40338results about "Climate change adaptation" patented technology

The invention relates to a disaster multi-source situation intelligent sensing management and control method and system. According to the method, hydrometeorological and topographic data are collected, and a standardized data set is generated through space-time alignment and anomaly cleaning; constructing a directed topological graph containing node and edge attributes based on the extracted river network topological relation; designing a neural network model, and training through a physical constraint loss function embedded in a water balance principle to obtain a flood dynamic routing prediction model; inputting real-time hydrological data into the model for graph convolution operation, and predicting water level, flow and split ratio changes of each node in a future time period; and finally, carrying out submerging simulation analysis in combination with a digital elevation model, and generating a flood control scheduling scheme and risk early warning information. The deep fusion of a physical mechanism and data driving is realized, the flood propagation rule under the river network topology constraint is effectively captured by using the graph neural network, the calculation efficiency is remarkably improved while the prediction precision is ensured, and real-time and reliable decision support is provided for flood disaster prevention and control in a complex river network region.

The invention discloses a cascade reservoir collaborative flood control scheduling decision-making method of a coupling meteorological-hydrological-hydraulic model. The method comprises the steps of multi-source meteorological data fusion and probability forecast generation, dynamic coupling hydrological-hydraulic simulation, risk entropy driven collaborative optimization decision-making, digital twin platform verification and correction, instruction execution and closed loop feedback. Through multi-technology fusion and an intelligent optimization mechanism, the scientificity, timeliness and safety of flood control scheduling are remarkably improved. In the weather forecast stage, multi-source data are integrated to output ensemble rainfall forecast scene data, rainfall input uncertainty is reduced from the source, it is ensured that initial driving precision of flood simulation is improved, and a reliable input basis is provided for subsequent model coupling; in the hydrological-hydraulic dynamic coupling stage, a coarse-fine grid dynamic division strategy is adopted to reduce redundancy calculation, the asynchronous pipeline technology enables the flood routing calculation efficiency to meet the real-time scheduling requirement, and the simulation speed is greatly accelerated.

The invention provides a control node locating protecting method of undercutting channel segment water level lowering amplitude. The method comprises the steps that a lateral erosion zone and a deep erosion zone are divided; a target channel section is subjected to meshing, a plane two-dimension water flow mathematic model is established, parameter calibration and verification are carried out; a protection project is generalized to the plane two-dimension water flow mathematic model, the arranging zone and the width of the protecting project are determined; local gradient amplification and upstream water level amplification corresponding to each working condition are obtained, mesh sequences are analyzed respectively; key nodes or other nodes which have obvious influence are selected, for lateral erosion nodes, a hidden closure dam and a beach protection part are arranged on a river branch or a bottomland, and for deep erosion nodes, a bottom protecting band or a hidden closure dam are placed in a main groove. According to the method, nodes are divided into the lateral erosion nodes and the deep erosion nodes, protection part difference of the nodes in different types is provided, by comparing the efficiency of a local channel segment arranging protecting project, the positions of the nodes are accurately located, and the optimum efficiency of the protecting project is guaranteed powerfully.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention discloses an urban flood toughness evaluation method, apparatus and device, a medium and a computer program product. The method comprises the steps of obtaining hydro meteorological data of a to-be-evaluated region and geographic information data of an urban spatial form element layer; setting boundary conditions of a plurality of flood scenes according to the hydrometeorological data, constructing a multi-scene urban flood disaster simulation model in combination with geographic information data, and respectively generating flood inundation depth data of urban spatial form element layers of the plurality of flood scenes; and calculating a first toughness index, a second toughness index and a third toughness index of each flood scene so as to calculate a flood toughness comprehensive evaluation result of each flood scene of the to-be-evaluated region. According to the method, an evaluation system of multiple composite flood disaster scenes is established, different disaster factors and interaction thereof are comprehensively considered, and the effect of key space parameters in urban flood toughness is fully considered, so that the capability of coping with flood disasters in cities can be more comprehensively and accurately evaluated.

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 relates to the technical field of cables, and discloses a multi-layer composite wrapped high-temperature-resistant anti-aging cable which sequentially comprises a conductor, an insulating cable core, a shielding layer, an outer insulating layer, a fireproof layer, a heat insulation layer, a buffer cushion layer, an armor layer, an anti-aging layer and an outer sheath layer from inside to outside. The fireproof layer is formed by alternately laminating synthetic mica tapes and ceramic boron silicone rubber tapes, a reliable fireproof barrier is formed, and the heat insulation layer is arranged to achieve efficient heat insulation and stable structure through cooperation of gradient pore nano aerogel, ceramic silicone rubber round rods and phase change capsules. The buffering cushion layer is combined with the composite layer, the aluminum oxide hollow ball array and the composite capsule, so that the mechanical buffering and auxiliary heat insulation functions are achieved; the armor layer is woven by stainless steel fibers, the mechanical protection capability is improved, and the anti-aging layer is of a double-layer structure composed of montmorillonite composite fluororubber and an anti-oxidation layer, so that thermal oxidation aging is delayed.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The invention relates to a quick river flood forecasting method based on a physical information neural network, and belongs to the field of river flood forecasting. The method comprises the following steps: on the basis of a traditional physical information neural network (PINN), introducing a boundary condition parameter as an input variable, and enabling the PINN to learn a flood wave propagation rule under different boundary conditions. Furthermore, on this basis, a physical information neural network flood fast forecasting framework (RFF-PINN) integrated with a hydrodynamic method is provided, a numerical solution based on grid discretization is reconstructed into a continuous function in a time-space domain through a piecewise polynomial interpolation method, residual error loss between network output and a hydrodynamic model simulation value is constructed, and therefore, a flood fast forecasting result is obtained. The network parameters are optimized in cooperation with the PDE loss, and the problem that the network parameter optimization effect is reduced due to the fact that the PDE loss of the complex flow state area is difficult to converge is solved. The method has the beneficial effect that the water depth change process of each section of the river channel under any boundary condition can be accurately and quickly predicted.

The invention relates to a multi-source data fusion-based urban physical examination evaluation index calculation method and system. The method comprises the steps of extracting a multi-source data sequence; identifying a data source of the urban physical examination index set, and extracting an independent time sequence data sequence; calculating the information entropy of the independent time sequence data sequence, and distributing a basic fusion weight; calculating a dynamic state evaluation value of the independent time sequence data sequence, and performing weighted fusion on the basic fusion weight and the dynamic state evaluation value to obtain a comprehensive state evaluation value; obtaining a distribution variance of the basic fusion weight, inputting the distribution variance into the uncertainty quantification model, and obtaining an index calculation result containing uncertainty measurement; the real-time performance of the evaluation result is enhanced through an aging attenuation mechanism, and the latest state of the city system is accurately reflected; the output uncertainty measurement index provides a quantitative basis of result credibility for a decision maker, and the decision risk caused by a data fusion error is reduced.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

Provided in the present invention are a creation method and apparatus for a tea leaf disease recognition model, and a device and a storage medium. The creation method comprises: acquiring a plurality of images of tea leaves, and disease and insect pest information corresponding to each image; generating a tea leaf disease recognition model, wherein the tea leaf disease recognition model includes a MobileNetV3 model, an ultra-lightweight attention model and a fully connected layer; and on the basis of the plurality of images of tea leaves and the disease and insect pest information corresponding to each image, training the tea leaf disease recognition model. The tea leaf disease recognition model can automatically recognize tea leaf diseases and insect pests.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention discloses a multi-disaster disaster risk grade coupling evaluation method. The method comprises the following steps: establishing a multi-dimensional database covering multiple disaster types; key features related to the disaster risk are extracted from the multi-dimensional database; establishing a risk assessment model according to the key features, and determining a plurality of single-disaster risk levels; identifying association rules, causal relationships or space-time coupling modes among different disasters by using a big data mining algorithm; coupling weights among different disasters are determined, and a coupling model is constructed to convert a coupling relation into a quantitative risk superposition effect; based on the coupling model, a single-disaster risk and a coupling effect are integrated, a multi-disaster comprehensive risk level is generated, through multi-dimensional data integration, quantitative coupling modeling and a dynamic adjustment mechanism, advantages are formed in evaluation precision, social and economic adaptability and model adaptability, and the method is more suitable for scenes needing accurate risk grading and decision support.

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention discloses a gravity type submerging net cage parameter design optimization method, and relates to the field of mariculture equipment structure optimization, and the method comprises the steps: collecting the multi-period marine environment data of a target sea area, and constructing an environment load sequence with a time-varying characteristic; establishing a three-dimensional coupling finite element model, performing disturbance analysis on a plurality of structure control parameters, and identifying a key design parameter set based on a response sensitivity function; and constructing a multi-objective optimization model including floating and sinking time control, maximum stress minimization and attitude offset constraint, performing global optimization by adopting an adaptive genetic algorithm, obtaining an optimal parameter solution, substituting the optimal parameter solution into the simulation model for dynamic response verification, and if a response index is converged to a target interval, outputting the optimal parameter solution as final design configuration. According to the method, systematic optimization of structural parameters of the submerging and surfacing net cage can be achieved, the stability and safety of the submerging and surfacing net cage under complex sea conditions are improved, and the method has high engineering applicability.

The invention discloses a device and a method for intelligently investigating types and quantity of fishes. The device comprises a self-adaptive sonar detection module, a three-dimensional image acquisition module, an image preprocessing module, a fish detection module, a main body segmentation module and an identification and classification module. The sonar module is integrated with a three-frequency-band transducer, can dynamically switch frequency according to fish school depth, and realizes target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module constructs a fish school three-dimensional image model by combining a depth camera with a light attenuation compensation algorithm; the preprocessing module fuses acoustic and optical features and expands a data set; the detection and segmentation module is used for accurately positioning fishes and removing impurities; the identification and classification module realizes type identification based on transfer learning and supports incremental learning of new fingerlings. According to the method, through cooperation of multiple modules, the problems of insufficient detection precision, poor image quality and the like in traditional investigation are solved, efficient and accurate investigation of fish species and quantity is realized, and technical support is provided for fishery resource management and ecological protection.

The invention relates to the technical field of rock monitoring, in particular to a safety monitoring method and system for a rock slope, which is characterized in that an abnormal focusing area is generated through gradient change and density sudden change judgment, a basic entrance for early risk identification is constructed, the positioning accuracy of a crack sensitive section is enhanced, and the safety of the crack sensitive section is improved. The identification precision and the dynamic intervention capability of the potential risk area of the rock slope are enhanced, the linear features of the boundary response are extracted through the convolutional neural network, the identification and the reconstruction of the crack main extension path are realized, and the method combines the direction sorting and the structure continuity analysis to improve the reliability of the crack main extension path. Closed fitting and stable connection node labeling of a crack path are completed, a traceable structural framework is provided for subsequent trend analysis, dynamic modeling of crack migration evolution is achieved through comparison recognition of a displacement direction and an angle abrupt change point, a closed path grid can be constructed in a crack direction reversal area, and the dynamic modeling of crack migration evolution is achieved. And the dynamic response layout of the crack control structure is realized.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention discloses an aquaculture environment intelligent regulation and control method and system based on an AI model, and relates to the technical field of aquaculture management and control. The AI model-based aquaculture environment intelligent regulation and control method comprises the steps of S1, collecting water quality state data, feeding behavior data and meteorological data in an aquaculture environment, preprocessing and storing the data, and then constructing an aquaculture regulation and control management database; s2, constructing a water body state time sequence causal model based on the multi-source time sequence data, and outputting a causal connection structure; s3, performing prediction and risk assessment on water quality evolution under different feeding schemes through multivariable time series and causal coupling; s4, analyzing a feeding scheme target based on the feeding decision amount, the water quality risk and the growth income; and S5, continuously correcting the causal relationship and the feeding strategy through feeding execution feedback. The problems that the bidirectional coupling relation between the feeding behavior and the water quality feedback is not clear, and the feeding decision and the water quality risk are difficult to cooperatively regulate and control are solved.

The invention relates to a three-dimensional live-action model generation method for city updating. The method comprises the following steps: collecting multi-source city image information, and obtaining semantic data according to the multi-source city image information; performing city modeling visualization according to the multi-source city image information to generate a city model; and matching the semantic data with the city model to obtain a three-dimensional real scene model containing the semantic data. According to the method, city information can be obtained more comprehensively by performing total factor accurate collection through a multi-source three-dimensional monitoring technology, the accuracy of a real scene model is improved, semantic data is matched with a city model, a three-dimensional real scene model containing the semantic data is obtained, multiple materials and textures of the model are combined, and the accuracy of the real scene model is improved. And the model is lightened, so that the purpose of reducing the resource loading time is achieved.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention relates to the field of gene engineering for enhancing the cold resistance of plants, and relates to a recombinant expression vector, a recombinant strain and a primer pair of a pennisetum purpureum PpMYB2 gene, a method for improving the cold resistance of the plants and application of related materials in improving the cold resistance of the plants. One purpose of the invention is to provide application of the Pennisetum purpureum PpMYB2 gene and related materials thereof in improving the cold resistance of the plant, the amino acid sequence of the encoding protein of the Pennisetum purpureum PpMYB2 gene is as shown in SEQ ID NO.1, and the plant is arabidopsis thaliana or Pennisetum purpureum. Under the background that extreme low temperature events caused by global climate change are increasingly frequent, the cold resistance enhancing technology provided by the invention has important significance on guaranteeing the stability of agricultural production and reducing loss caused by low temperature. Through overexpression of the gene PpMYB2, the adaptability of perennial pennisetum purpureum to low-temperature stress can be remarkably improved, so that the planting range of perennial pennisetum purpureum is expanded to regions with lower temperature, and the survival and growth performance in different ecological environments is enhanced.

The invention provides an urban subway network toughness pre-disaster and post-disaster dual-stage optimization method under flood disasters. The method comprises the following steps: acquiring urban subway network information, road network information and flood disaster information; constructing an urban subway topology network and a road topology network; simulating flood depth space-time change in the subway and road network during the duration of the flood disaster, and determining a subway network failure station set based on the accumulated water depth; calculating flood control resource demand quantity based on the platform number, the platform type and the flood depth of each station in the failure station set; establishing and solving a pre-disaster-post-disaster dual-stage optimization model of urban subway network toughness to obtain an optimal pre-disaster site closing decision, an optimal flood control resource warehouse site selection decision and an optimal post-disaster restoration decision; wherein the double-stage optimization model is a double-layer planning model; and the upper-layer model and the lower-layer model are mutually coupled and optimized through a pre-disaster closing site set and a post-disaster repairing process.

The invention discloses a fishery pond water temperature control optimization method based on a swarm intelligence algorithm, and belongs to the technical field of control optimization, and the method comprises the following steps: S1, constructing a fishery pond water temperature PID control system introducing an improved single-corner whale optimization algorithm module; s2, introducing an improved single-corner whale optimization algorithm, and constructing an improved single-corner whale optimization algorithm module; s3, setting and optimizing the pond water temperature PID control parameters by using an improved single-corner whale optimization algorithm to obtain optimal control parameters; and S4, setting an optimal control parameter obtained through optimization by using the improved single-corner whale optimization algorithm as a parameter of a pool water temperature PID controller, and optimizing a pool water temperature regulation control effect. According to the method, a swarm intelligent optimization algorithm, namely an improved unicorn whale optimization algorithm, is introduced, high-performance control over the pond water temperature is achieved, and reliable control technical support is provided for efficient, stable and safe operation of aquaculture.

The invention discloses an oil reservoir comprehensive production optimization method and device, and the method comprises the steps: carrying out the region level recognition, single well level recognition and production optimization model recognition according to the production historical data, and updating the oil reservoir dynamic recognition; based on production historical data, performing cyclic historical fitting on model parameters of the various production optimization models by adopting the following steps: performing historical fitting on the model parameters, and performing inter-well communication relation calibration on the various production optimization models; determining a constraint condition corresponding to each production optimization model according to the updated reservoir dynamic cognition, solving a target function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; an oil reservoir comprehensive development regulation and control scheme is obtained; and according to the regulation and control effect obtained through real-time monitoring of the development operation, oil reservoir dynamic understanding is updated. According to the invention, the efficiency and precision of oil reservoir production optimization can be improved.