10899 results about "Environmental resource management" patented technology

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention relates to the technical field of water photovoltaic flexible support pile foundation construction data processing, in particular to a process for a water photovoltaic flexible support pile foundation, which comprises the following steps: fusing meteorological, hydrological and pile deformation multi-source data through a dynamic weight distribution mechanism, and generating a space-time associated dynamic load spectrum by adopting wavelet packet transformation and entropy feature extraction; a collaborative analysis architecture of a finite element simulation and machine learning agent model is constructed, an anchoring depth mapping relation and a real-time design parameter adjustment instruction are output in parallel, and macroscopic fluid parameters and microscopic material degradation characteristics are associated through a data cascade interaction correction mechanism; full-life-cycle data version management is achieved through the block chain technology, and finite element model re-calibration and protection strategy dynamic updating are triggered. The dynamic load response analysis precision and long-term stability of the pile foundation are improved, and the deformation coordination requirement of the flexible support in the complex water area environment is met.

The invention relates to the technical field of agricultural meteorological disaster monitoring, in particular to a construction method of a composite agricultural meteorological disaster monitoring index system. The method comprises the following steps: acquiring regional agricultural component data, carrying out spatial distribution analysis to identify an agricultural distribution boundary, delimiting an agricultural overlapping region, then collecting historical meteorological disaster data, generating a meteorological disaster time-space sequence, identifying a correlation mode of meteorological and agricultural disasters, and determining the agricultural overlapping region according to the correlation mode of the meteorological and agricultural disasters. According to the agricultural overlapping area, composite agricultural boundary development data is predicted, available resources are identified, heterogeneous component interaction simulation is performed, an agricultural interaction effect field is constructed, meteorological disaster absorption capability is deduced, finally, composite agricultural disaster prediction is performed based on a historical association mode and the absorption capability, and a monitoring index system is constructed. And systematic monitoring and management of agrometeorological disasters are realized. According to the invention, systematic monitoring and evaluation of agrometeorological disasters are realized, and intelligence and scientization of agricultural management are promoted.

The present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

The invention relates to the technical field of intelligent water conservancy, and discloses a cross-regional water transfer project intelligent scheduling method and system, and the method comprises the steps: building a digital twin system based on a geographic information system, hydrological monitoring data and a spatial topological structure, and integrating a meteorological evolution prediction model, a basin hydrological response model and a water demand prediction model; predicting a water demand and an adjustable water amount by using a space-time convolutional neural network and a gating circulation unit; establishing a multi-objective optimization model taking water supply benefit, ecological influence and energy consumption cost as optimization objectives; an optimal water transfer scheme is generated through a Markov decision process and multi-agent cooperation; and carrying out robustness evaluation on the scheme and generating an emergency scheduling plan. According to the method, the scheduling efficiency and adaptability of the water transfer project are remarkably improved, and efficient configuration of water resources and quick response under extreme situations are achieved.

The invention discloses an electrical cabinet condensation defense method and system based on environmental parameter monitoring. The method comprises the following steps: acquiring temperature and humidity information of multiple points in an electrical cabinet and outside the cabinet in real time; calculating a dew point temperature and a condensation risk index in the cabinet; predicting the lowest temperature and humidity change rate in the cabinet in the next time period in the control period; based on the minimum temperature and humidity change rate prediction value in the cabinet and the current condensation risk index, whether the electrical cabinet meets the condensation risk trend criterion is judged, if yes, the electrical cabinet enters a condensation risk suppression linkage control mode, and if not, the electrical cabinet enters a condensation risk defense self-adaptive mode; and in the condensation risk suppression linkage control process, when the current condensation risk index of the electrical cabinet is lower than a condensation risk index threshold value, entering a condensation risk defense self-adaptive mode, and otherwise, entering a next control period. Data acquisition is comprehensive and accurate, the dew point temperature can be accurately calculated, the control strategy is intelligent and flexible, equipment can operate according to needs, condensation is effectively prevented, and the energy-saving effect is remarkable.

The invention discloses a water conservancy project construction monitoring data supervision system and method based on multi-source data fusion, and relates to the technical field of engineering construction. The multi-source data fused hydraulic engineering construction monitoring data supervision system and method comprises the following steps: S1, acquiring hydraulic engineering construction monitoring data, and preprocessing the hydraulic engineering construction monitoring data; s2, constructing a sliding window, extracting key structure and environment variables, comprehensively analyzing a structure disturbance degree, and dividing different disturbance state sections according to the structure disturbance degree; s3, an unstable state time period is extracted, a disturbance feature sequence is constructed, highly-suspected disturbance fragments are identified, response deviation between the structure and the environment is analyzed, highly-credible abnormal fragments are screened, and an abnormal data structure is generated; and S4, the coupling relation between the structural response and the environmental disturbance is analyzed in a combined mode, and a dynamic unbalance index used for measuring the unsteady state intensity is constructed. The problem that the abnormal recognition false alarm rate is high due to the fact that monitoring data are disturbed frequently in a complex construction scene is solved.

The embodiment of the invention provides a BIM-based hydraulic engineering digital management method and system. The method comprises the following steps: performing multi-dimensional monitoring system deployment on a to-be-monitored area to obtain multi-dimensional hydrological data; constructing a first BIM based on the topographic data of the to-be-monitored area, the real-time work area image, the hydraulic engineering construction information of the design stage and the equipment deployment information; fusing the multi-dimensional hydrological data, the water area change data, the construction progress data and the operation and maintenance monitoring data into the first BIM, and constructing a second BIM including a full life cycle; and through the equipment characteristic curve and the water conservancy project physical model, in combination with the multi-dimensional hydrological data and the water area change data, predicting a water conservancy project structure change trend and potential risk factors in the second BIM, fusing the water conservancy project structure change trend and the potential risk factors into the second BIM, and displaying an implementation effect and improvement suggestions in real time in the second BIM. A user is assisted to realize hydraulic engineering digital management, and the engineering management efficiency is improved.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention relates to the technical field of water and fertilizer integration, and provides an intelligent water and fertilizer integration optimization method and device based on the Internet of Things technology, and the method comprises the following three core stages and interaction mechanisms: 1, a data perception and edge processing stage; 2, an intelligent decision-making and dynamic optimization stage; and step 3, an accurate execution and closed-loop control stage. Soil parameters, meteorological parameters and crop physiological parameters are comprehensively collected through a multi-modal sensor network, the data transmission efficiency is improved by using an LoRa / NB-IoT hybrid transmission protocol, data cleaning and standardization processing are completed in combination with edge computing nodes, and the data fusion depth and quality are improved; a multi-objective optimization function is constructed based on a crop growth model, a water and fertilizer regulation and control strategy is dynamically generated through a machine learning algorithm, and collaborative optimization of water saving, yield increasing and environmental protection is achieved.

The invention relates to the technical field of remote sensing image recognition, in particular to a remote sensing sewage area recognition method and system based on a graph structure and multi-stage enhancement. The method comprises the steps of performing data preprocessing and representation enhancement on an acquired remote sensing image; performing sewage salient region preliminary screening on the enhanced remote sensing image, including abnormal enhancement mapping construction based on local statistical distribution; pollution candidate graph extraction based on spatial structure prior driving; enhancing the response of the stable region based on a structure consistency enhancing mechanism of the polluted region; high-precision segmentation and identification of the sewage area comprises the following steps: constructing a multi-resolution residual pyramid structure; carrying out fine-grained boundary structure modeling and uncertainty suppression; generating a sewage distribution probability graph and optimizing structural consistency; according to the method, the multi-resolution residual pyramid structure is constructed, image context information under different perception scales is fully mined, and the sensitivity and edge integrity of the model to a sewage area under a complex texture background are remarkably enhanced.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention discloses a wetland ecological restoration dynamic monitoring method based on deep learning, and relates to the technical field of ecological restoration, and the method comprises the following steps: obtaining multi-source wetland ecological sensor data and remote sensing image flow in real time, constructing a space-time fusion data cube, and extracting an ecological feature tensor; performing degradation mode analysis on the ecological characteristic tensor, generating an ecological state dynamic topological graph, and calculating an ecological connectivity index; carrying out restoration demand identification based on the ecological connectivity index, positioning a degradation hot spot region through a multi-modal graph convolutional network, and generating a restoration priority region coordinate set; through multi-source data space-time fusion and deep crossing of deep learning and landscape ecology, a whole-process technical system from ecological state dynamic perception to restoration scheme intelligent optimization is constructed. The problems that in traditional wetland restoration, data scales are not matched, degradation area positioning is fuzzy, restoration path ecological adaptability is poor, and multi-target cooperation is difficult are effectively solved.

The invention discloses a smart tea garden resource dynamic scheduling system and method integrated with environment monitoring, and the method comprises the following steps: S1, collecting environment and operation data, processing the environment and operation data, and generating a standardized data set; s2, carrying out discrete modeling and environment field generation, carrying out topological analysis on humidity and pest and disease damage intensity, judging abnormity and triggering local scheduling; s3, generating a task, resource and environment set, constructing a task-resource-environment hypergraph, establishing a time expansion graph and forming constraints; s4, dynamic game solving is introduced, and a scheduling scheme and a control instruction are generated; s5, issuing an instruction to an execution end, collecting an execution state and environment data, and forming a feedback data set; and S6, constructing a double-layer memory scheduling unit, and generating a parameter set of a next period. According to the invention, precise allocation and efficient management and control of tea garden resources are realized by fusing environmental monitoring and dynamic scheduling, so that the production efficiency is improved and ecological sustainability is guaranteed.

The invention provides a highway tunnel intelligent illumination control method and system, and the method comprises the steps: collecting a real-time perception data set of a plurality of monitoring nodes in a target tunnel, covering a light source operation parameter, an illumination intensity parameter and an environment state parameter sequence, carrying out the feature extraction of the real-time perception data set, and carrying out the feature extraction of the real-time perception data set; generating a global feature set including light source operation stability, regional illumination coordination and environmental coupling influence features, and then inputting the global feature set into a preset dynamic decision model for strategy decision to obtain a multi-stage illumination regulation and control strategy set covering different regions, including target brightness, dimming time sequence and equipment cooperation parameters; and finally, generating an executable instruction set according to the multi-stage illumination regulation strategy set, issuing the executable instruction set to each partition illumination control terminal, and triggering adaptive dimming of the illumination equipment, thereby realizing intelligent management and control of tunnel illumination, improving an energy-saving effect and illumination quality, and ensuring driving safety.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to a supply and demand multi-source data fusion farmland water-saving irrigation water system design and dynamic water distribution optimization method, which is characterized by comprising the following steps: S1, crop-soil-weather multi-source data coupling modeling, S2, water-saving water system design optimization, S3, dynamic water-saving water distribution strategy, and S4, intelligent terminal control system control. According to the scheme, accurate configuration of water resources is achieved through the artificial intelligence technology, the irrigation water consumption is reduced to the maximum extent, and meanwhile the crop growth requirement is met.

The invention provides a mangrove forest restoration area carbon sink quantity data monitoring system, and relates to the technical field of ecological carbon sink metering, and the system comprises an acquisition module which is used for collecting a carbon exchange original data set of an atmosphere-vegetation-soil interface based on a multi-type sensor network arranged in an intertidal wetland of a mangrove forest restoration area; the data generation module is used for carrying out space-time coordinate matching based on the original data set and generating a multi-dimensional carbon flux fusion data set of geographic position and time joint indexing; and the identification module is used for calculating a local space-time dispersion and a global correlation threshold value based on the fused data set, and identifying and marking abnormal data points. According to the invention, real-time and multi-dimensional monitoring of the carbon sink amount of the mangrove forest restoration area is realized, the monitoring efficiency and the data reliability are improved, and support is provided for assessment of restoration and management strategies.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention relates to the technical field of farmland nutrient management, and discloses an organic fertilizer application method and system for dynamic management of farmland nutrients, and the method comprises the steps: obtaining and standardizing original data of a farmland environment, extracting features to generate farmland feature vectors, and completing nutrient demand recognition and fertilization type analysis; integrating the data to generate an enhanced farmland matrix, performing multi-dimensional nutrient analysis, calculating application necessity and an environmental influence value, and generating decision data after verification; constructing an enhanced fertilizer feature space based on an organic fertilizer library and the decision data, completing fertilizer matching, proportioning and application path optimization, and outputting pre-inspection report data; and collecting application data to carry out anomaly detection and adaptive learning. The system comprises a processor and a memory, and instructions stored in the memory can implement the method. According to the invention, through multi-dimensional data integration and intelligent decision making, the precision and scientificity of farmland nutrient management are improved, and the system is suitable for precise fertilization requirements of modern agriculture.

The invention relates to the technical field of hydrological process simulation, in particular to an ecological hydrological process simulation method and system based on dynamic vegetation and a medium. The method comprises the following steps: acquiring a vegetation physiological monitoring parameter set and a root system water absorption dynamic data set; performing time-space unified registration on the vegetation physiological monitoring parameter set and the root system water absorption dynamic data set to obtain a vegetation physiological dynamic parameter set; performing soil dry and wet process division on the target area based on the vegetation physiological dynamic parameter set to obtain a segmented soil moisture state sequence; dividing the vegetation physiological dynamic parameter set according to the segmented soil moisture state sequence to obtain a state classification vegetation physiological data set; and constructing a vegetation water stress adaptation strategy model based on the state classification vegetation physiological data set. According to the method, the simulation precision of vegetation coverage and soil moisture dynamic balance is improved, and the problem of simulation errors caused by vegetation parameter static processing in the prior art is solved.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.