1139results about How to "Realize intelligence" patented technology

The invention relates to the technical field of logistics transportation, in particular to a logistics transportation route optimization method and system based on digital twinning. The method comprises the following steps: constructing a digital twin logistics base through multi-source heterogeneous data fusion and dynamic logistics network modeling; carrying out regional logistics distribution feature extraction and dynamic time sequence prediction based on a digital twinborn substrate to obtain a predicted regional transportation demand; performing real-time state evaluation on the line, generating a dynamic line performance matrix, and performing vehicle resource allocation in combination with the order task priority; and carrying out multi-objective solution and dynamic adjustment on the transportation scheduling scheme to finally obtain an optimal transportation scheduling scheme. And performing iterative optimization on the digital twin model through real-time deviation analysis and a feedback mechanism, and dynamically adjusting a transportation plan to form a closed-loop optimization process. Through the logistics transportation route optimization technology, the logistics transportation efficiency is remarkably improved, the cost is reduced, and the toughness of the system is enhanced.

The invention provides a photovoltaic station intelligent operation and maintenance simulation method and system based on digital twinning, and relates to the technical field of photovoltaic station operation and maintenance, and the method comprises the steps: constructing a digital twinning hierarchical structure through multi-modal detection signals through discrete wavelet packet transformation, tensor decomposition and a graph neural network, and determining structured data; extracting fault features by using a dynamic neural network, predicting a fault probability based on a Markov model, and triggering maintenance early warning; inputting the early warning signal into a maintenance strategy model based on multi-agent reinforcement learning, and generating a candidate maintenance time window set; the maintenance scheduling module optimizes sorting to obtain an optimal time window sequence, and the resource allocation module generates a personnel material allocation scheme by adopting a genetic algorithm; and iteratively optimizing the strategy parameters based on the maintenance cost, and outputting a final operation and maintenance implementation scheme.

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention provides an unmanned aerial vehicle accessory intelligent identification and access method and system based on a sensor, and relates to the technical field of unmanned aerial vehicle accessory management, and the method comprises the steps: collecting the three-dimensional contour information of unmanned aerial vehicle accessories, generating standard point cloud data, carrying out the feature extraction, and carrying out the feature alignment and interaction, generating a high-dimensional fusion feature matrix of the target unmanned aerial vehicle accessories; based on the feature pyramid network, constructing multi-scale feature representation, generating hierarchical feature descriptors, performing similarity calculation, generating a similarity score matrix, performing optimal matching, establishing an adaptive verification rule base and performing accessory verification; calculating an optimal action sequence of an access path based on model information, mapping the optimal action sequence into a motion track parameter of a mechanical arm, collecting multi-point stress data, predicting the optimal clamping force, establishing an accessory service life prediction model, performing prediction in combination with historical data, and generating an accessory state evaluation report and a maintenance suggestion.

The invention discloses an intelligent textile production line control method and system, and relates to the technical field of industrial automation, and the method comprises the steps: collecting operation data in a textile production process in real time, and carrying out the data preprocessing; dynamically adjusting process parameters of the textile production line based on the preprocessed data; detecting the quality state in the textile production process in real time, and adjusting the operation of the production line based on the detection result; constructing a multi-feature fusion fault prediction model, and predicting the operation state of the production line equipment; potential faults are recognized in advance according to prediction results, and the operation mode of the production line is adjusted. The method combines multi-feature fusion analysis, dynamically optimizes the equipment operation state, identifies and adjusts potential faults in advance, improves the production quality and efficiency, reduces the maintenance cost and the shutdown risk, is significantly superior to a traditional method, and provides powerful technical support for intelligent upgrading of the textile industry.

The invention relates to the technical field of data processing, and discloses an intelligent bridge reinforcement decision generation method, device and equipment based on damage identification. The method comprises the following steps: carrying out bridge damage identification and evaluation on a bridge state data set to obtain a bridge damage characteristic spectrum; performing intelligent decision analysis on the bridge damage characteristic spectrum, generating a preliminary reinforcement scheme, and obtaining a candidate reinforcement strategy set; performing parametric modeling and multi-scale simulation on the candidate reinforcement strategy set, and performing coupling calculation evaluation on a reinforcement effect to obtain an optimized reinforcement scheme; virtual construction planning is conducted on the optimized reinforcement scheme, a construction process model is generated through an intelligent scheduling algorithm, and a target construction scheme is obtained; and monitoring and feeding back the target construction scheme in real time, and dynamically optimizing the construction process through an adaptive control algorithm to obtain a target intelligent reinforcement implementation scheme. The intelligent bridge reinforcement decision generation efficiency and accuracy based on damage identification are improved.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention provides a mining intelligent watering dust fall method and system, electronic equipment and a storage medium, and relates to the field of watering dust fall. The method comprises the following steps: acquiring multi-source heterogeneous environment sensing data; preprocessing the multi-source heterogeneous environment sensing data to obtain preprocessed multi-source heterogeneous environment sensing data, and constructing a mining area holographic environment model based on the preprocessed multi-source heterogeneous environment sensing data; based on the holographic environment model of the mining area, extrapolating and estimating the dynamic change trend of the wind dust concentration by using a space-time sequence prediction algorithm to obtain a wind dust concentration distribution prediction result of each area of the mining area in a future time period; according to the technical scheme, dust suppression is taken as an optimization target, water mist parameters and a nozzle arrangement scheme are taken as decision variables, a mining area holographic environment model and a wind dust concentration distribution prediction result are taken as constraint conditions, an optimal spraying strategy meeting dust falling requirements is solved through a heuristic search algorithm, and the dust falling effect is improved and the water consumption is reduced through the technical scheme.

The invention relates to an activity scene arrangement scheme generation method and system, and the method comprises the following steps: obtaining activity scene data, and carrying out the analysis to obtain an activity theme and the characteristics of participants; objective condition parameters of the target site are obtained, and the candidate arrangement elements are screened; constructing a multi-dimensional attribute vector according to the screened arrangement elements; clustering the multi-dimensional attribute vectors, and performing spatial layout planning on layout elements; performing combined optimization on the generated spatial layout plan, color scheme and light effect setting; the time period of the activity is extracted, and the recommended arrangement scheme is adjusted according to the relevance; and performing visual rendering on the adjusted recommended arrangement scheme, and optimizing and determining an activity scene arrangement scheme. Intelligentization and individuation of activity scene arrangement are achieved, integrity, creativity and coordination of the arrangement effect are improved, meanwhile, influences of actual site conditions and activity time are considered, and a more optimized scene arrangement solution is provided for a user.

The present disclosure provides a control system, method, apparatus, medium and program product for a welding process. The control system comprises a sensor module configured to collect data in the welding process; the data analysis module is configured to pre-process data in the welding process, extract key features from the pre-processed data, perform weighted fusion processing on the key features to obtain comprehensive features, and perform defect identification through a machine learning model based on the comprehensive features to identify defect features; the knowledge graph module is configured to obtain a repair strategy corresponding to the defect characteristics according to the defect characteristics and by utilizing a knowledge graph; the control module is configured to transmit the data received from the sensor module in the welding process to the data analysis module, receive the repair strategy and generate a repair instruction based on the repair strategy; and the repairing module is configured to automatically adjust welding parameters or control an executing mechanism to conduct repairing operation based on the repairing instruction.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

The invention provides a self-organizing closed-loop feedback driven iterative optimization power exception processing method and system, and relates to the technical field of power exception processing. Comprising the steps of inputting modal data into a hybrid expert model, and obtaining comprehensive features of the multi-modal data through a weighted fusion method; based on the comprehensive characteristics, detecting the abnormality of the power system; adopting a reinforcement learning method to generate a plurality of processing suggestions for the power system abnormity; obtaining feedback information of a user on various processing suggestions, inputting the feedback information into the self-organizing mapping model for clustering, and dynamically adjusting the weight of each modal data in the hybrid expert model based on a clustering result; the feedback information serves as a reward for generating multiple processing suggestions in reinforcement learning, and generation of the multiple processing suggestions in the next round is optimized; and iteration is carried out until an optimal processing suggestion is obtained. According to the method, the accuracy, the real-time performance and the individuation degree of anomaly detection and processing can be effectively improved.

The invention relates to the technical field of intelligent control and optimization of a photovoltaic power generation system, and discloses a self-adaptive photovoltaic module inclination angle adjusting method and system based on environmental conditions, and the method comprises the steps: obtaining short-term weather forecast data through a meteorological data interface, analyzing the weather forecast data, and predicting the environmental change in several hours in the future; monitoring pollution data of the surface of the photovoltaic module in real time through a sensor, and judging whether the pollution degree affects the power generation efficiency or not; according to the prediction result and the pollution degree, the inclination angle of the photovoltaic module is dynamically adjusted, the power generation efficiency under the upcoming illumination condition is optimized, the inclination angle of the photovoltaic module is dynamically adjusted in combination with short-term weather forecast data obtained by the meteorological data interface, and the power generation efficiency of the photovoltaic system under the complex environment is remarkably improved. The system can quickly respond to environmental changes, and efficiency loss caused by adjustment lag is avoided. And multi-factor collaborative optimization is realized, and the maximum power generation efficiency of the photovoltaic system under various conditions is ensured.

The invention discloses an improved YOLOv11 crack identification and quantification method for a strip mine area and a slope thereof. The method comprises the steps of data preprocessing, model improvement and adaptability adjustment, initial identification model training, crack detection by applying a target identification model, local area identification and post-processing. According to the method, technical means such as a feature extraction network improvement method based on an AReLU function and an ACmix model and a self-adaptive local threshold segmentation algorithm based on a gray scale standard deviation are introduced, and efficient and accurate recognition of cracks is achieved. According to the method, the automation degree of crack detection is improved, the labor cost and the time cost are reduced, and the method is of great significance to safety monitoring of the strip mine area.

The invention relates to the technical field of humidity compensation, in particular to a humidity gradient compensation method and device for an alternating humidity heat test box. The method comprises the following steps: acquiring test box state monitoring parameters based on a multi-dimensional sensor array; performing spatial humidity gradient distribution analysis on the state monitoring parameters of the test box, performing discrete interpolation fitting, and constructing a three-dimensional humidity gradient trend network; carrying out internal dynamic airflow diffusion analysis on the alternating damp heat test box so as to generate dynamic airflow diffusion data of the test box; carrying out dynamic humidity diffusion path mining on the three-dimensional humidity gradient trend network based on the dynamic airflow diffusion data of the test box, and constructing a dynamic humidity diffusion twin model; acquiring external environment monitoring parameters of the test box; and carrying out environment change association mining on the three-dimensional humidity gradient trend network based on the test box external environment monitoring parameters so as to generate humidity gradient change association data under environment fluctuation. According to the invention, efficient and accurate humidity gradient compensation is realized.

The invention relates to the technical field of audio testing, and discloses an intelligent audio testing system frequency response analysis method, device and equipment, and the method comprises the steps: carrying out the audio parameter initialization of an intelligent audio testing system, and obtaining a testing parameter matrix and an excitation signal; performing dual-channel acquisition and time-frequency feature extraction to obtain a time-frequency feature data set; executing double-branch processing of time domain noise reduction and frequency domain enhancement to obtain a purified feature data set and a feature weighting matrix; performing segmented recursive calculation and feature aggregation to obtain a frequency response feature vector; decomposing the frequency response feature vector according to a test frequency band, and performing environment compensation and nonlinear calibration to obtain a standard frequency response data set; and inputting the standard frequency response data set into the deep neural network for feature recognition and mode classification to obtain a frequency response curve and a phase characteristic curve, thereby improving the test precision, the anti-interference capability, the automation degree and the adaptability of the intelligent audio test system.

The invention provides a method and a system for intelligently generating a complete dispatch report for financial unfavorable assets, and relates to the technical field of data investigation and arrangement, comprising Sp1, complete dispatch demand acquisition, Sp2, intelligent reasoning planning, Sp3, data acquisition and processing, Sp4, public opinion retrieval and analysis, Sp5, legal information extraction and verification, Sp6, data integration and report generation, and Sp7, report modification and output, and the method comprises the steps of Sp1, complete dispatch demand acquisition, Sp2, intelligent reasoning planning, Sp3, data acquisition and processing, Sp4, public opinion retrieval and analysis, Sp5, legal information extraction and verification, Sp6, data integration and report generation, and Sp7, report modification and output. By integrating a large model, an online retrieval tool, a data table tool and a key point extraction tool, automation and intellectualization of a complete dispatching process are realized. The system can automatically acquire, process and integrate data, greatly improves the efficiency of data acquisition, analysis and report generation, and reduces the probability of manual intervention and errors.

The invention discloses a conflict detection processing method and system based on a BIM model, a medium and a product, and relates to the technical field of constructional engineering, and the method comprises the steps: obtaining a construction project design scheme; based on a preset model framework, determining a BIM model according to the construction project design scheme; predicting potential conflict points in the BIM model based on a preset conflict mode library; matching the potential conflict points with the building structure, the electromechanical pipeline and the equipment layout by using a space mapping algorithm, and determining possible conflict areas; on the basis of a natural language processing technology, operable solution suggestions are generated according to the possible conflict areas; after the operable solution suggestions are sent to the user, the solution suggestions determined by the user are received; and adjusting the BIM model based on the solution suggestion. By implementing the method, the accuracy and intelligence of conflict detection can be improved.

The invention provides a large-model-assisted dynamic configuration and quick response method for a knitting production line, and the method comprises the steps: obtaining the historical data of the knitting production line according to the dye molecular weight, the dye polar group number and the dye molecular space structure as dye property parameters, and constructing a dye property data warehouse; according to historical data of a knitting production line, dye molecular structure parameters, yarn surface morphology, material characteristics and friction coefficient test data are selected, and a correlation prediction model of dye properties and yarn friction coefficients is constructed; according to dye properties and friction coefficient distribution characteristics, performing clustering grouping on dye formula compositions, determining dye combinations having similar influence on yarn friction coefficients, and forming a dye formula and friction coefficient mapping library; knitting mechanical structure parameters, a knitting needle motion trail equation, a fabric organization structure matrix and fabric density parameters are obtained and combined with a dye formula and friction coefficient mapping library, and a weaving process digital twinning model is constructed.

The invention relates to the technical field of battery testing, and discloses a truck lithium battery testing method and device based on Bluetooth communication. The method comprises the following steps: performing intelligent remote matching processing on Bluetooth identification information of a truck lithium battery management system to obtain a remote Bluetooth control key, and performing lithium battery data acquisition protocol test processing to obtain a dual-channel acquisition instruction set; performing dynamic voltage, current and temperature cooperative acquisition processing on the dual-channel acquisition instruction set to obtain a multi-dimensional battery working condition characteristic spectrum; remote parameter linkage calibration processing is carried out on the multi-dimensional battery working condition characteristic spectrum, an intelligent calibration control sequence is obtained, remote charging and discharging management test processing is carried out, and a battery running state monitoring record is obtained; and performing remote health assessment processing on the battery running state monitoring record to obtain a lithium battery performance test report. According to the lithium battery testing scheme, physical connection is not needed, operation is easy and convenient, and the real-time monitoring capability is achieved, so that the testing efficiency and flexibility are improved.

The invention discloses a cable fault positioning method and system based on reflected wave decomposition and kurtosis sorting, and aims to solve the problem that a cable fault is difficult to detect under a low impedance change rate. The method comprises the steps of transmitting an incident excitation signal to a cable to obtain a first reflection signal in the cable; an improved VMD algorithm is adopted for decomposition to obtain K IMF components, and the IMF with the maximum kurtosis is selected as a second reflection signal; acquiring a time-frequency distribution curve of the second reflected signal by using short-time Fourier transform and frequency spectrum redistribution technologies; and constructing a two-dimensional energy-time curve by extracting an energy value and time under the frequency of the maximum energy amplitude point, and determining a fault position. The system is composed of an incident excitation module, a decomposition module, an STFT-RSP module and a fault positioning output module, and the positioning method is achieved. According to the method, the limitation of a traditional spread spectrum time domain reflection positioning method in cable fault detection is effectively overcome, and an efficient and accurate instructive technology is provided for cable fault positioning and detection.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

The invention relates to the technical field of energy efficiency optimization, and discloses a lamp energy efficiency optimization method based on artificial intelligence, and the method comprises the steps: data collection: carrying out the all-around and multi-dimensional data collection of an environment where a lamp is located through a plurality of sensors. According to the lamp energy efficiency optimization method based on artificial intelligence, in order to make energy utilization efficient, the multi-dimensional environment and lamp operation data are obtained through the data acquisition step, optimization is carried out in combination with the adaptive learning algorithm, lamp energy efficiency control parameters can be adjusted in real time according to different environments, places, time periods and other situations, and the energy efficiency optimization efficiency is improved. The lamp can be kept in the optimal energy efficiency state in various complex and changeable environments, the energy utilization efficiency of the lamp is effectively improved, energy waste is reduced, in the scene-based energy efficiency optimization link, for different application scenes, differentiation strategies are implemented according to respective typical lighting requirements and energy-saving potentials, parameters such as lamp brightness are accurately adjusted, and the energy efficiency of the lamp is improved. And the energy utilization efficiency of the lamp in various scenes is improved.

The invention belongs to the technical field of intelligent agricultural planting, and particularly relates to an agricultural planting decision-making method and system based on a large language model.The technological process of the method includes the steps that firstly, historical field data and field data are preprocessed, and then processing such as feature extraction, clustering and sensor data fusion is conducted; and finally, predicting according to the agricultural data set and sending to the user. The concrete structure of the system comprises a data processing module, an intelligent agent module and an agricultural interaction model module. The system can be widely applied to precision agriculture, and helps farmers to implement precise management strategies such as precise fertilization, irrigation and pest control according to specific environment and crop requirements, so that the yield and quality of crops are improved. According to intelligent greenhouse management, environment parameters such as temperature, humidity and illumination are monitored in real time through an environment sensor, and after data are processed, an intelligent agent can provide an optimized management strategy to adjust conditions in the greenhouse, so that an optimal environment is created for growth of plants.

The invention belongs to the field of irrigation systems, and provides an intelligent water-saving irrigation system based on the Internet of Things, which comprises a control terminal, and the control terminal comprises a control module, a communication module, a data analysis module and a power supply module; the data acquisition module comprises a meteorological sensor, a soil humidity sensor and a plant growth state monitor which are electrically connected with the communication module; the irrigation execution module comprises an adjustable electric valve and a variable frequency water pump which are controlled in a partition mode, and irrigation operation is conducted according to the instruction sent by the control module; by arranging the control terminal, the data acquisition module and the irrigation execution module, the soil humidity and the plant water demand condition can be accurately sensed, the system can avoid unnecessary irrigation, water resource waste is effectively reduced, full automation and intelligentization of the irrigation process are achieved, manual intervention is reduced, and the working efficiency is improved.

The invention relates to an intelligent compaction monitoring method and system for a highway subgrade pavement. The method comprises the following steps: collecting construction site data of the highway subgrade pavement; wherein the construction site data comprises soil mass density, water content and temperature; inputting the construction site data into a compactness prediction model to obtain the compactness of the roadbed and the pavement; wherein the compaction degree prediction model is constructed based on a quantitative relation between construction site data and radar signals; and carrying out early warning on the compaction degree. According to the invention, intelligent and precise monitoring of the compaction degree of the roadbed and the pavement can be realized, the accuracy and the reliability of compaction quality evaluation are improved, data support is provided for optimization of a compaction construction process, and the roadbed and pavement engineering quality is effectively guaranteed.

The invention discloses a multi-sensor fusion unmanned aerial vehicle environment monitoring and early warning system, and relates to the technical field of environment monitoring and early warning, and the system comprises the following components: an unmanned aerial vehicle platform which is used as a carrying basis of sensors and data processing equipment and has excellent flight stability and strong cruising ability; according to the invention, by introducing a task planning mechanism based on autonomous learning, the unmanned aerial vehicle can continuously learn and optimize the flight strategy when executing the monitoring task, the innovation point significantly improves the efficiency of task execution, and the unmanned aerial vehicle can perform real-time monitoring according to the actual environment feedback and historical task data. The unmanned aerial vehicle can automatically adjust the parameters of the flight path, speed and height to avoid adverse factors and select the optimal path, and meanwhile, the unmanned aerial vehicle is endowed with strong scene adaptability by the autonomous learning ability, so that the unmanned aerial vehicle can quickly respond to a complicated and changeable natural environment or a sudden emergency, and smooth completion of a task is ensured.

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.

The invention discloses a multi-source information fusion irrigation decision optimization method and platform, and relates to the related field of data processing, and the method comprises the steps: determining a data collection node, and enabling the data collection node to be determined by taking a preset time interval as a reference and taking an interval irrigation factor feature as a compensation amount; triggering source end acquisition equipment, synchronously performing data acquisition on the irrigation area, and determining multi-source information; performing fuzzy set analysis on the irrigation factors, mining a confidence coefficient matrix, and supervising and training an irrigation decision model; transmitting the multi-source information to an irrigation decision model, executing deisomerization and matrix operation decision, and determining an irrigation strategy; an intelligent irrigation decision-making system is carried, an irrigation strategy is responded, and irrigation equipment is managed and controlled. The technical problems that an existing irrigation decision is lack of intelligentization and low in decision precision are solved, and the technical effect of achieving intelligentization and precision of irrigation management is achieved.