3919 results about "Sustainable development" patented technology

The invention relates to the technical field of port ecological construction, and particularly discloses a port ecological construction evaluation method and system, and the method comprises the following steps: collecting dynamic ecological environment data in port construction operation through a multi-source sensor array; performing space-time coupling analysis on the data, calculating a dynamic attenuation rate # imgabs0 # of each dimension environment index by adopting a self-adaptive sliding time window algorithm, adjusting an ecological response time constant theta in combination with hydrodynamic conditions of a sea area around a port, performing synchronous integral calculation on a historical cumulative effect # imgabs1 #, and generating an environment health degree evolution curved surface; constructing a comprehensive evaluation model; and predicting an environmental risk value based on the hybrid prediction model. According to the invention, an intelligent, automatic, safe, reliable, green and low-carbon evaluation solution is provided for ecological construction of the port, and the ecological protection and sustainable development level of the port is remarkably improved.

The invention relates to the technical field of biomass and carbon sink collaboration, in particular to a tropical rainforest biomass and carbon sink collaborative optimization decision-making system based on deep learning. According to the technical scheme, the system comprises a multi-modal data acquisition system, a space-time coupling preprocessing module, an ecological coupling deep learning model, a carbon sink dynamic evaluation system, a multi-objective decision optimization engine and an intelligent interaction execution platform. Data are comprehensively and safely acquired by means of a multi-modal data acquisition system, the data quality is optimized through a space-time coupling preprocessing module, biomass and carbon sink are accurately analyzed by means of an ecological coupling deep learning model and a carbon sink dynamic evaluation system, a scientific decision is made through a multi-objective decision optimization engine, execution is guaranteed, and the method is suitable for large-scale popularization and application. Convenient interaction and efficient execution are realized by means of the intelligent interaction execution platform, so that the accuracy, scientificity and execution efficiency of tropical rainforest biomass and carbon sink collaborative optimization decision making are remarkably improved, and ecological protection and sustainable development of tropical rainforests are powerfully promoted.

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

The invention discloses a soil organic matter prediction mapping method, device and equipment based on deep learning and a medium, and relates to the field of digital soil mapping, and the method comprises the steps: obtaining a remote sensing image of target region soil, and setting a preset number of soil sampling points; comprehensively acquiring environmental data indexes such as terrain factors, vegetation factors, climate factors and land utilization factors; constructing three-dimensional environment tensor data in a preset range of each sampling point, and inputting the three-dimensional environment tensor data into the soil organic matter content prediction model to obtain an organic matter content prediction value set of the soil sampling points in the target area; and generating a spatial distribution prediction map of the soil organic matter content of the target area by combining the prediction value set and the spatial coordinates of the soil sampling points, thereby improving the accuracy of soil organic matter prediction and the mapping efficiency, and providing technical support for soil resource management and agricultural sustainable development.

The invention discloses an intelligent port scheduling and energy efficiency optimization method based on digital twinning. The method comprises the steps that S1, three-dimensional point cloud data and sensor data of a target port are acquired; s2, constructing a port digital twin model comprising a quay crane model, an AGV model and a storage yard model; s3, agents are constructed for a quay crane model, an AGV model and a storage yard model in the port digital twinborn model, and a multi-agent reinforcement learning environment is built; s4, training each agent in a multi-agent reinforcement learning environment through a multi-agent reinforcement learning algorithm; s5, mapping the real-time task data and state data of the port to each trained agent to simulate the scheduling of the target port, and generating a port scheduling strategy; and S6, realizing operation scheduling of the quay crane equipment, the AGV equipment and the storage yard equipment of the target port based on the port scheduling strategy. According to the invention, the operation efficiency and the energy efficiency level of the port can be improved, and powerful technical support is provided for sustainable development of the intelligent port.

The invention relates to the technical field of oil and gas field development, and discloses an oil and gas reservoir intelligent dynamic monitoring and optimized mining method based on big data. According to the method, through multi-source data real-time sensing and dynamic coupling modeling, the limitation of traditional static analysis is broken through, and minute-level accurate description of the state of the oil and gas reservoir is achieved; a machine learning prediction model based on physical constraints effectively fuses a data rule and a geomechanics principle, and the early warning reliability of water-driven dynamics and equipment faults is remarkably improved; a multi-objective optimization strategy of digital twin driving comprehensively plans the recovery ratio, the economy and the safety, and supports intelligent decision making under complex working conditions; the edge control and the block chain technology are combined to form a'sensing-optimizing-executing-auditing 'closed loop, so that the high efficiency, the self-adaptability and the data credibility of the mining process are ensured. According to the overall scheme, oil and gas exploitation is promoted to be transformed to intelligentization, refinement and transparency, and technical support is provided for cost reduction, efficiency improvement and sustainable development of an oil field.

The invention relates to the technical field of engineering construction management and control, in particular to an engineering construction management and control method and system based on digital twinning. The method comprises the following steps: acquiring real-time construction engineering data and regional environment data, identifying a real-time structured construction state and generating a three-dimensional simulation model, then acquiring a target construction engineering plan and performing process node positioning to calculate a real-time engineering progress deviation, performing compensation simulation based on the deviation, and determining an engineering construction development trend. And then environment constraint mapping and resource conflict analysis are performed to generate corresponding data, and an optimized construction path conforming to environment constraint and resource conditions is screened out through construction optimization path mining of the simulation model, so that the construction efficiency and the management level are improved. According to the invention, the competitiveness and sustainable development capability of the building industry are enhanced, and an efficient and transparent engineering management system is finally formed.

The invention provides a sewage treatment system and method based on a large language model and multi-agent collaboration, and the system comprises a sensor network which is used for collecting the water quality parameters of sewage in real time; the monitoring agent is used for performing data verification on the water quality parameters; the at least one edge computing node is used for performing data preprocessing on the water quality parameters; the cloud decision intelligent agent is used for generating a sewage treatment strategy according to the preprocessed water quality parameters based on a large language model; the execution agent is used for executing the sewage treatment strategy and treating the sewage; the evaluation agent is used for analyzing and evaluating the sewage treatment effect and feeding back the sewage treatment effect to the cloud decision agent so as to optimize the sewage treatment strategy; by constructing an intelligent and dynamic treatment system, real-time monitoring, intelligent analysis and self-adaptive treatment of the mariculture sewage are realized, so that the sewage treatment stability and environmental protection benefits are improved, and the sustainable development of the mariculture industry is promoted.

A fuzzy cloud-based risk assessment method for reservoir operation schemes, including: collecting basic data of target reservoirs; constructing optimal operation model of conventional reservoirs to obtain model calculation results; acquiring change process of operation characteristics during whole operating period according to model calculation results, and constructing multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to multi-level reservoir multi-dimensional risk assessment index system; obtaining large number of sample data xij of reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct risk assessment; calculating cloud digital features of different indexes by inverse cloud generator; and weighting indexes in index layer and criterion layer by Analytic Hierarchy Process to obtain multi-dimensional risk assessment results. In order to promote sustainable development and utilization of water resources and enhance level of reservoir risk management, scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

The invention discloses a water quality prediction method based on a Transform-LSTM fusion model, and belongs to the technical field of water quality time series data prediction and artificial intelligence. Comprising the following steps: (1) acquiring water quality data from a water quality monitoring station; (2) carrying out pretreatment; (3) screening out water quality characteristic data; (4) dividing into a training set, a verification set and a test set; (5) inputting the data into a Transform-LSTM (Long Short Term Memory) fusion model; (6) embedding water quality data time sequence information by a Transformer encoder through position coding, extracting a global dependency relationship among features by utilizing a multi-head attention mechanism, and optimizing gradient propagation by combining residual connection and layer normalization; (7) the LSTM layer receives the high-order features after Transform coding, and captures a local time sequence dynamic mode; and (8) mapping the extracted water quality time sequence characteristics to a specific prediction result by a regression output layer by adopting a linear activation function, and calculating an evaluation index. According to the method, the water quality change trend of the surface water body can be effectively predicted, and powerful support is provided for water ecological protection and sustainable development.

The invention relates to the technical field of intelligent breeding monitoring, in particular to a breeding early warning method and system based on artificial intelligence image recognition. The method comprises the following steps of: 1, dynamically acquiring and adaptively preprocessing a multi-modal image; step 2, constructing a multi-branch convolutional neural network based on the preprocessed image data, respectively extracting space apparent characteristics and time behavior characteristics of a target object, and dynamically fusing characteristic weights by combining real-time environment parameters to generate an abnormal probability value; and step 3, performing multi-level dynamic early warning decision and feedback optimization, dynamically generating a multi-level early warning threshold according to environmental parameters and historical abnormal probability value distribution, triggering differential response operation, and updating parameters of the multi-branch convolutional neural network and the multi-level early warning threshold based on an artificial recheck result and equipment feedback data. The method can automatically adjust the early warning mechanism according to the change of the real-time environment, thereby effectively preventing the potential risk in the breeding process, improving the breeding management efficiency, and guaranteeing the sustainable development of the breeding industry.

The invention relates to the technical field of ecological restoration prevention and control, in particular to a sea-entering river pollution source targeted blocking and ecological restoration integrated prevention and control system which comprises a pollution source recognition unit, a targeted blocking unit, an ecological restoration unit and a cooperative control platform. The recognition unit combines a multispectral unmanned aerial vehicle with a water quality sensor and positions a pollution source through artificial intelligence traceability; the targeted blocking unit implements physical-chemical combined blocking according to pollution types; the ecological restoration unit purifies water and reconstructs ecology through a modular constructed wetland and the like; the cooperative control platform optimizes each unit strategy based on hydrological data; each unit correlation formula accurately calculates parameters, such as a recognition algorithm, a gate dam opening control formula and the like. According to the invention, multi-technology fusion and system collaboration are realized, pollution events can be quickly responded, measures are accurately applied, the sea-entering river pollution prevention and control efficiency is effectively improved, the treatment cost is reduced, and drainage basin water environment ecological restoration and sustainable development are promoted.

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 water surface rate configuration, in particular to a method and device for determining a water surface rate configuration scheme. Obtaining regional meteorological data, hydrological data, water quality data and underlying surface factor data corresponding to the target water network region; inputting the regional meteorological data, the hydrological data, the water quality data and the underlying surface factor data into a preset water surface rate configuration scheme model, and outputting a target water surface rate configuration scheme corresponding to the target water network region; and configuring the water surface rate corresponding to the target water network area based on the target water surface rate configuration scheme. The scientific and reasonable water surface rate configuration scheme is beneficial to balance the supply-demand relationship of water resources and guarantee the economic development and ecological stability of the target water network area. By optimizing the configuration of the water surface rate, the current requirement for water resources can be met, the water resources and the ecological environment can be protected, a foundation is laid for long-term sustainable development of a region, and coordinated development of economy, society and environment is achieved. Reasonable water surface rate configuration of the target water network area is realized.

The invention relates to the technical field of agricultural information, discloses a corn precise topdressing prescription map generation method based on unmanned aerial vehicle multispectral remote sensing, and effectively solves the key problems of insufficient monitoring precision, poor environmental adaptability, low decision intelligent level and the like in the traditional fertilization technology. Through the unmanned aerial vehicle multispectral imaging technology, efficient and accurate monitoring of the corn nitrogen nutrition condition is achieved; in combination with a machine learning algorithm, an intelligent fertilization decision model adapted to different environmental conditions is established, and the fertilization precision in a complex farmland scene is remarkably improved; and a multi-source data fusion technology is adopted, and crop growth, soil characteristics and meteorological factors are comprehensively considered, so that the fertilization scheme is more scientific and reasonable. Compared with a traditional method, the method has the advantages that accurate variable fertilization can be realized according to actual needs of crops, the utilization rate of the fertilizer is increased, environmental pollution is reduced, and reliable technical support is provided for sustainable development of modern agriculture.

The invention relates to the technical field of power systems, and discloses an AI-based power grid investment analysis method and system, and the system comprises a data collection module, a data processing module, an intelligent prediction module, an optimization investment decision module, a risk assessment module, an intelligent scheduling and visualization module, and a real-time feedback module. Through deep learning, an optimization algorithm and an intelligent evaluation technology, the decision-making precision, flexibility and risk management capability of power grid investment are comprehensively improved, various investment scenes can be supported, the method has strong expansibility and adaptability, dynamic adjustment can be performed according to actual demands and changes of a power grid, and the method is suitable for large-scale popularization and application. And finally, intelligentization, precision and sustainable development of power grid investment are promoted.

The invention relates to the technical field of new energy power systems, and discloses a new energy consumption software model construction method based on a simulation platform, and the method comprises the steps: firstly dividing new energy consumption system units, determining the topological structure and operation parameters of the new energy consumption system units, and constructing a multi-physics field coupling simulation model; then, carrying out distributed parallel solution on the model, defining a global optimization target, constructing a joint optimization and network reconstruction model, respectively adopting a deep reinforcement learning algorithm and graph neural network optimization, and generating an optimization scheme through multi-agent collaboration; whether the optimization target converges is verified, if yes, control parameters are output, and if not, optimization variables are updated to continue optimization; and finally, the optimized parameters are imported into the digital twin platform calibration model integrated with the block chain technology, and hyper-parameters are adjusted. The method improves the accuracy of a new energy consumption system model, optimizes the system operation, enhances the cooperative stability, and promotes the efficient utilization of new energy and the sustainable development of a power system.

The invention discloses a big data driven dynamic resource scheduling method for an IPv6 edge computing node, and relates to the technical field of edge computing, and the scheduling method comprises the following specific steps: S100, resource data collection and integration: deploying monitoring equipment at the IPv6 edge computing node, collecting computing, network, storage and energy resource data, and transmitting the data to a server; through multi-dimensional resource data acquisition and storage, comprehensive monitoring and quantitative integration of calculation, network, storage and energy resource data are realized, the resource scheduling is more accurate and efficient through the comprehensive data acquisition mode, and through construction of the energy consumption performance model, the performance value can be accurately calculated, and the energy consumption performance is improved. And the model is trained and optimized by using big data, so that the balance of energy consumption and performance in the resource scheduling process is realized, the resource utilization efficiency is improved, the energy consumption cost is reduced, and powerful support is provided for green and sustainable development of edge computing nodes.

The invention relates to the technical field of carbon emission optimization, in particular to a logistics vehicle carbon emission optimization method, system and device based on multi-target cooperation and a storage medium. The invention discloses a logistics vehicle carbon emission optimization method based on multi-target cooperation, and the method reduces the load error through the real-time correction of multi-source data, and improves the time synchronization precision. Abnormal behaviors such as overload, high fuel consumption and false mileage are accurately identified by using an abnormal detection model; a multi-dimensional carbon effect feature model is constructed, the operation problem is effectively diagnosed, and the overload occurrence rate is reduced; an improved NSGA-II algorithm is adopted to solve the multi-objective optimization model, carbon emission per ton kilometer is reduced, and the daily average utilization rate of the vehicle is improved; a decision support mechanism is established, the scheme generation time is shortened, and the constraint violation rate is reduced. Therefore, a low-cost and quantifiable carbon efficiency improvement approach is provided for logistics enterprises in the aspects of environmental protection compliance and operation efficiency, and the enterprises are assisted to realize green and sustainable development.

The invention relates to the technical field of land utilization change evaluation, in particular to a multi-scene land gradient utilization carbon emission prediction method and system and a readable storage medium. A multi-scenario land gradient utilization carbon emission prediction coupling model under three scenarios of historical development trend, future climate change and policy planning effect is considered to comprehensively evaluate the influence of land gradient utilization change on carbon emission under different development paths. And a decision basis is provided for formulating scientific and reasonable regional space planning, a carbon emission reduction policy and a sustainable development strategy. The invention aims to solve the problem of how to improve the simulation accuracy of the carbon emission management regulation simulation system.

The invention discloses an agricultural and pastoral area water network regulation and storage project scale determination method and system. The method comprises the following steps: collecting historical data of an agricultural and pastoral area, dividing the agricultural and pastoral area into a plurality of secondary sub-areas, respectively calculating the water demand and available water consumption of each secondary sub-area, and identifying a water shortage time period and a water shortage area; constructing a regulation and storage project layout scheme set, and calculating the regulation and storage capacity and project parameters of each regulation and storage project; constructing a single-target optimization model, simulating a regulation and storage result of each regulation and storage project layout scheme, evaluating each regulation and storage project layout scheme, and screening out an optimal regulation and storage project layout scheme; and constructing an irrigation scheduling optimization model based on the optimal regulation and storage project layout scheme, solving the model to dynamically optimize and adjust the water storage time, the water discharge time and the water discharge amount of the regulation and storage project, and generating an agricultural and pastoral area water network regulation and storage project layout scheme. The water resource utilization efficiency of agricultural and pastoral areas is improved, the water resource supply and demand contradiction is relieved, and sustainable development of agriculture and animal husbandry and ecological environment improvement are promoted.

The invention relates to the field of artificial intelligence medical application, and discloses a lightweight intelligent traditional Chinese medicine inquiry system and a construction method thereof, and the system comprises a multi-dialect adaptive speech recognition module, a traditional Chinese medicine intelligent dialogue large language model module, a natural speech synthesis module, and a continuous learning mechanism module. The multi-dialect adaptive speech recognition module is used for converting dialect speech input of a patient into a standard text; the traditional Chinese medicine intelligent dialogue big language model module is the core of the system and is used for carrying out natural language understanding, dialectical reasoning and inquiry dialogue generation, and the natural speech synthesis module is used for converting a text response generated by the system into speech output; and the continuous learning mechanism module realizes continuous optimization of the large language model through incremental learning architecture and clinical feedback integration. According to the method, while the professional traditional Chinese medicine diagnosis capability is maintained, the calculation complexity is remarkably reduced, and the universality and sustainable development capability of system application are improved.

The invention relates to the technical field of control parameter optimization, in particular to a particle swarm optimization-based grinding parameter optimization method for a full-automatic coarse and fine grinding all-in-one machine. The method comprises the following steps: acquiring equipment operation multi-modal data, and performing multi-thread aggregation to obtain structured grinding field sensing data; performing grinding parameter semantic abstraction on the structured grinding field sensing data to obtain a grinding parameter intention nested mapping table; performing rough solution space boundary evolution on the grinding parameter intention nesting mapping table to obtain a coarse-grained grinding parameter track group; soft clustering biased learning is carried out on the coarse-grained grinding parameter trajectory group, and a high-dimensional grinding parameter candidate map is obtained; and parameter tensor loopback optimization is conducted according to the high-dimensional grinding parameter candidate atlas, and a stable grinding parameter set is obtained. According to the invention, the precision and the production efficiency of the equipment are improved, the energy consumption is reduced, and the abrasion and the maintenance cost of the equipment are reduced, so that higher benefits and sustainable development are realized in industrial production.

The invention discloses a road de-duplication method based on GPS positioning and comprehensive consideration of disease types and disease sizes, and the method comprises the specific steps: S1, data collection and processing: collecting road image data through an edge detection device, carrying out the road disease recognition of the road image data, obtaining disease data, and processing and storing the disease data; and S2, disease duplicate removal: matching the positioning information, the disease type and the size information of the processed disease data, if the positioning information, the disease type and the size information are all within a preset matching threshold, determining that the disease data are duplicated items, removing the duplicated items, and then storing the disease data after duplicate removal. According to the method, duplicate removal is performed by integrating multiple data dimensions, so that the accuracy of road disease data can be remarkably improved, and errors and redundant information are reduced; the intelligent and automatic level of road maintenance is improved, technical support is provided for sustainable development of road maintenance, and the intelligent road maintenance management system becomes an effective road maintenance management tool.

The invention discloses a production method of a high-performance sintered neodymium-iron-boron magnet. The production method comprises the steps of rapid quenching flaking, hydrogen decrepitation mixing, magnetic field forming, multi-stage treatment and composite coating. A nanocrystalline sheet is prepared through a rapid quenching technology, and uniform distribution of a grain boundary phase and rare earth elements is achieved through hydrogen decrepitation and material mixing; forming in a strong magnetic field and applying high pressure to improve the orientation degree; through high-temperature sintering, medium-temperature annealing and low-temperature aging treatment, magnet densification, grain boundary phase reconstruction and a magnetic domain structure are optimized; and finally, the corrosion resistance is improved by adopting a composite coating. The high-performance sintered neodymium-iron-boron magnet produced by the method comprises a main phase alloy, a grain boundary phase additive and a rare earth supplementary phase. Through low-oxygen nanocrystal preparation, grain boundary coordinated regulation and green process integration, compatibility of high performance, low cost and environmental protection is realized, and sustainable development of the rare earth permanent magnet material is promoted.

The invention provides an agricultural product production process intelligent supervision system and method based on big data analysis. The system comprises a data acquisition assembly, a data analysis assembly and a management planning assembly. The method comprises the following steps: deploying various sensors in a field for automatically collecting target data, and uploading the target data to a data analysis component in real time; analyzing the target data by using the collected target data through an agricultural product production data analysis algorithm to obtain the change trend of the soil nutrient content, and predicting when to apply fertilizer and the amount of the fertilizer; meanwhile, pest and disease damage is predicted, and a corresponding prevention and control scheme is provided; suggestions are provided for irrigation according to historical data and current environmental conditions; the analysis result is applied to an agricultural product planting area; the agricultural product planting area plans the planting layout of agricultural products by means of satellite remote sensing and a geographic information system. The agricultural production efficiency and economic benefits are improved, and powerful technical support is provided for agricultural sustainable development.

The invention relates to a marine ranching digital construction method and system based on artificial intelligence, and the method comprises the steps: monitoring plankton in a marine ranching in real time, obtaining a water body image through an image recognition technology, and building a dynamic database, so as to regularly store and update the related data of the plankton. Based on monitoring data, a water quality evaluation model is established, and the correlation among plankton species, density and water quality parameters is analyzed. Factors such as water flow velocity and flow direction are integrated, a flow change model is established, and the influence of water flow on plankton is judged. A primary culture optimization scheme is formulated by simulating the interaction between cultured organisms and planktons, and the culture trend is predicted in real time and the management strategy is adjusted by integrating the water quality evaluation and the output result of the flow change model. According to the scheme, the intelligent management level of the marine ranching is effectively improved, dynamic and scientific management of the culture environment is achieved, powerful support is provided for healthy growth of cultured organisms, and sustainable development of the marine ranching is promoted.

The invention provides a wetland ecological protection area biodiversity evaluation system, and relates to the technical field of ecological system diversity evaluation. The evaluation system comprises a data acquisition and management module, a species diversity analysis module, an ecological system function evaluation module, a threat factor identification and analysis module, a decision support and suggestion module and a data visualization and report module. According to the invention, the system is a platform integrating data collection, analysis, assessment and decision support, aims to comprehensively assess the biological diversity condition in the wetland ecological system and provide a scientific basis for sustainable management and protection of the wetland, not only can realize scientific and systematic management of wetland ecological protection, but also can provide a scientific basis for the sustainable management and protection of the wetland. The sustainable development of biological diversity protection can be promoted, the relationship between the biological diversity and ecological processes such as material circulation and energy flow in the wetland ecological system can be better understood through the evaluation system, and the ecological process of the wetland ecological system can be deeply understood.