3344 results about "Sustainable development" patented technology

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 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 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 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 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 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.

The invention relates to the technical field of the Internet of Things, and more specifically discloses an intelligent aquaculture management system and method based on the Internet of Things, which can provide a fine management scheme, optimize aquaculture conditions and improve yield and efficiency for farmers by monitoring aquaculture environment parameters in real time through a sensor and combining data analysis and artificial intelligence. The risk is reduced, and sustainable development of the aquaculture industry is promoted.

The invention belongs to the technical field of asphalt mixtures, and relates to a high-dosage RAP low-temperature hot mix plant recycled SMA-13 asphalt mixture and a preparation process thereof, and the low-temperature hot mix plant recycled SMA-13 asphalt mixture comprises the following components by weight: 50-60 parts of an asphalt mixture reclaimed material; 30-40 parts of modified steel slag aggregate; 3-5 parts of SBS modified asphalt; 0.02 to 0.06 part of a low-temperature asphalt modifier; 3-8 parts of superfine slag powder; 0.2 to 0.5 part of lignin fiber; and 1-2 parts of a self-made asphalt regenerant. The limitation of the traditional RAP doping amount is broken through, the highest doping amount reaches 70%, the steel slag recycled aggregate replaces natural aggregate, the consumption of raw materials and the emission of waste are remarkably reduced, and the steel slag recycled aggregate meets the target requirements of the new era of dual-carbon strategy and sustainable development; and the method is particularly suitable for winter construction, heavy traffic roads and green road construction in iron and steel industry areas, and has technical advancement and engineering practicability.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention discloses a Burkholderia gladioli (Burkholderia gladioli) CT-5 strain and an application of the Burkholderia gladioli CT-5 strain, and relates to the technical field of microorganisms. The strain is preserved in the China Center for Type Culture Collection, the preservation number is CCTCC NO: M20242732, and the preservation date is December 05, 2024. The Burkholderia gladioli (Burkholderia gladioli) CT-5 provided by the invention has a remarkable prevention and treatment effect (Plt, Plt) on idesia root rot caused by Fusarium solani (Fusarium solani), and the Burkholderia gladioli (Burkholderia gladioli) CT-5 has a remarkable prevention and treatment effect on idesia root rot caused by Fusarium solani (Busarium solani). The compound has the advantages that the compound can be used for inhibiting pathogenic bacteria of various plants, and the compound can be used for inhibiting pathogenic bacteria of various plants, such as Schizophyllum commune, Diaporthe erens, Colletotrichum acutatum and the like, and has a remarkable inhibiting effect on other various plant pathogenic bacteria such as the Schizophyllum commune, the Diaporthe erens, the Colletotrichum acutatum and the like. In a pot experiment, the biomass, soil nutrients, SOD, POD and CAT enzyme activities of idesia polycarpa seedlings in a CT-5 treatment group are remarkably improved compared with those of a pathogenic bacteria control group (FP) (Plt; 0.05), which indicates that the strain can promote plant growth and activate the activity of defensive enzyme in the body, so that effective control of idesia root rot is realized, and powerful theoretical basis and technical support are provided for sustainable development of idesia.

The invention provides a layered multi-agent-based full-process intelligent decision-making method and system for mineral development. The method comprises the following steps: constructing a three-level intelligent decision-making main body-based full-process layered collaborative decision-making architecture for mineral development; designing a communication protocol; and establishing a quantitative coupling relationship between the cross-link coupling constraint and the process parameters. And constructing a state space and an action space of the three-level intelligent decision-making main body. Designing a global objective function, a link objective function and a key parameter collaborative optimization model; and establishing an adaptive weight optimization mechanism. And designing a distributed collaborative optimization mechanism based on an alternating direction multiplier method. And implementing an agent collaborative decision-making process of the evolutionary game. The selected decision scheme is converted into a specific production instruction to be issued and executed, the execution effect and feedback data are monitored in real time, and the strategy is dynamically adjusted and optimized. According to the invention, the multi-objective collaborative optimization of the full life cycle of mineral development is realized, and the intelligent, green and sustainable development of mineral resource development is promoted.

The invention discloses high-performance fluid-state solidified soil for backfill based on composite ardealite of building residue soil, which is prepared by taking the building residue soil as a core raw material, taking a compound of a polycarboxylic acid water reducer and hydroxypropyl methyl cellulose as a fluidization regulator, and mixing the fluidization regulator and the building residue soil to prepare the high-performance fluid-state solidified soil for backfill. Superfine slag powder, fly ash, nano silicon dioxide, metakaolin and ardealite are used as curing agents, so that the fluid-state solidified soil with strong performance is obtained. Through the synergistic effect of all the components and innovative processes such as liquid nitrogen low-temperature grinding, nano material compounding and a segmented vacuum stirring process, the effect that the ultrahigh strength of the construction waste soil reaches 7.5 MPa within 14 days is achieved for the first time and is improved by 40% or above compared with that of conventional solidified soil, and the industrial problem that traditional waste soil is insufficient in solidification strength is solved; and collaborative optimization of strength and fluidity is realized. Meanwhile, efficient resource utilization of the construction waste is achieved, land occupation and environmental pollution caused by muck landfill are greatly reduced, and the requirement for green and sustainable development is met.

The invention relates to the technical field of urban planning, and particularly provides an intelligent site selection method and system based on a spatio-temporal data platform, and the method comprises the steps: collecting multi-source spatio-temporal data of key industry categories of a target region, constructing an enterprise demand feature set based on the multi-source spatio-temporal data, and building a quantitative demand standard based on the feature set; constructing an industrial ecology adaptation model based on the current industrial basic data of the target area, calculating the matching degree between the feature set and the industrial ecology of the target area through the industrial ecology adaptation model, and outputting candidate parks according to the matching degree by taking industrial ecology clustering as a target; establishing a multi-element adaptation relationship between the target enterprise and the candidate parks based on the quantitative demand standard, performing matching verification through a multi-dimensional compliance verification unit, and removing parks which do not meet the constraint condition of the quantitative demand standard; and based on the verification result, outputting a site selection scheme, finally realizing high matching of enterprise demands and public resources, improving site selection efficiency and quality, and assisting sustainable development of regional industries.

The invention relates to the technical field of underground structure management, in particular to an underground structure full life cycle management method and system based on fusion sensing data. According to the technical scheme, the system comprises a sensing layer, a data transmission layer, a data processing and analysis layer and a decision support and application layer. Through cooperation of the distributed optical fiber sensor and the multiple sensors, all-around and multi-parameter monitoring of the underground structure is achieved, the monitoring accuracy and reliability are improved, historical data and operation rules of the structure are automatically learned through a data driving prediction model, different geological environments and load conditions are adapted, a scientific basis is provided for maintenance decision making, and meanwhile, the system has good application prospects. The self-adjusting capability is realized; monitoring system application and data accumulation and utilization in each stage are considered, seamless connection and cooperative work are achieved, full-life-cycle integrated management is achieved, the overall performance of the structure is improved, the service life of the structure is prolonged, the maintenance cost is reduced, and sustainable development is promoted.

The invention belongs to the technical field of agronomy experiment devices, and particularly relates to a mulch-mulched soil environment simulation device which comprises a closed space, an air inlet and an air outlet. The mulching film sample is arranged in the closed space; the top of the soil simulation assembly is in contact with the bottom of the mulching film sample, and the soil simulation assembly is used for simulating a soil environment covered by the bottom of the mulching film sample; the natural environment simulation assembly is communicated with the closed space, the natural environment simulation assembly is used for simulating environmental conditions of the top of the mulching film sample, and the environmental conditions comprise temperature and humidity parameters, illumination parameters, rainfall parameters and wind speed parameters. According to the invention, researchers can accurately and controllably carry out experiments indoors, simulate soil states under different mulching film materials and environmental conditions, and deeply understand the influence of mulching film mulching on key parameters such as soil moisture, temperature and gas exchange. Scientific basis is provided for agricultural production and crop management, and sustainable development of agriculture is promoted.