397 results about "Land utilization" patented technology

The invention discloses an agricultural land utilization monitoring management system and method based on remote sensing, and belongs to the technical field of remote sensing image processing. Multi-temporal remote sensing images are acquired, and a land surface energy fluctuation spectrogram is constructed; extracting disturbance characteristics through small-scale grid slices to form a multi-dimensional disturbance characteristic tensor; identifying an abnormal evolution region by using a sparse volume accumulation algorithm, and outputting a preliminary screening identification graph; inputting the region with the continuous evolution characteristic into a time sequence attention mechanism inversion network, estimating a crop growth state trajectory, matching an agricultural planting mode library, and generating a candidate land utilization behavior probability distribution diagram; in combination with regional consistency optimization, a historical planting period and meteorological disturbance data, calculating a purpose change confidence score, and outputting early warning information and a monitoring report; the agricultural land dynamic change identification precision and management capability can be effectively improved.

The invention relates to the technical field of agricultural data analysis, in particular to a land reclamation suitability evaluation and cultivated land quality improvement method based on big data, which comprises the following steps of: acquiring and superposing land utilization, soil and elevation data to construct a multi-source land attribute evaluation data set, calculating the distance from a pattern spot to a water source, reconstructing a weight through Gaussian attenuation, and calculating the land utilization, soil and elevation data; the method comprises the following steps: forming a spatial distance weighted feature matrix, extracting a gradient and an irrigation guarantee rate, performing weight reduction on an overrun part of a conflict threshold value, performing linear weighting calculation on a comprehensive score after normalization, dividing a regulation priority area, matching quality improvement measures, and forming a cultivated land quality improvement project layout map. A pattern spot level evaluation basis is formed through multi-source attribute space superposition, water source distance attenuation calculation is introduced to enable weight to change along with reachability, irrigation difference response is enhanced, conflict constraint correction is carried out on gradient and irrigation guarantee, adverse combination influence is inhibited, stable sorting is formed through uniform scale weighting, and the method is applied to the field of irrigation. And improvement priority area identification and quality improvement measure directional configuration are supported.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention discloses an intelligent redevelopment research method for low-efficiency land use, which comprises the following steps of: acquiring multi-source data including land change survey data, topographic data, urban planning data, remote sensing data, point-of-interest data, mobile signaling data and social economic data in a research area, and inputting the multi-source data into a land use identification model; obtaining a spatial distribution diagram of low-efficiency land in the research area and corresponding low-efficiency factors; inputting the spatial distribution diagram and the low-efficiency factors into a redevelopment potential calculation model to obtain a redevelopment comprehensive potential value of the low-efficiency land; a plurality of redevelopment schemes are obtained based on the redevelopment comprehensive potential value, the rigid constraint of the low-efficiency land during redevelopment and the redevelopment mode library; and obtaining respective corresponding benefit index difference values after the low-efficiency land is redeveloped by using the plurality of redevelopment schemes, and obtaining an optimal redevelopment scheme from the plurality of redevelopment schemes by using a multi-attribute decision algorithm and the benefit index difference values.

The invention belongs to the technical field of inter-basin underground water circulation flux estimation, and particularly relates to an infiltration and lateral inflow separated underground water flux remote sensing estimation method and system. The method includes: acquiring a multi-source remote sensing data set of a research area; performing partition correction on evapotranspiration data of different land utilization type areas in the research area; the method comprises the following steps: dividing a research region into equal-area units, identifying a vertical dominant unit and a lateral dominant unit, and further determining a cross-basin underground water exchange zone; and solving a local vertical infiltration amount by using the vertical dominant unit and interpolating to form a background field, establishing a combined equation set which takes the background field as an initial value and takes the dynamic characteristic parameters of the underground water as additional constraints for the lateral dominant unit, solving to obtain an external lateral inflow amount, and further obtaining a cross-basin underground water circulation flux. According to the method, the vertical dominant unit and the lateral dominant unit are identified, and the cross-basin groundwater circulation flux is quantitatively estimated.

The invention discloses a satellite hyperspectral remote sensing image-based adaptive random forest land utilization classification and identification method, which comprises the following steps of: data preprocessing: loading a vector boundary of any predetermined administrative region, the method comprises the following steps: acquiring surface reflectance data of a certain satellite hyperspectral image in any preset time period from a natural resource satellite remote sensing cloud service platform, and preprocessing; making a sample set; parameter definition and remote sensing index selection: calculating a group of representative remote sensing indexes by using a specific wave band combination of the hyperspectral image; a remote sensing index and an original wave band are used as input feature vectors together to form a complete feature space, and in the training process of the random forest, part of features are randomly extracted from the feature space during construction of each decision tree for node division; performing cross validation and parameter tuning; the visualization of the optimal parameter result is selected; according to the invention, adaptive parameter tuning based on random forest algorithm classification is realized.

The invention belongs to ecological environment engineering, and particularly relates to a river and lake wetland zoning and classifying ecological water demand fine accounting method, which comprises the following steps of S1, performing zoning and classifying on a river and lake wetland of a research area; s2, establishing a space-time coupling hydrodynamic model based on a partition classification relationship; s3, constructing a multi-factor ecological response model, and calculating an ecological suitability index of each partition; s4, establishing a multi-objective optimization model, and performing optimization solution on the ecological water demand of the partition by combining ecological benefits, water balance and process smoothness objectives; and S5, performing robust correction on an optimization result by adopting an uncertainty analysis method to obtain a final ecological water demand distribution result. By establishing a river and lake wetland partition classification spatial database based on digital elevation, river network distribution, land utilization and ecological attribute information, fine description and dynamic association of different ecological functional areas are realized, and the effects of ecological process differentiation characterization and remarkable improvement of water demand accounting precision are obtained.

The invention relates to a land investigation, analysis and comparison method and system, and belongs to the technical field of land investigation and data analysis, and the method comprises the steps: receiving an investigation and analysis instruction, and obtaining multi-source data of an investigated object, the multi-source data at least comprising a remote sensing image and land utilization attribute information; preprocessing the multi-source data to obtain processed data; based on a preset comparison logic, comparing the processed data with specified reference data, and generating and outputting a comparison result for an investigator to know; wherein the specified reference data refers to multi-source data of the investigated object with the same description as the processed data; the comparison result is at least used for representing the deviation between the processed data and the specified reference data, and the deviation is used for representing the change condition of the investigated object. According to the invention, the accuracy and high efficiency of land investigation and data analysis can be improved.

The invention belongs to the technical field of geographic information science, and particularly relates to a Markov-FLUS model-based feedback land utilization change simulation method and system, and the method comprises the steps: firstly predicting an initial area target vector based on historical data and a Markov chain, and inputting the initial area target vector into a FLUS model to carry out the first space distribution simulation; by calculating the achievement degree of the simulation result and the area target, the space conflict type which cannot be achieved due to the space constraint is identified, the area target vector is dynamically corrected according to the space conflict type, and the corrected target is fed back to the FLUS model for iterative simulation until the result is converged. According to the method, the technical problem of disjunction of area prediction and spatial layout in a traditional coupling model is effectively solved, and the scientificity and accuracy of land utilization change simulation are remarkably improved.

The invention discloses a method for quantifying the influence of vegetation change on regional rainfall and a related device, and the method comprises the steps: carrying out the evapotranspiration simulation of a target region through a remote sensing evapotranspiration model according to meteorological driving data and vegetation and land utilization data, and obtaining an evapotranspiration data set under different vegetation utilization scenes; performing monthly scale correction on the water vapor tracking input data through the evapotranspiration data set to obtain a corrected water vapor tracking input data set; the meteorological driving data and the corrected water vapor tracking input data set are adopted to drive the water vapor tracking model to perform forward tracking, and water vapor tracking results under different vegetation utilization scenes are output; and quantitatively analyzing the downwind rainfall influence of the vegetation change on the target area through the water vapor tracking results under different vegetation utilization scenes. According to the invention, based on the water vapor source-sink relationship of the region, the evapotranspiration change caused by the vegetation change is spatially redistributed, the influence on downwind rainfall is tracked, and the quantitative relationship of the influence of the vegetation change on the regional rainfall is revealed.

The invention relates to the technical field of land data planning, and discloses a reasonable land planning system based on machine learning carbon storage, and the system comprises a multi-source data fusion module, a carbon storage prediction module, a three-generation coordination discrimination module, a land use intelligent optimization module, a scene interaction deduction module, and a planning result generation module. By integrating data such as standardized land utilization and carbon density, a machine learning model is utilized to predict the dynamic state of the carbon density in real time, and the functional coordination degree of the three plants is quantitatively evaluated; and then, by taking the improvement of the carbon reserves and the coordination of the three plants as targets, carrying out spatial simulation and iterative optimization in combination with multi-objective optimization and a cellular automaton, comparing different policy carbon sink effects through scene deduction, and finally outputting an optimization scheme, thereby realizing the real-time closed loop of evaluation and optimization, and synergistically improving the carbon sink benefits and the three-plant function.

The invention relates to the technical field of photovoltaic power generation system optimization, in particular to a multi-objective collaborative optimization photovoltaic dip angle determination method, which comprises the following steps of: calculating solar radiation intensity received by a photovoltaic module based on a sun trajectory algorithm by collecting resource data, engineering parameters, terrain correction parameters and economic parameters; the assembly spacing and the land utilization rate are optimized in combination with full-time shadow analysis, and the minimum value of the occupied area is determined; a dynamic weight distribution strategy is adopted, factors such as power generation capacity, land rent and operation cost are coupled to calculate annual income, and the optimal photovoltaic dip angle is solved through a segmented iterative algorithm with the purpose of maximizing the total investment internal return rate (IRR). According to the method, collaborative optimization of land resource utilization and economic benefits can be realized, and a scientific dip angle decision basis is provided for photovoltaic projects with different land costs and topographic conditions.

The invention relates to the technical field of intelligent environmental protection monitoring, in particular to a method for intelligently monitoring and analyzing land utilization change in an ecological protection red line, which comprises the following steps of: acquiring a historical remote sensing image, extracting pixel-level vegetation and artificial indexes, constructing an ecological index time sequence data set, and constructing an ecological index time sequence data set based on the data set; a dynamic baseline prediction model fusing a long-term trend term, a seasonal periodic term and a standard deviation is established for each pixel, personalized prediction of a theoretical state of the pixel is realized, abnormality diagnosis is performed through comparison with a confidence interval output by the model, and an abnormal pixel is marked as a collaborative abnormal pixel, so that comprehensive risk assessment is carried out, and the risk assessment efficiency is improved. The method comprises the steps of calculating risk scores of all indexes and carrying out weighted fusion to obtain a comprehensive risk index, then dividing risk levels, outputting an anomaly diagnosis report, finally, carrying out reverse calibration on model parameters based on confirmed anomaly cases, and mining risk modes from case characteristics through unsupervised clustering.

The invention provides a remote sensing time sequence-based abandoned farmland identification method and device, equipment and a medium, and the method comprises the steps: obtaining multi-source land utilization data in a preset time period, and carrying out the preprocessing of the multi-source land utilization data, so as to obtain the reference farmland data of a target region; acquiring remote sensing image data in a preset time period, and extracting target cultivated land image data from the remote sensing image data based on the reference cultivated land data; processing the target cultivated land image data based on a preset processing algorithm to obtain vegetation change data and land state stage data in different time periods; and performing abandoned land recognition on the land state stage data based on the vegetation change data through a preset abandoned land recognition model to obtain abandoned land data. According to the method, remote sensing index time sequence change detection is associated with phenological characteristics, the farmland abandoned land judgment rule is constructed, farmland abandoned land data are extracted, a large number of training samples are not needed, and a large-range farmland abandoned land state can be automatically, intelligently and precisely recognized.

The invention discloses a water nutritive salt remote sensing inversion method based on multi-source fusion and a machine learning model. According to the method, non-optical environment factors such as optical remote sensing features, meteorological features and land utilization data are subjected to unified formatting and structured processing, so that multi-source data are input into an inversion model in a unified feature vector form. A self-adaptive screening strategy based on feature importance is adopted, the contribution degree of each feature can be dynamically calculated in the model training process, and variables with low contribution or large noise are automatically deleted. Temperature attenuation, a Metropolis criterion and disturbance generation are introduced, and a globally optimal model structure and hyper-parameter combination is automatically searched. Through joint design of data fusion, feature screening and an optimization algorithm, a stable, generalizable and easy-to-deploy nutritive salt inversion system is constructed, the problems that an existing method depends on a single spectrum, generalization is poor and parameter optimization is difficult can be effectively solved, and inversion of the concentration of the nutritive salt in the water body can be directly carried out.

The invention discloses a construction and verification method of a watershed water resource comprehensive simulation and configuration model, and belongs to the technical field of water resource planning and configuration. On the basis of a natural-social binary water circulation structure, flow generation, flow concentration and allocation modules are coupled, a topological relation is determined by dividing calculation units, and a model is constructed with the minimum water shortage rate and the optimal water supply fairness as targets; establishing a multi-source database containing meteorological information, land utilization information, reservoir information and water consumption unit information; performing iterative calibration and verification on natural water circulation and social water circulation parameters by using historical data; and coupling the model which is verified to be reliable with underlying surface and climate scene data of a future planning year, and operating and outputting a water resource optimal configuration scheme. The method solves the problems that an existing model is low in coupling degree and poor in precision, and high-precision simulation and scientific configuration of a drainage basin water resource system are achieved.

The invention discloses a regional carbon emission weight coefficient distribution method based on multi-source data fusion, and particularly relates to the technical field of carbon emission. The method comprises the following steps: establishing a unified space grid, carrying out standardization processing on multi-source data such as land utilization, industry POI, night light, high-resolution remote sensing images and electric power facilities, respectively calculating space carbon emission intensity indexes, and fusing annual reference and monthly dynamic characteristics to establish a basic carbon emission coefficient; then introducing high-resolution remote sensing NDVI correction and electric power facility enhancement weight, performing fine correction on the spatial expression of the carbon emission intensity, and finally distributing the total amount of regional statistical carbon emission to each grid unit according to a normalized proportion to form a high-resolution carbon emission spatial distribution diagram; the method has the advantages of high data fusion, high spatial precision and high dynamic response capability, and is suitable for application scenes such as urban carbon monitoring, quota setting and carbon management decision making.

The invention discloses a small watershed nitrogen and phosphorus load accounting method based on key process parameter calibration, and the method comprises the steps: obtaining the basic geographic hydrological data of a target small watershed, the data comprising the areas of different land utilization types in the watershed, the flow length of a water collection region river channel, and the average flow velocity; based on the hydrology and water quality monitoring data of the long-time sequence of the catchment area, adopting a Bayesian inversion model to quantitatively identify the nitrogen and phosphorus output coefficient of each land utilization type; calculating a soil nitrogen and phosphorus output load in the catchment area, determining a natural reduction coefficient of a nitrogen and phosphorus first-order river, and calculating transport time of nitrogen and phosphorus from a starting point of a river channel of the catchment area to a water outlet and transport time of soil output nitrogen and phosphorus from a tributary estuary to the water outlet of the catchment area; and calculating the water collection area outlet nitrogen and phosphorus load. According to the method, the overestimation error caused by the fact that a production and pollution discharge coefficient method is adopted in a traditional method is remarkably reduced through fusion of'pollution source nitrogen and phosphorus entering the river-remaining soil output-along-the-way natural reduction 'ternary accounting, the accuracy of load accounting is improved, the feasibility and advantages of the technical scheme are verified, and the method is particularly suitable for small watersheds with deficient basic data.

According to the public service facility supply and demand matching site selection method and system based on the geographic grid, the geographic grid is adopted to integrate multi-source heterogeneous data, data such as population and land utilization are mapped to the unified grid, statistics and space anomaly inspection are combined to guarantee data quality, manual operation is reduced, the technical threshold is lowered, and the method and system are suitable for large-scale popularization and application. Non-professionals can carry out analysis through a standardized process, a precise data foundation is laid for subsequent work, land space planning and current situation data are fused, a population prediction model is constructed to deduce future distribution, planning data are incorporated into analysis, a result is made to adapt to 5-10-year city development requirements, perspectiveness and practical operation are considered, and the method is suitable for popularization and application. And finally, quantizing an accessibility gap through a Gaussian two-step moving search method, iteratively screening an optimal scheme in combination with a greedy algorithm, determining a facility construction priority order, avoiding resource idleness and repeated construction, and ensuring that newly-added facilities efficiently fill the service gap.

The invention discloses a water area space land utilization function evaluation regulation and control method. The method comprises the following steps: collecting multi-source heterogeneous data of a water area space to construct a three-dimensional heterogeneous information network; inputting the three-dimensional heterogeneous information network into a pre-trained heterogeneous graph neural network to generate a node embedding vector; based on the node embedding vector, extracting a dynamic evolution characteristic tensor of a water area space land utilization function through a space-time diagram convolutional network; inputting the dynamic evolution characteristic tensor into an ecological system service value evaluation model, and outputting an ecological value matrix; and by taking the ecological value matrix as a constraint condition, calling a multi-objective optimization algorithm and performing collaborative optimization in combination with a multi-agent reinforcement learning framework, and generating a Pareto optimal solution set of water area space land utilization planning and a corresponding water area function partition configuration scheme. According to the method, scientificity, reasonability and sustainability of water area space land utilization planning are achieved, the land utilization rate is increased, and sustainable development of the water area space is promoted.

The invention provides a simulation design method of an urban ecological corridor, which comprises the following steps: S1, collecting a multi-temporal thermal infrared remote sensing image of a city to be simulated and designed and preprocessing to obtain a core heat island node; s2, heat resistance surface construction is carried out based on different surface types, a heat transfer resistance surface is obtained, and the different surface types comprise a digital elevation model, an impervious surface index, a normalized vegetation index, a normalized building index, a normalized water body index and a gradient; s3, taking the core heat island node as a network end point, searching a communication path with the lowest resistance on a heat transmission resistance surface, and obtaining an urban heat island corridor; and S4, on the basis of the urban land utilization data, ecological lands of forests, wetlands, large parks and water bodies serve as candidate ecological source lands, ecological connection paths are constructed from the angle of ecological connectivity by adopting the S2 and the S3, and the urban ecological corridor is obtained.

The invention discloses a land survey auxiliary device for land utilization planning, which belongs to the technical field of land survey, and comprises a shell, a manual rotating shaft arranged outside the shell, a ruler belt fixedly connected with the outer surface of the manual rotating shaft, and an annular plate fixedly connected with the front surface of a bottom cylinder arranged on the outer surface of the shell, and the lower part of the inner wall of the shell is fixedly connected with a pair of communicated screen drums. According to the device, when the ruler belt is pulled to drive the manual rotating shaft to rotate or the manual rotating shaft is rotated to wind the ruler belt, the first piston rod reciprocates on the inner wall of the piston groove, external air flows to the outside of the screen drum, and the effect of cleaning dust on the outer surface of the ruler belt is achieved under the cooperative arrangement of the first banister brush; and the conditions that due to accumulation of impurities such as dust, reading is difficult in the follow-up use process or blocking occurs in the winding process are relieved, the outer surface of the tape is automatically cleaned when the tape is stretched and wound in the device, and follow-up repeated use of the device is facilitated.

This invention relates to the field of ecological environment management technology, specifically disclosing a refined method and system for visualizing regional vegetation carbon sequestration. The method includes: extracting land use types of a target area based on remote sensing data of land use types and biomass; determining forest areas based on land use types and establishing growth curves for the forest areas based on preset fitting curves; calculating the carbon sequestration amount of the forest areas based on the growth curves and determining the carbon sequestration ratio of different forest areas; allocating the total carbon sequestration amount to different sub-regions of the target area based on the carbon sequestration ratios, and visually converting the allocated target areas. This invention first selects a target area, calculates the carbon sequestration amount based on the forest areas within the target area, then redistributes the carbon sequestration to different areas according to land use types, and visualizes the redistribution results, facilitating the management of the area.

This invention relates to a snowmelt flood prediction method and system based on a physical information neural network, specifically in the field of snowmelt flood prediction. By embedding physical constraints such as shallow water equations and energy conservation equations into the neural network model, the accuracy and stability of prediction are improved while maintaining physical consistency. It can effectively compensate for the shortcomings of traditional empirical models in terms of spatiotemporal continuity and generalization under extreme scenarios. At the application level, this invention can realize the fully automated operation of the entire process from meteorological data input, snowmelt and runoff calculation, flood evolution prediction to risk level assessment. The system can output multi-time step water depth, flow velocity and inundation range results based on real-time or forecast meteorological conditions, and combine them with population density, land use and infrastructure distribution data to generate flood risk zoning maps and early warning information, providing a scientific basis for watershed flood control scheduling, engineering design and disaster emergency management.

The invention discloses a GIS (Geographic Information System)-based urban and rural planning management system, which relates to the technical field of land planning and is characterized by acquiring multi-source data, preprocessing, standardizing and spatio-temporal indexing the multi-source data, evaluating a planning scheme, generating a comparison and improvement scheme, recharging actual construction data and public opinions and correcting a model. According to the method, multi-source data fusion and standardized processing are realized, multi-dimensional evaluation can be performed on travel convenience, land utilization efficiency, environmental toughness and law and regulation compliance, an optimization scheme is automatically generated based on an evaluation result, a problem land parcel is positioned through a spatio-temporal index, and the method is high in practicability and high in practicability. A plurality of sets of comparable adjustment schemes are provided, a feedback correction mechanism is combined, actual construction data and public opinions can be recharged, the model precision is continuously improved, finally, through visual display and permission generation, it is ensured that a planning result is legal, compliant, executable and transparent, and urban and rural planning scientificity and public participation degree are improved.

The invention discloses a hydrological analysis method and system based on SWAT model runoff reconstruction and a storage medium. The hydrological analysis method comprises the following steps: 1, obtaining research basin runoff data, meteorological data, digital elevation model data, land utilization data and soil data; 2, based on the data in the step 1, analyzing the evolution trend and potential mutation points in the runoff time sequence period by adopting a mutability test method; 3, dividing a natural period and a variation period based on the mutation points, and obtaining a natural runoff sequence by using an SWAT model; and 4, based on a SWAT model simulation result, extracting multiple hydrological indexes representing hydrological situation changes by adopting an IHA method, extracting ecologically-related hydrological indexes ERHIs from the hydrological indexes by adopting a PCA method, and calculating contribution rates of climate changes and human activities to the ecologically-related hydrological indexes by adopting a quantitative attribution method. The runoff driving mechanism of climate factors and human activities is disclosed, and the physical interpretability and decision support capability of the SWAT model are enhanced.

The invention relates to the technical field of environment monitoring and big data analysis, and discloses a soil environment quality evaluation method based on big data. The method comprises the following steps: fusing measured data of soil heavy metals with multi-source heterogeneous data such as remote sensing, terrain, land utilization, hydrogeology and the like; a space-attribute joint constraint depth generation network is constructed, an encoder of the space-attribute joint constraint depth generation network extracts multi-scale features, an attribute physical constraint module is embedded into a convection-dispersion equation to model a heavy metal migration mechanism, and a decoder outputs a high-fidelity concentration field; and dividing four pollution risk grades according to national standards, and generating an evaluation diagram. Through data driving and physical mechanism collaborative modeling, the pollution boundary depicting precision is improved, the prediction error under the low sampling density is reduced compared with a traditional method, the recall rate of a high-risk area is increased, and reliable support is provided for precise soil treatment.

This invention discloses an urban land use classification method based on the fusion of multi-source data and deep learning, comprising: Step 1: Constructing a spatiotemporal map using Poisson disk sampling and graph neural networks, integrating the spatiotemporal heterogeneity of multi-source data, and dynamically adjusting weights using an attention mechanism; Step 2: Extracting static features from remote sensing and DEM using Swin Transformer, processing dynamic features from social media and mobile phone signaling using LSTM, fusing static and dynamic features through cross-modal attention, and optimizing data matching degree based on Grad-CAM++; Step 3: Combining the person-to-land flow of mobile phone signaling with the spatial correlation of social media activities, predicting trends using LSTM and embedding ecological indicator constraints; Step 4: Constructing a cross-departmental data sharing platform, collaboratively optimizing planning through a unified indicator system, and dynamically adjusting parameters using social media to mine public demands and POI data; Step 5: Introducing ecological footprint and carbon emission indicators to assess sustainability, establishing a monthly update mechanism, and dynamically calibrating model parameters through real-time data streams.

The invention relates to the technical field of crowd flow prediction, discloses a crowd OD flow prediction method and related equipment, and effectively overcomes the defects in fine-grained city internal OD flow prediction in the prior art by constructing an urban crowd flow prediction model fusing multi-dimensional key factors. Spatial correlation among geographic units is captured by means of a spatial adjacency encoder, regional function complementarity and geographic distance characteristics are mined through a land utilization complementation encoder, traffic reachability information is integrated by means of a traffic connection encoder, and a characteristic pattern cooperatively output by a multi-dimensional encoder provides comprehensive support for prediction. According to the design, the comprehensive influence of spatial positions, land utilization and traffic conditions on urban travel is fully considered, the defects that a traditional model ignores spatial dependence and an existing machine learning and deep learning model is insufficient in multi-factor integration are overcome, and the prediction accuracy and the fitting degree are remarkably improved.