214 results about "Plant management" patented technology

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention discloses an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

The invention relates to a cotton field carbon sink dynamic evaluation method and system based on multi-source data fusion. The method comprises the following steps: firstly, comprehensively collecting multi-source data of a cotton field area environment, and processing the multi-source data by using a time window alignment technology to generate a multi-source heterogeneous data set; based on the data set, key features are extracted through a data mining means, and a key influence factor weight table is generated; and then, inputting the key influence factor weight table and the multi-source heterogeneous data set into the trained optimization evaluation model together to obtain cotton field carbon sink space-time distribution characteristics. And finally, carrying out correlation analysis on the spatial-temporal distribution characteristics and meteorological continuity data, and generating a curve capable of visually displaying the dynamic change of the cotton field carbon sink along with time. The method overcomes the limitation of a single data source, greatly improves the accuracy and comprehensiveness of cotton field carbon sink evaluation, can accurately reflect the real state of cotton field carbon sink under different time-space conditions, and provides a key basis for agricultural managers to formulate scientific and reasonable cotton field planting management strategies.

The invention relates to the technical field of air separation plant management, and discloses an air separation plant energy efficiency intelligent management system based on data analysis, which comprises a data acquisition module, an energy efficiency analysis module, an optimization strategy generation module, a real-time adjustment module and a dynamic feedback module. The data acquisition module acquires and stores data in real time in a multi-source heterogeneous manner. The energy efficiency analysis module uses a hybrid convolutional neural network and an adaptive wavelet packet decomposition algorithm to extract features. And the optimization strategy generation module formulates a strategy in combination with deep reinforcement learning and a multi-target particle swarm optimization algorithm. And the real-time adjustment module dynamically adjusts equipment parameters based on fuzzy logic control. And the dynamic feedback module forms closed-loop optimization. The system solves the problem of energy efficiency management of traditional air separation equipment, and can accurately collect and process data, deeply analyze energy efficiency, intelligently generate optimization strategies, accurately adjust parameters in real time and continuously optimize the parameters, improve the energy efficiency of the air separation equipment, reduce energy consumption, enhance the operation stability and reliability of the equipment and assist industrial sustainable development.

The invention relates to the technical field of agriculture, in particular to an intelligent planting management method and system for Huai apples, and the method comprises the following steps: S1, monitoring soil environment parameters around roots in real time; s2, evaluating the health state of the root system based on the data collected in the S1; s3, adjusting irrigation and fertilization parameters according to the root health score obtained in S2; s4, configuring an unmanned aerial vehicle to execute a regular flight task, and collecting growth state images of the Huai apple trees; s5, comprehensively analyzing the image data collected in the step S4 and the root system health score data in the step S2; and S6, automatically generating a planting management report and an adjustment suggestion based on an analysis result of S5. According to the invention, by integrating environment monitoring, unmanned aerial vehicle imaging and machine learning analysis technologies, real-time and accurate planting management suggestions are provided, so that the growth quality and yield of the Huai apples are significantly improved, resource use is optimized, and the efficiency and sustainability of agricultural production are enhanced.

The invention discloses an intelligent operation risk analysis method and system based on deep learning, and relates to the technical field of power plant management, and the method comprises the steps: collecting the operation original data of power plant equipment in real time; carrying out data fusion on the collected data, and extracting equipment evaluation data; analyzing the equipment evaluation data through the long-short-term memory neural network model, and predicting an equipment fault based on an analysis result; based on the equipment fault prediction result, generating an equipment scheduling scheme by using a particle swarm optimization algorithm, and optimizing the scheduling scheme; and converting the optimized equipment scheduling scheme into a control instruction, and generating and executing equipment scheduling through a low-code development platform. According to the intelligent operation risk analysis method based on deep learning, a scheduling scheme can be optimized according to the health state and the fault risk of the equipment, the operation efficiency of the equipment is improved, a repair and maintenance plan is effectively arranged, and the problem of excessive maintenance or repair lag is avoided.

The invention discloses an intelligent disk monitoring system based on multi-modal data fusion and a fault early warning method, and relates to the technical field of power plant management, and the method comprises the steps: collecting the real-time operation data of equipment; performing dimension reduction processing on the operation data by using a principal component analysis algorithm; inputting the data subjected to dimension reduction into a long short-term memory network model, modeling the time sequence dependence of the data by using the long short-term memory network model, and predicting the future operation state of the equipment; based on the prediction result of the operation state, performing feature extraction on the data through a convolutional neural network; according to an output result of the convolutional neural network, alarm signals of different levels are generated according to set threshold levels; and according to the grade of the alarm signal, adjusting operation parameters of the equipment through a feedback control mechanism. According to the intelligent disk monitoring system based on multi-modal data fusion, the accuracy, the real-time performance and the intelligent level of equipment monitoring are improved, and the fault early warning precision and the system response speed are remarkably improved.

The invention provides a crop planting management method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring current environment state information; inputting the current environment state information into a pre-trained reinforcement learning model to obtain a global management decision output by the reinforcement learning model; wherein the reinforcement learning model is determined by completing various crop planting management decisions through cooperation of multiple agents according to a multi-objective optimization reward function. According to the crop planting management method provided by the invention, climate change information, soil change information and crop growth change information are taken as real-time environment change, and further through a complex reinforcement learning model, a multi-objective optimization reward function and a collaborative optimization mechanism, the crop planting management information is obtained. Various crop planting management decisions in agricultural production are continuously optimized according to real-time environment changes, the efficiency and accuracy of agricultural intelligent decision making are greatly improved, and the method is suitable for large-scale crop planting, especially intelligent management in a dynamic environment.

To enable understanding of a plant growth state regardless of the skill level of a worker.SOLUTION: The growth state estimation device may include an acquisition unit that acquires multiple indices related to leaf expansion of a plant. The growth state estimation device may include an estimation unit that estimates the growth state of the plant based on relationship information indicating a relationship between the multiple indices related to leaf expansion of the plant and the growth state of the plant, and the multiple indices acquired by the acquisition unit. The multiple indices may include at least two of leaf hardness, leaf thickness, vein density, stem thickness, leaf color, vein color, leaf shape, leaf size, phyllotaxis, and leaf attachment angle.SELECTED DRAWING: Figure 5

The invention belongs to the technical field of wind power plant management, and relates to a method and system for real-time positioning and trajectory analysis of wind power plant workers. The method comprises the following steps of: preprocessing multi-source position data by utilizing an edge computing node; fusing the preprocessed multi-source position data to generate a high-precision positioning result; performing trajectory analysis by using a deep learning algorithm to obtain a trajectory analysis result; performing intelligent task allocation by combining wind power plant equipment state data and environment data; on the basis of the physiological data and the environmental data of the staff, health condition monitoring and safety risk assessment of the staff are carried out; generating an equipment maintenance strategy by using a predictive maintenance algorithm in combination with the trajectory analysis result and the wind power plant equipment state data; and a worker real-time trajectory analysis result, an intelligent task allocation result, a monitoring and evaluation result and a standby maintenance strategy are visually presented. The positioning precision is improved, track analysis is accurate and reasonable, real-time safety monitoring of workers can be achieved, and the intelligent degree is high.

The invention provides an abnormal data detection and recovery method and system for reports of a water plant management and control platform, and relates to the technical field of water plant data detection.The abnormal data detection and recovery method comprises the steps that report data of the water plant management and control platform are collected, then the threshold range of the report data is obtained through threshold detection, and the report data exceeding the threshold range are marked as potential abnormal data; performing exception scoring on each potential exception data in the report data by adopting an exception detection model, and then marking the potential exception data with an exceptional exception scoring result as burr data points; and deleting each burr data point in the report data, and then estimating missing data points by utilizing a linear relationship between adjacent data points of the deleted burr data points to carry out linear interpolation so as to carry out data recovery on the report data. The method has the beneficial effects that the accuracy and stability of data analysis of the water plant management and control platform are improved through multi-stage anomaly detection, and efficient detection and recovery of abnormal data are realized through combination of statistical analysis and machine learning technologies.

The invention belongs to the technical field of agricultural supervision, and particularly relates to an intelligent agricultural planning decision-making system based on the Internet of Things, which comprises a planting plan decision-making unit, a resource allocation decision-making unit, an irrigation and fertilization monitoring unit, an agricultural planting management and analysis unit and an agricultural management and control terminal, planting plan decision-making information is generated through the planting plan decision-making unit to improve planting benefits, and monitoring analysis of the resource distribution decision-making unit and the pest and disease damage monitoring decision-making unit is beneficial for achieving precise irrigation and fertilization and timely preventing and treating pest and disease damage. The irrigation and fertilization monitoring unit analyzes the nutrient water content suitability abnormal performance of the corresponding area in the detection period, enhances subsequent irrigation and fertilization supervision when an irrigation and fertilization alarm signal is generated, and comprehensively evaluates the planting management performance of the corresponding area when an irrigation and fertilization qualification signal is generated; and subsequent planting supervision is enhanced when the planting management alarm signal is generated, so that the growth of crops in corresponding areas is ensured, and the intelligent level is high.

The invention provides a new energy power plant assessment and compensation information transparent management method and system, and relates to the technical field of new energy power plant management, and the method comprises the steps: predicting a power generation capability reference value through a deep learning model, and constructing a power generation capability assessment model based on the reference value, and calculating a comprehensive assessment reference value in combination with the real-time power generation data and a preset assessment rule, performing grading assessment on the power plant, and generating a report. And a cause-and-effect analysis model is utilized to judge a power generation loss reason, if the power generation loss reason is caused by power grid dispatching limitation, compensation power data is calculated, and related data is written into the alliance block chain. And the intelligent contract automatically calculates a compensation amount, calculates an operation efficiency index based on historical data, generates a transparent management report, and distributes the transparent management report to users with different permissions through an encryption channel.

The invention relates to the technical field of information management, in particular to an informatization concrete mixing plant management system. In the invention, through periodically distributing the state labels of the inventory remaining amount of the concrete materials and generating the time sequence, a dynamic label mapping relation can be established for inventory management of the concrete materials, and the coupling degree between the inventory state and the consumption trend is enhanced. In a process of deducing a future state change path by using a state label sequence, frequency statistics and transition probability modeling between label pairs are adopted, so that inventory change trend prediction has a data-driven evolution capability, and the adaptability and foresight of prediction are improved. In the aspect of analysis of the running state of the conveying belt, running tracks of continuous time periods are generated in a potential energy space by means of a normalized vector form of running parameter tension, belt speed and motor load, so that the running state can specifically express and monitor the jamming trend in a multi-dimensional space, and the accuracy of abnormal state recognition is enhanced.

The invention relates to the technical field of agricultural automation, and discloses a rape seedling stage missing seedling detection method based on an unmanned aerial vehicle and image processing, and the method comprises the following steps: S1, collecting field rape seedling videos for two consecutive days by the unmanned aerial vehicle; s2, identifying and counting seedlings in real time by adopting a YOLOv8 model and a ByteTrack algorithm; s3, comparing the number of seedlings in two consecutive days, and if the count in the next day is increased, repeating the counting work to prevent a non-seedling-emergence area from being misjudged as seedling missing; if the count is not increased, entering the next step; s4, the unmanned aerial vehicle collects seedling photo data again; s5, using a YOLOv8 model to identify seedling positions, and extracting coordinates; s6, using an improved DBSCAN algorithm to classify the coordinate points; s7, fitting a compound row straight line by a least square method, and judging whether a seedling missing area exceeding a distance threshold exists or not; s8, calculating the position where seedlings need to be supplemented with the seedlings; s9, a seedling supplementing instruction is sent to the equipment, and the process is completed. According to the method, the rape planting management efficiency is improved, the labor cost is reduced, and the crop yield and quality can be improved.

The invention relates to the technical field of personnel monitoring, in particular to a power plant three-dimensional model building personnel real-time positioning method and related equipment. Comprising the steps of receiving a first job number of a first to-be-checked person; obtaining a unique identifier of a first UWB positioning tag associated with the first job number according to the first job number; a positioning label database is accessed, corresponding first position signal data are inquired and obtained based on the unique identification, and the first position signal data comprise first space coordinate information; analyzing coordinate data in the first space coordinate information, and converting the coordinate data into preset positioning coordinates in a power plant three-dimensional model; projecting the converted positioning coordinates into a preset power plant three-dimensional model; continuously adjusting position information of the first to-be-checked person in a preset power plant three-dimensional model based on the first space coordinate information updated in real time; real-time positioning is realized, power plant management personnel can quickly master personnel distribution and movement conditions and respond to emergency situations in time, and the safety and emergency response capability of power plant operation are remarkably improved.

The invention discloses an intelligent power plant management system construction method, and relates to the technical field of intelligent power plant construction. The method comprises the following steps: constructing a data integration platform based on a cloud architecture, and uniformly storing and managing data of a power plant in each stage from planning, design, construction, operation and maintenance to decommissioning; deploying a sensor network, and collecting equipment data information in real time; the collected data information is deeply mined and analyzed, an equipment fault diagnosis model and an operation state prediction model are established, and the health condition of the equipment is monitored in real time; a unified management and scheduling platform of intelligent applications is constructed, and centralized management and cooperative scheduling are carried out on various intelligent applications of a power plant. By constructing a data integration platform based on a cloud architecture, unified storage and management of data in the whole life cycle of the power plant are realized. A solid foundation is provided for subsequent data analysis and intelligent application, and the timeliness and accuracy of data acquisition are improved.

The invention relates to the technical field of intelligent agriculture, in particular to a planting management method and system for intelligent agriculture, and the method comprises the steps: S1, obtaining multi-modal data through configuring an independent sensing module and a local control execution end for each micro-block; s2, recognizing a current crop variety in real time according to the multi-modal data by adopting a low-power-consumption edge computing device in combination with image recognition; when the system is used, each micro-block operates independently, maintenance of each plant can be refined, multi-source sensing data can be fused, the problems of data lag and weight deviation among different sensors are solved, full-automatic feedback regulation and control are achieved, the labor burden of workers is reduced, low-power-consumption and high-real-time recognition is facilitated, multi-source data such as images and fusion of temperature and humidity are achieved, and the system is suitable for popularization and application. According to the method, the identification accuracy is improved, the problem of crop confusion which is like a long variety can be solved conveniently, accurate management in multi-variety mixed cropping planting can be achieved conveniently, and the accuracy of fine management and control of different demands of multiple different crops in a small range can be improved.

The utility model belongs to the technical field of unmanned agriculture, and particularly relates to a covering plant management device for an unmanned farm, which comprises a C-shaped planting assembly, a first driving assembly and a second driving assembly, the C-shaped planting assembly comprises a plurality of C-shaped planting plates, corresponding C-shaped sowing grooves are formed in the C-shaped planting plates, cutting knives are arranged in the C-shaped sowing grooves, the cutting knives are in sliding fit along the inner walls of the C-shaped sowing grooves, supporting rods are vertically arranged on the upper end faces of the cutting knives, blocking pieces are arranged at the upper ends of the supporting rods, and the blocking pieces obliquely extend into the C-shaped sowing grooves; the first driving assembly is connected with the cutting knives; according to the application management device, the structure is simple, unmanned crop laying and covering can be achieved, the covering plants are pressed to the C-shaped inner side (the roots of grapes) of the C-shaped planting disc through the blocking pieces, it is ensured that the cut-off covering plants can cover the roots of the grapes, and covering and soil moisture conservation are conducted on the grapes.

The invention discloses a smart tea garden planting management system and method. The smart tea garden planting management system comprises a multi-source sensor network, an edge computing gateway, an AI decision unit and a control execution unit. The soil fertility and pest and disease damage are monitored in real time through a high-precision sensor, and precise recognition of disease spots and pests is achieved in combination with a YOLOv5 algorithm; the AI decision-making unit dynamically generates variable fertilization and graded prevention and control instructions according to the growth stages of the tea trees, and controls the water and fertilizer integrated device and the biological prevention and control equipment to execute operation. The system supports adaptation of multiple tea types, compatibility of new and old facilities and stable operation in an extreme environment, the fertilizer utilization rate is remarkably increased, the pesticide usage amount is reduced, and the system is suitable for intelligent and standardized management of large-scale tea gardens.

The invention relates to the technical field of smart agriculture, and provides a digital twinborn decision support system for farmland management and a control method thereof, and the system comprises a digital farmland module which is used for collecting a plurality of farmland parameters; the crop growth phenotype monitoring module is used for collecting and analyzing growth state phenotype data of crops to obtain simulation values of crop growth parameters; the crop production management model decision module comprises a crop knowledge model which is used for fitting a growth change curve of a compound according to a plurality of farmland parameters; the crop three-dimensional structure function model is used for performing three-dimensional reconstruction on a crop population and calculating true values of crop growth parameters; the production management decision calculation model is used for deciding a water and fertilizer regulation prescription according to the simulation value and the true value, determining disease information of crops according to the growth state phenotypic data and determining a pesticide application decision prescription in combination with pesticide application requirements, so that an agricultural implement performs water, fertilizer and pesticide comprehensive operation; according to the system, the crop planting management efficiency and the intelligent level are improved.

The invention discloses a protection setting calculation and optimization method based on an intelligent power plant management platform, and relates to the technical field of power system protection. By constructing a platform and equipment communication link, integrally collecting and preprocessing internal and external parameters, and dynamically updating topology and model parameters in combination with a switch state, the problem that a traditional static model is difficult to adapt to operation changes is solved, the timeliness and accuracy of the model are improved, a multi-scene database is constructed in a classified mode, labels are labeled, and the efficiency is improved. Fault current distribution is accurately calculated, constant value adaptability verification is carried out, constant value optimization is carried out in combination with a multi-objective optimization algorithm and sensitivity analysis, limitation of single-scene constant values is avoided, constant value conflicts under different scenes are reduced, reliability of initial constant values is guaranteed, sensitivity analysis is superposed, it is ensured that the constant values are still reliable during parameter fluctuation, and the reliability of the constant values is improved. The risk of protection operation refusal and misoperation is reduced, a constant value is issued, the device state is monitored in real time, evaluation indexes are calculated, the constant value is controllable in the whole process, and stable operation of the device is guaranteed.

The invention discloses an integrated management system for a concrete mixing plant, and relates to the technical field of mixing plant management, a data acquisition unit is accessed to a raw material supplier system to acquire raw material supply data and concrete usage plans in different time periods, and a demand prediction unit introduces meteorological information and construction site states to predict the concrete usage plans in different time periods. And the dynamic optimization module substitutes the raw material supply data and the concrete dosage demand result into a dynamic adjustment model, the dynamic adjustment model dynamically adjusts the stirring progress, a stirring task queue is established, and the production sequence is intelligently sorted according to the material supply condition and the delivery priority. The management system not only can automatically generate a stirring task queue according to the material arrival condition and the construction usage priority, effectively avoid raw material waste and production plan conflicts, but also can adjust the delivery time, the loading sequence and the vehicle scheduling path in a linkage manner, break through the management breakpoint between production and transportation, and improve the production efficiency. And the overall cooperation efficiency and the customer response speed are improved.

The invention discloses a global-oriented multi-language multi-time-zone power station management method and system, and relates to the technical field of electric power digital management, and the method comprises the steps: collecting power station data, carrying out the standardized identification, and constructing a power station multistage coding identification main key; a multi-language mapping system is constructed by taking a multi-level coding identification main key of a power station as a drive, and time zone offset logic is constructed by using standardized identification; and performing hierarchical authority control based on the power station multi-level coding identification primary key prefix and generating a localized alarm prompt. According to the method, standardized identification of power station data is realized by constructing a unified power station multi-level coding main key; based on primary key driving multi-language mapping and time zone offset processing, accurate presentation and time consistency of data in a multi-language environment are guaranteed; authority control is achieved through primary key prefix division, multi-language alarm prompts are triggered in combination with signal coding, and a power station event management mechanism which is clear in alarm semantics, timely in response and controllable in boundary is achieved.

The invention discloses a crop target character accurate matching algorithm model, and belongs to the technical field of agricultural information, and the crop target character accurate matching algorithm model comprises a data and knowledge management unit, an intelligent decision-making unit and a closed-loop optimization mechanism. Constructing a domain knowledge base by integrating genetic, environmental and agronomic data, generating genotype-environment-character combined response virtual data, and training a character prediction model by using deep learning; small sample scene parameters are optimized in combination with transfer learning, a decision report containing genetic function annotations and environment adaptation analysis is generated after multi-dimensional rule verification, and a closed-loop optimization mechanism is formed through user feedback. The character matching precision is remarkably improved, the test cost is reduced, and the method is suitable for precision agricultural breeding and planting management.

The invention discloses a factory data management system and method based on digital twinning, relates to the technical field of factory management, and solves the problems that in the prior art, equipment operation parameters are collected mostly through a sensor, abnormity is judged based on a static threshold value, but coupling influence of environment variables on faults is not fused, misjudgment is easily generated due to environment fluctuation, and meanwhile, the fault rate is low. The comprehensive optimization of the production efficiency is neglected, so that the monitoring and early warning precision is not high; the method comprises the following steps: constructing a digital twinborn model of factory production equipment; the method comprises the following steps: acquiring simulation data during simulation operation of production equipment and environment data at a corresponding moment; calculating an evaluation coefficient based on the simulation data; based on the environment data, constructing a correction model to correct the fault evaluation coefficient, and obtaining a target fault coefficient; predicting the current simulation data based on a pre-trained fault prediction model to obtain a prediction result; and finally, whether early warning is carried out is judged based on a prediction result. The technical problem is solved.

The invention relates to the technical field of plant management, and discloses a chaenomeles speciosa remote management and data modeling method based on a cloud collaborative architecture. The method comprises the following steps: firstly, receiving begonia growth environment data collected by terminal equipment, and carrying out standardized preprocessing and multi-source data fusion analysis to obtain a growth feature entity candidate list and a non-feature data fragment; after a first to-be-modeled data entity is extracted from the candidate list, a plurality of matched candidate data entity entries are extracted from the cloud collaborative knowledge base, the candidate data entity entries and the non-feature data fragments are input into a collaborative disambiguation model, semantic-level collaborative aggregation analysis is carried out with the non-feature data fragments as contexts, semantic aggregation coding features are obtained and decoded, and the semantic aggregation coding features are obtained; and obtaining a disambiguation data entity. And circularly executing the steps to obtain a disambiguation data entity list, and further generating a remote management strategy. The method can improve data processing accuracy and management pertinence, and is suitable for large-scale and distributed planting scenes of begonia.

The invention discloses an artificial intelligence-based economic crop planting management method and system. The method comprises the steps of obtaining planting management original data, optimizing the original data, selecting target economic crops, predicting market prices of the economic crops, optimizing economic crop planting strategies and intelligently managing economic crop planting. The invention relates to the technical field of economic crop planting data processing, in particular to an artificial intelligence-based economic crop planting management method and system, which innovatively takes market price prediction as a guide and integrates a method system of target crop intelligent screening and full-cycle planting strategy optimization. The planting income stability of commercial crops is improved, price data signal decomposition processing is introduced, and the quality of commercial crop market price prediction input data is improved; a market price prediction model is innovatively designed, and the accuracy and stability of a model prediction result are improved; and an improved particle swarm optimization algorithm is introduced to realize efficient global optimization of the economic crop planting strategy combination.

The invention discloses an offshore wind turbine generator foundation scouring dynamic monitoring and power limit evaluation method, and aims to solve the problems that in the prior art, foundation scouring monitoring and protection measures lack quantitative evaluation, and power limit control is not real-time. Comprising the steps of data acquisition and real-time monitoring, scouring influence evaluation, fan power dynamic limit control, quantitative evaluation of fan protection measures and system integration and intelligent decision support. By monitoring the basic scouring condition, the fan state and environmental data in real time and combining numerical simulation and neural network optimization, the influence of scouring on the stability of the fan is dynamically evaluated, power output is automatically adjusted, safe and stable operation of the fan is ensured, quantitative evaluation and optimization of protective measures are achieved, the management precision and response speed of the wind power plant are improved, and the wind power plant management cost is reduced. The safety and the economical efficiency of the offshore wind turbine generator are improved; intelligent early warning and fault prediction can be integrated, prevention measures are taken before potential risks occur, non-planned downtime is shortened, and a comprehensive intelligent solution is provided for operation of the wind power plant.

The invention provides a palm oil plantation multi-modal decision management method, system and device and a medium, and belongs to the technical field of Internet of Things in the planting industry, and the method comprises the steps: building a heterogeneous federated learning framework for a palm oil plantation; the edge layer constructs a lightweight model, a planting industry heterogeneous terminal device is used for collecting data for local training, and asynchronous federal aggregation is performed in combination with the fog computing layer until requirements are met; the edge layer is used for extracting local features from data collected by heterogeneous terminal equipment in the planting industry; the cloud computing layer aligns local features of the edge layer by using a cross-modal space-time encoder, constructs a causal graph in combination with a Bayesian network, determines intervention measures, quantifies the influence of the intervention measures by using anti-factual reasoning, generates and executes a decision scheme, and solves layered Nash equilibrium through a distributed Actor-Critic algorithm, so as to improve the performance of the cloud computing layer. And task allocation and path planning are carried out on the planting industry heterogeneous terminal equipment. According to the invention, the utilization rate of planting management data, decision reasonability and equipment collaboration are improved.