack defect detection result comprises the type, size, form and specific position of the crack defect.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention provides a new energy station operation intelligent management method and system, and relates to the technical field of new energy station operation, and the method comprises the steps: collecting various data in real time through a sensor and monitoring equipment in a new energy station; transmitting the collected data to a central data center, carrying out iterative optimization processing, and determining final data classification, cleaning and compression parameter combination so as to construct a new energy station big data platform; and according to the constructed big data platform of the new energy station, constructing a digital twinborn model of the new energy station so as to reflect the operation state, the equipment health condition and the environment change of the station in real time. Through real-time data acquisition, digital twinborn model construction and simulation analysis, automatic generation and closed-loop management of an operation optimization scheme are realized, and the operation efficiency, prediction accuracy and fault handling capacity of the new energy station are effectively improved.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention discloses an industrial environment information wireless monitoring system based on the Internet of Things and a control method thereof, and relates to the technical field of industrial management, and the system comprises an intelligent edge calculation module, a data visualization module, a self-healing network management module, a precise energy consumption management module, an environment anomaly detection module and a wireless network optimization module. According to the industrial environment information wireless monitoring system based on the Internet of Things and the control method thereof, real-time analysis and decision are realized at a data source by using an intelligent edge computing module, data transmission delay is reduced, response can be quickly made in an emergency, potential faults are predicted through a time sequence abnormity self-learning network model, and the monitoring accuracy of the industrial environment information wireless monitoring system based on the Internet of Things is improved. And network self-healing is actively carried out before a problem occurs, so that the network downtime is effectively reduced. In addition, due to the introduction of a low-power-consumption ad hoc network protocol, the energy consumption optimization management of the sensor nodes is realized, and the service life of equipment is prolonged.

The invention discloses a new energy power generation equipment health management platform based on a large model, and relates to the technical field of power generation equipment health management. The system comprises a multivariate data acquisition and region identification module, a data preprocessing and region adaptation unit, a region perception large model analysis module, an intelligent operation and maintenance decision and scheduling module and a spare part inventory optimization sharing module. The multivariate data acquisition and area identification module deploys multiple types of sensors at key equipment parts of photovoltaic power generation stations in different areas, acquires equipment operation parameters and environment state data, endows the areas with unique codes and feature tags, and ensures that the data are traceable and are associated with area characteristics; according to the invention, the health management efficiency and accuracy of the new energy power generation equipment are significantly improved; the system not only realizes real-time monitoring and fault early warning of the equipment state, but also reduces the maintenance cost and improves the system operation efficiency by optimizing the operation and maintenance resource configuration and intelligent spare part inventory management.

The invention discloses an on-line energy scheduling optical storage, charging and conversion system, and belongs to the technical field of photovoltaic energy storage, the system comprises an energy generation module, an energy storage module, an energy management scheduling module and a temperature control module, the energy generation module comprises a photovoltaic array unit, the photovoltaic array unit can convert solar energy into electric energy, and the temperature control module is connected with the photovoltaic array unit. Green energy is provided for the whole system; the energy generation module is connected with a power grid through an inverter, and the inverter is also in communication with the energy management scheduling module, so that real-time monitoring and adjustment of power output are facilitated. The advanced energy management scheduling module is integrated to realize dynamic adjustment and efficient utilization of energy production, and the system can dynamically adjust energy production, storage and distribution strategies according to factors such as real-time load conditions and weather forecast, so that energy utilization is more reasonable, and energy waste is reduced.

The invention provides a distributed service intelligent master selection method and system based on double-engine cooperation, and relates to the technical field of intelligent master selection, and the method comprises the steps: collecting the processing capability index, the network throughput and the resource utilization rate of a service node to construct a performance data matrix, and generating an intelligent identification code based on a node scoring function and a physical clock value; nacos and Redis signals are monitored through a double-layer event monitor, the optimal incremental step length is determined according to the score sequence distribution trend, and a target score is generated to construct a dynamic priority sequence; and periodically selecting a main node and a preheating standby node, and when the main node fails, starting increment state synchronization based on Redis affairs after voting arbitration confirmation, so as to realize main and standby non-inductive switching. According to the method, the election accuracy of the main node and the switching reliability are improved, and the system interruption risk is reduced.

The invention relates to the technical field of tin smelting, and discloses an AI intelligent tin smelting control system and method, and the system comprises a data collection and preprocessing module, an intelligent decision and control module, and a real-time monitoring and early warning module. The method comprises the following steps: collecting process data in a tin smelting production process in real time, and cleaning the collected process data; extracting and selecting characteristics of which the influence on the smelting quality and efficiency exceeds a preset threshold value, and carrying out standardization processing on the data; receiving the standardized process data, training the model, processing the data monitored in real time, and outputting the adjusted process parameters; various key parameters and data in the production process are displayed in real time, the real-time data are analyzed, abnormal conditions are detected, early warning is carried out on faults, and response measures are automatically triggered. Intelligent and automatic tin smelting is achieved, and the efficiency, quality and safety of the smelting process can be effectively improved.

The invention relates to a real-time monitoring method and related equipment for an intelligent manufacturing digital twinning system, and the method comprises the following steps: constructing a three-dimensional digital twinning model based on a real-time image sequence; based on the three-dimensional digital twinborn model, analyzing whether an abnormal phenomenon exists in the production field, and if yes, performing abnormal region identification in the three-dimensional digital twinborn model through a preset abnormal identification algorithm to obtain abnormal state data; performing abnormity deduction on the production field based on the abnormal state data to obtain an abnormal evolution trend; and performing risk assessment on the production field based on the abnormal evolution trend to obtain a risk assessment result, and selecting a corresponding risk control strategy based on the risk assessment result, thereby solving the problem that a traditional method is difficult to provide enough details to comprehensively know reasons behind an abnormal phenomenon, and improving the risk control accuracy. And subsequent risk assessment and selection of control strategies are influenced.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

The invention discloses a multi-dimensional cutting precision control system applied to a laser pipe cutting machine, and relates to the technical field of laser cutting control, and the multi-dimensional cutting precision control system comprises a data acquisition module, a prediction module, an actual verification module, a judgment module and a response module; the data acquisition module is used for acquiring cutting track data of the initial laser pipe cutting machine, including but not limited to cutting speed, cutting depth, cutting angle and cutting path; a sensor is installed at the corresponding position of the laser pipe cutting machine; according to the laser pipe cutting machine, through the integrated data acquisition module, key parameters such as the cutting speed, depth, angle and path of the laser pipe cutting machine in the cutting process can be monitored in real time, the control precision of the cutting process is remarkably improved, and therefore the consistency and reliability of the cutting quality are ensured; the system can more accurately predict and compensate deviation possibly occurring in the cutting process, and the rejection rate caused by parameter fluctuation is reduced.

The invention discloses an air purification system for a clean workshop. The air purification system comprises a self-adaptive control unit, an air quality monitoring module, an air flow control assembly, an environmental parameter adjusting assembly, a self-optimization filtering assembly, a human-computer interaction interface and predictive maintenance system, an energy efficiency optimization module and a remote diagnosis and early warning system. On the basis of intelligent technologies such as machine learning, reinforcement learning and a data fusion algorithm, real-time monitoring, dynamic optimization and energy efficiency management of the air quality of the clean workshop are achieved. Based on real-time data and historical data, an algorithm is used for carrying out self-adaptive optimization on the air purification system, and manual intervention is reduced. And the running state of the system is dynamically adjusted through the energy efficiency optimization module, so that the energy consumption is reduced while the cleanliness is ensured. The system can diagnose equipment faults in advance, reduce downtime and prolong the service life of equipment. And due to a man-machine interaction interface and a remote monitoring function, an operator can know the state of the system at any time and carry out regulation and control.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building operation and maintenance management system and method based on a BIM platform. The method comprises the following steps: performing equipment connection topology network construction according to acquired target BIM building model data, and generating equipment physical connection topology data; performing automatic logic conflict elimination on the equipment physical connection topological data to generate equipment connection topological data; performing fault influence link analysis on the equipment connection topological data to generate fault propagation link data; performing fault severity evaluation on the fault propagation link data to generate equipment fault severity data; and performing intelligent operation and maintenance path planning according to the equipment fault severity data to obtain intelligent equipment operation and maintenance path data. Through fine division of the operation and maintenance equipment management space and analysis of the equipment fault propagation link, intelligent scheduling of the fault operation and maintenance task is realized, and the operation and maintenance efficiency and the resource allocation precision are remarkably improved.

The invention discloses an LED large screen automatic repair and fault-tolerant display system and method, and relates to the technical field of LED display. The method comprises the steps that a sensor network and a high-definition camera collect data in real time; when an abnormal area is found, an intelligent diagnosis algorithm carries out comprehensive judgment according to sensor data and an image processing result, and the position and the type of a fault lamp bead are determined; starting an automatic repair process; redundant pixels are started to carry out hardware restoration; the dynamic reconstruction algorithm automatically adjusts the arrangement and brightness of redundant pixels, so that the redundant pixels replace fault lamp beads to participate in display; the system automatically adjusts the resolution and the display mode of the large screen according to the number and the distribution of the fault lamp beads. The problem that the display effect of an existing LED large screen is reduced when part of LED lamp beads are damaged is solved, it is ensured that the large screen can still maintain the good display effect under the fault condition through an innovative automatic repair and fault-tolerant mechanism, and the reliability of the system and the user experience are improved.

The invention relates to the technical field of flow anomaly identification, and discloses a flow anomaly identification method for a numerical control machine tool protocol, which comprises the following steps of: 1, multi-source data acquisition: acquiring various types of data from a numerical control machine tool and a control system thereof by arranging a data acquisition module, the data sources include network protocol flow data, machine tool sensor data, operation log data and equipment environment data, and the data formats of the data sources are kept uniform; the collected original data is subjected to standardization processing, the problems of data dimension inconsistency, noise interference and missing values are solved, and alignment of the data on a time axis is guaranteed through a synchronization algorithm; through a multi-source data acquisition and high-precision sensor system, the real-time monitoring of the flow of each key fluid system of the numerical control machine tool is realized. And by combining a plurality of feature extraction methods, the traffic anomaly can be effectively identified.

The invention relates to the technical field of production process control, in particular to a PVC developing functional granule production control method and system, and the method comprises the steps: calling a spectrometer to capture spectral data emitted from PVC granules in real time based on the starting of a production line, analyzing the intensity and wavelength distribution in the spectral data in real time, and generating spectral data characteristic values; and comparing and analyzing the deviation of historical data and real-time data through the spectral data characteristic value, and generating a deviation dynamic prediction analysis result. According to the invention, by monitoring and analyzing the spectral data of the PVC granules in real time, the response speed and precision of a production line are improved, and the spectral data are compared in real time, so that production deviations such as light source intensity and reaction temperature can be quickly found and adjusted, material waste is reduced, the output quality is improved, and continuous data analysis helps to adjust chemical agent feeding and heating power; product consistency is guaranteed, automatic fault diagnosis is achieved, production parameters are adjusted in time, the service life of equipment is effectively prolonged, shutdown is reduced, and cost benefits are increased.

The invention relates to the technical field of building construction regulation and control, and discloses an efficient construction multi-element real-time dynamic regulation and control method based on an intelligent algorithm, and the method comprises the steps: building a personnel database, and recording the competent work types, skill levels and working experience of personnel; according to a construction project, screening personnel based on an intelligent algorithm, and performing personnel allocation; monitoring the material inventory of the construction project in real time; acquiring data of personnel position, material supply, equipment operation state, construction progress and construction quality of the construction site; based on an intelligent algorithm, adjusting task distribution of the personnel according to the acquired personnel position state data; according to the efficient construction multi-element real-time dynamic regulation and control method based on the intelligent algorithm, human resources can be reasonably utilized, supply of construction materials is guaranteed, the construction efficiency can be effectively improved, the construction quality is guaranteed, and the construction cost is reduced.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

The invention relates to an intelligent integrated modular cabinet control method, system and device and a medium, and the method comprises the steps: monitoring the external environment parameters of a cabinet in real time through a sensor group in the cabinet, and carrying out the preprocessing of the external environment parameters through an edge computing device, and obtaining multi-dimensional environment parameters; integrating and analyzing the multi-dimensional environmental parameters and the internal operation data of the cabinet by using a multi-modal data fusion algorithm, and outputting a fusion analysis result; according to a fusion analysis result, generating a digital twinborn model of the current environment and the equipment state of the cabinet in the virtual environment, and performing rapid fusion simulation analysis on different adjustment schemes through simulation drift and a genetic algorithm to obtain an optimized adjustment scheme; and adjusting the resource configuration of each module in the cabinet and the operation state of the heat dissipation equipment based on the optimization adjustment scheme. The equipment cabinet has the effect of improving the operation efficiency of the equipment cabinet.

The invention relates to the technical field of engineering early warning, in particular to a drilling engineering early warning method and system, and the method comprises the following steps: collecting a plurality of operation parameters of drilling equipment in real time based on a drilling engineering environment, screening the data, carrying out the comparative analysis of the parameters, evaluating the deviation between the current operation parameter and the conventional operation data, and carrying out the early warning of the drilling engineering. And obtaining equipment operation deviation information. According to the method, the operation parameters of the drilling equipment are collected and processed, key parameters such as the drill bit speed and torque are monitored in real time, deviation with conventional data is found in time, the abnormal condition of the equipment can be quantitatively evaluated by analyzing the frequency and duration of the operation deviation of the equipment, and the stratum data aggregation and trend analysis are combined. The safety of drilling operation is enhanced, the stability of a drilling site can be evaluated by extracting key features such as stratum density and rock hardness, risk classification is performed accordingly, risk early warning is achieved, and potential accidents are effectively avoided.

The invention discloses an automatic fault detection and repair integrated circuit design method based on a neural network. The method comprises the following steps: S1, obtaining and preprocessing operation data of an integrated circuit; s2, analyzing the preprocessed data by using a trained neural network model, and dynamically optimizing the model through an adaptive weight pruning algorithm; s3, inputting circuit operation data in real time, automatically identifying fault types and positions, and storing fault information; s4, starting an automatic repair mechanism based on the stored fault information, and generating a repair strategy through a multi-target collaborative optimization algorithm; s5, logic reconstruction is executed according to the repair strategy, a redundant circuit and parameter adjustment are started, and a repair path is optimized through deep reinforcement learning; s6, after repairing is completed, the neural network model is used for detecting and verifying the repairing effect again; and S7, after the repair verification is passed, updating the fault historical database. According to the invention, the automation level of fault detection and repair of the integrated circuit is improved, and the reliability and efficiency of the system are enhanced.

The invention discloses an optical storage and charging integrated intelligent management system and method based on a virtual power plant platform, relates to the technical field of optical storage and charging, and aims to solve the problems that in energy storage battery management, long-time storage may cause increase of self-discharge rate of batteries and aggravated capacity attenuation, but an existing system often adopts a unified management strategy, batteries with different storage times are not distinguished, and the energy storage efficiency is poor. According to the optical storage and charging integrated intelligent management system and method based on the virtual power plant platform, the storage time and temperature of the energy storage battery are incorporated into a charging and discharging management strategy, so that the service life of the battery is prolonged; the charging and discharging logic can be dynamically adjusted according to charging requirements and light energy input changes, the adaptability of the system to complex conditions is enhanced, energy waste and charging delay are avoided, and the energy utilization efficiency is improved; the maintenance cost and the downtime are reduced, and the overall reliability and flexibility of the system are improved.

The invention provides an AI-based servo driver fault on-line rapid diagnosis method and system, and the method achieves the quantitative evaluation of fault data through obtaining the candidate system fault monitoring data of a servo driver control system, and converting the candidate system fault monitoring data into a diagnosis difficulty index and a fault description knowledge attribute. When the diagnosis difficulty index exceeds the preset index and the fault data lacks dynamic change information, the method can further utilize a plurality of fault penetration derivation data and the associated fault knowledge networks matched with the data, and effectively identifies and integrates similar fault modes by calculating the state feature distance between the associated fault knowledge networks, thereby improving the fault diagnosis accuracy. Compared with the prior art, the method has the advantages that the accuracy and efficiency of fault diagnosis are remarkably improved, particularly in a complex and changeable fault scene, the fault reason can be quickly positioned, and data support is provided for instant maintenance and recovery of the servo driver, so that the downtime of a system is shortened, and the overall operation reliability and stability of equipment are improved.

The invention discloses a digital twinning method and device for energy loss and dynamic response of a steam pipe network and a computer program product, and the method comprises the following steps: S1, constructing a pipe network model according to the structural parameters of the steam pipe network; s2, acquiring current steam pipe network measuring point data, wherein the steam pipe network measuring point data comprises pressure and temperature of a gas supply end and mass flow of a user end; s3, parameters including the enthalpy value and the pressure difference of steam are obtained according to an IAPWS-IF97 formula; s4, the steam operation pressure and the specific enthalpy of adjacent nodes are solved according to node distribution of the steam pipe network; s5, the flow between the nodes is obtained according to the obtained pressure and specific enthalpy of the adjacent nodes; and S6, solving parameters of other nodes by analogy to obtain temperature and pressure of all nodes and flow parameters among the nodes. Through field experiment verification, the result shows that the method can effectively improve the calculation precision of the steam quality of the steam pipe network node, and is suitable for simulation research of most steam pipe networks under different working conditions.

The invention relates to the technical field of digital printing machines, in particular to an intelligent ink droplet control method and system of a digital printing machine and the digital printing machine. The method comprises the steps of real-time data acquisition, multi-dimensional parameter modeling, real-time ink droplet behavior prediction, self-adaptive ink jet parameter adjustment, closed-loop feedback control, medium characteristic self-adaptive matching, intelligent multi-region optimization and environmental factor compensation. Ink droplet behaviors and environmental data are collected in real time through a high-speed camera and various sensors, a nonlinear model is constructed by using a deep learning technology, and a dynamic parameter optimization curve is generated. According to the method, the printing quality and efficiency are remarkably improved, and the method is suitable for various media and complex scenes.