A method and system for spatio-temporal evolution modeling driven by ground surveying

Abstract

The embodiments of the present invention relate to the technical field of ground surveying and mapping data modeling. Specifically, it relates to a spatio-temporal evolution modeling method and system driven by ground surveying and mapping. The method first integrates the multi-dimensional correlation features of elevation, coverage type, and deformation data within at least three consecutive periods, breaking through the limitations of single-time-point or single-parameter analysis, and can accurately capture the non-linear spatio-temporal coupling law in ground evolution. Secondly, based on the spatio-temporal model construction method with dynamic parameter optimization, it effectively integrates the geographical space correlation features and the coverage evolution trend, enabling the model to have the ability of self-adaptive representation of complex geographical processes. In addition, by establishing a closed-loop feedback mechanism between the prediction results and the model parameters, the real-time iterative optimization of the model parameters is realized, significantly improving the long-term prediction stability under different geographical scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of ground surveying and mapping data modeling. Specifically, it relates to a spatio-temporal evolution modeling method and system driven by ground surveying and mapping. Background Art

[0002] The geographical evolution analysis technology is the core means to study the dynamic change law of the earth's surface, and has important application value in the fields of disaster warning, ecological governance, and geographical planning. This technology provides a scientific basis for identifying potential geological hazards and predicting the trend of ecological degradation by analyzing the spatio-temporal evolution characteristics of key elements such as elevation change, surface cover migration, and deformation.

[0003] However, traditional geographical evolution analysis mainly relies on single-cycle remote sensing data or manual surveys, combined with static geographic information systems for overlay analysis. Although it can achieve the evaluation of the basic surface state, its static modeling framework is difficult to adapt to the complexity of dynamic geographical processes, restricting the prediction accuracy and decision-making timeliness. In summary, the existing technologies are difficult to adapt to the changes in the dynamic geographical environment, and thus difficult to meet the long-term prediction stability requirements for different geographical scenarios at the present stage. Summary of the Invention

[0004] In order to at least overcome the above deficiencies in the existing technologies, one of the objectives of the present invention is to provide a spatio-temporal evolution modeling method and system driven by ground surveying and mapping.

[0005] An embodiment of the present invention provides a spatio-temporal evolution modeling method applied to a spatio-temporal evolution modeling system. The method includes: obtaining a multi-cycle ground surveying and mapping data set of a target geographical area, where the multi-cycle ground surveying and mapping data set includes at least three consecutive surveying and mapping cycle surface surveying and mapping data sets, and each surface surveying and mapping data set is composed of surface elevation data, surface cover type data, and surface deformation monitoring data under the same geographical coordinate system; performing spatio-temporal feature coupling processing on the multi-cycle ground surveying and mapping data set to generate a spatio-temporal evolution feature set, where the spatio-temporal evolution feature set includes surface deformation features, geographical space association features, and surface cover evolution features corresponding to each surveying and mapping cycle; based on a preset basic spatio-temporal evolution model, performing dynamic parameter optimization processing on the spatio-temporal evolution feature set to generate a target spatio-temporal evolution model, where the target spatio-temporal evolution model is used to represent the mapping relationship between surface surveying and mapping data and geographical space evolution patterns; invoking the target spatio-temporal evolution model to perform evolution prediction processing on the surface surveying and mapping data set of the current surveying and mapping cycle to generate a spatio-temporal evolution prediction result of the target geographical area, where the spatio-temporal evolution prediction result includes surface deformation prediction distribution data and surface cover change trend data; performing dynamic update processing on the target spatio-temporal evolution model according to the spatio-temporal evolution prediction result to obtain an updated spatio-temporal evolution model, and synchronizing the updated spatio-temporal evolution model to the evolution prediction processing of the next surveying and mapping cycle.

[0006] An embodiment of the present invention also provides a spatio-temporal evolution modeling system, including a processor, a memory and a bus connected to the processor; wherein, the processor and the memory complete communication with each other through the bus; the processor is used to call program instructions in the memory to execute the above-mentioned ground surveying-driven spatio-temporal evolution modeling method.

[0007] An embodiment of the present invention also provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the above-mentioned ground surveying-driven spatio-temporal evolution modeling method is implemented.

[0008] A ground surveying-driven spatio-temporal evolution modeling method and system provided by an embodiment of the present invention realize refined modeling and adaptive prediction capabilities of the geographical evolution process through spatio-temporal coupling analysis and dynamic model optimization mechanism of multi-period ground surveying data.

[0009] First, by integrating the multi-dimensional correlation features of elevation, coverage type and deformation data within at least three consecutive periods, the limitations of single-time-point or single-parameter analysis are broken through, and the non-linear spatio-temporal coupling law in surface evolution can be accurately captured. Secondly, based on the spatio-temporal model construction method of dynamic parameter optimization, the geographical spatial correlation features and coverage evolution trends are effectively integrated, enabling the model to have the adaptive representation ability for complex geographical processes. In addition, by establishing a closed-loop feedback mechanism between the prediction results and the model parameters, the real-time iterative optimization of the model parameters is realized, and the long-term prediction stability under different geographical scenarios is significantly improved.

[0010] Designed in this way, the problem that traditional static models are difficult to adapt to dynamic geographical environment changes is solved, and through the deep coupling of spatio-temporal features of multi-source data, the associated evolution patterns of potential surface deformation and coverage change are effectively identified, providing high-confidence decision support for applications such as geological disaster early warning and ecological evolution analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0012] Figure 1 It is a flowchart of a ground surveying-driven spatio-temporal evolution modeling method provided by an embodiment of the present invention.

[0013] Figure 2 It is a block diagram of a spatio-temporal evolution modeling system provided by an embodiment of the present invention.

[0014] Icon:

[0015] 100 - Spatiotemporal Evolution Modeling System;

[0016] 101 - Processor; 102 - Memory; 103 - Bus. Detailed Implementation Manner

[0017] The exemplary embodiments disclosed by the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention can be more thoroughly understood and the scope of the present invention can be fully conveyed to those skilled in the art.

[0018] To better understand the above technical solution, the technical solution of the present invention will be described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0019] Figure 1 FIG. is a flowchart of a ground surveying-driven spatiotemporal evolution modeling method according to an embodiment of the present invention, applied to a spatiotemporal evolution modeling system, including steps 101 - 105.

[0020] Step 101: Obtain a multi-period ground surveying data set of a target geographic area, where the multi-period ground surveying data set includes at least three consecutive surveying period surface surveying data sets, and each surface surveying data set is composed of surface elevation data, surface cover type data, and surface deformation monitoring data under the same geographic coordinate system.

[0021] In this embodiment, the target geographic area is a specific geological area in a mountain landslide monitoring scenario, such as a certain mountain slope area. The multi-period ground surveying data set refers to a plurality of time-series geospatial data sets obtained through periodic monitoring. Each surveying period can be a natural time interval or an event-driven interval. The surface elevation data refers to the digital terrain elevation information obtained through lidar or synthetic aperture radar interferometry, used to characterize the three-dimensional morphological changes of the surface; the surface cover type data refers to the land cover category information obtained through multi-spectral remote sensing image interpretation or on-site investigation and classification, such as vegetation-covered areas, bare rock areas, soil erosion areas, etc.; the surface deformation monitoring data refers to the displacement rate and cumulative displacement data collected by surface displacement monitoring devices, used to quantify the surface stability.

[0022] Exemplarily, in the scenario of mountain landslide monitoring, the target geographical area is a high-risk landslide body. The surface mapping data sets for consecutive multiple mapping cycles need to meet the following conditions: all data uniformly adopt the standard geographical coordinate system, and the elevation data is obtained through a high-resolution digital elevation model; the coverage type data is based on the classification results of remote sensing images and is divided into categories such as forests, shrubs, and bare land; the deformation monitoring data is collected in real time by multiple monitoring points arranged on the landslide body, recording the periodic three-dimensional displacement amounts of each point. By integrating multi-source data and unifying the spatial reference, the constructed multi-cycle ground mapping data set can provide the basic input for subsequent spatio-temporal feature analysis.

[0023] Step 102: Perform spatio-temporal feature coupling processing on the multi-cycle ground mapping data set to generate a spatio-temporal evolution feature set, where the spatio-temporal evolution feature set includes surface deformation features, geospatial association features, and surface coverage evolution features corresponding to each mapping cycle.

[0024] In this embodiment, the spatio-temporal feature coupling processing refers to jointly analyzing multi-cycle data in the time dimension and the space dimension to extract cross-cycle and cross-region dynamic change laws. Specifically, the surface deformation features are obtained by calculating the differences or statistics of deformation monitoring data in adjacent cycles, and are used to characterize the spatio-temporal heterogeneity of surface movement; the geospatial association features refer to the correlation indexes between deformation and terrain parameters generated through spatial autocorrelation analysis or interpolation methods; the surface coverage evolution features are extracted through the change detection algorithm of time-series remote sensing images to obtain the coverage type conversion matrix.

[0025] In the scenario of mountain landslide monitoring, the generation process of the spatio-temporal evolution feature set is exemplified as follows: for multiple consecutive mapping cycles, first perform spatio-temporal interpolation on the displacement data to generate a landslide body global displacement rate distribution map, and calculate the displacement acceleration rate as the deformation feature; secondly, extract slope partitions based on the elevation data, and statistically analyze the spatial aggregation of displacement rates within each slope interval through spatial overlay analysis to generate geospatial association features; at the same time, use time-series remote sensing images to identify the vegetation degradation area and the bare land expansion range to construct the surface coverage evolution features. By fusing the coupling features of deformation, terrain, and coverage changes, the spatio-temporal evolution feature set can reveal the association pattern between the precursor of landslide body instability and surface environmental changes.

[0026] Step 103: Based on a preset basic spatio-temporal evolution model, perform dynamic parameter optimization processing on the spatio-temporal evolution feature set to generate a target spatio-temporal evolution model, where the target spatio-temporal evolution model is used to characterize the mapping relationship between surface mapping data and geospatial evolution patterns.

[0027] In this embodiment, the basic spatio-temporal evolution model can be a landslide dynamics model based on physical mechanisms or a data-driven machine learning model. The dynamic parameter optimization process refers to adjusting the model parameters according to the current spatio-temporal evolution feature set to make it adapt to the specific geological conditions of the target geographical area. For example, in a physical model, the optimized parameters may include geotechnical mechanical parameters; in a machine learning model, the optimized parameters may involve network weights or hyperparameters.

[0028] Exemplarily, in a mountain landslide monitoring scenario, the preset basic model is a prediction model that integrates spatio-temporal features, and its initial parameters are obtained through training based on historical data. The dynamic parameter optimization process includes: inputting the spatio-temporal evolution feature set into the model, calculating the deformation prediction error through an adaptive optimization algorithm, and updating the model weights; at the same time, constraining the parameter search space according to the geological exploration data of the target geographical area to ensure that the optimized target spatio-temporal evolution model can accurately map the co-evolution law of landslide body deformation-topography-coverage. The optimized model can output a landslide stability probability distribution map, providing a basis for risk grading.

[0029] Step 104: Invoke the target spatio-temporal evolution model to perform evolution prediction processing on the surface mapping data set of the current mapping cycle, and generate the spatio-temporal evolution prediction result of the target geographical area. The spatio-temporal evolution prediction result includes surface deformation prediction distribution data and surface coverage change trend data.

[0030] In this embodiment, the evolution prediction processing refers to using the optimized target spatio-temporal evolution model to deduce the surface state within a certain future time window. The surface deformation prediction distribution data can be spatial grid data, and each pixel value represents the predicted displacement amount or instability probability; the surface coverage change trend data is generated through a probability model to form a coverage type transition probability matrix.

[0031] In a mountain landslide monitoring scenario, when invoking the target spatio-temporal evolution model for prediction, the input data includes elevation data, displacement data, and coverage classification results of the current cycle. The model output results are: 1) Surface deformation prediction distribution data within a certain future time period, showing that the displacement rate in a specific area increases significantly, corresponding to a higher instability probability; 2) The surface coverage change trend data indicates that due to environmental factors, the bare land area is expected to expand, and the risk of degradation in the vegetation-covered area increases. This prediction result provides a quantitative basis for disaster warning.

[0032] Step 105: Perform dynamic update processing on the target spatio-temporal evolution model according to the spatio-temporal evolution prediction result to obtain an updated spatio-temporal evolution model, and synchronize the updated spatio-temporal evolution model to the evolution prediction processing of the next mapping cycle.

[0033] In this embodiment, the dynamic update process refers to iteratively correcting the model parameters or structure based on the difference between the latest prediction results and the actual observed data. The specific methods include incremental learning or transfer learning. For example, when the deviation between the measured data in the new cycle and the predicted value exceeds a preset threshold, the model retraining process is triggered to adjust the model parameters to reduce the prediction error.

[0034] For example, in the scenario of mountain landslide monitoring, the measured data in subsequent surveying and mapping cycles show that the displacement rate in a certain area exceeds the predicted value, and the model prediction error triggers the dynamic update mechanism. The update process includes: adding the new data to the training set and using the sliding window method to retain the recent data to optimize the model generalization ability; adaptively adjusting the model parameters by comparing the spatial correlation between the prediction and the measured results to enhance the response ability to emergencies. The updated spatio-temporal evolution model will be used as the basic model for subsequent prediction tasks to continuously optimize the model prediction ability.

[0035] Designed in this way, based on steps 101 - 105, through multi-cycle data fusion, spatio-temporal feature coupling, model dynamic optimization, and prediction-update closed-loop, the timeliness and accuracy of landslide evolution prediction are significantly improved. At the same time, it is ensured that the model can adapt to the changes in complex geological environments, providing reliable technical support for geological disaster prevention and control.

[0036] It is worth mentioning that in the embodiment of the present invention, the target spatio-temporal evolution model is a dynamic prediction framework constructed based on multi-source geospatial data, aiming to reveal the complex correlation laws between surface deformation and geographical environment elements. The target spatio-temporal evolution model takes the integration of physical mechanisms and data-driven as the design principle. By integrating multi-dimensional spatio-temporal features such as surface elevation, coverage type, and deformation monitoring, a quantitative mapping relationship between the surface evolution process and the driving factors is established. The core architecture of the target spatio-temporal evolution model consists of a basic spatio-temporal evolution model and a dynamic optimization mechanism: the basic model can select the landslide dynamics equation based on geotechnical mechanics principles, or use machine learning models such as long short-term memory network (LSTM), spatio-temporal graph convolutional network (ST-GCN), etc. The initial parameters are obtained through pre-training with historical multi-cycle data and have the basic modeling ability for basic processes such as terrain evolution and mass movement.

[0037] In addition, the target spatio-temporal evolution model can enhance regional adaptability through dynamic parameter optimization, and use the real-time updated spatio-temporal evolution feature set for iterative parameter tuning. In the scenario of mountain landslide monitoring, the optimization process adopts an adaptive particle swarm algorithm or Bayesian inference method, and imposes physical constraints on the parameter search space by combining geological exploration data (such as rock layer dip angle, soil moisture content) of the target area to ensure that the model can not only capture the non-linear relationship between displacement acceleration rate and slope change, but also reflect the coupling effect of vegetation degradation and surface seepage. For example, the optimization results for a certain landslide show that the model significantly reduces the displacement prediction error and accurately identifies the deformation correlation characteristics of potential slip surfaces and elevation mutation areas at the same time.

[0038] In addition, the target spatio-temporal evolution model also has multi-scale prediction capabilities, and can output surface deformation prediction distribution data with a spatial resolution of up to the meter level and a coverage type transfer probability matrix. Deformation prediction uses spatio-temporal sequence extrapolation technology, inputs features such as the elevation change gradient and displacement vector field of the current period into the trained network, and generates the expected displacement rate and instability probability heat map of each grid cell in the next few days. The prediction of coverage change trend is achieved by constructing a hidden Markov model, combining historical coverage conversion rules and environmental factors (such as rainfall, human activity intensity), and calculating the spatial probability distribution of bare land expansion and vegetation degradation.

[0039] Finally, the target spatio-temporal evolution model can adopt a closed-loop update mechanism to ensure prediction timeliness. By comparing the spatial correlation index between the predicted deformation field and GNSS monitoring data, the parameter update process is dynamically triggered. When the dynamic time warping (DTW) distance between the new period data and the prediction result exceeds the threshold, the system automatically starts the incremental learning module, and uses the elastic weight consolidation algorithm to incorporate new features while retaining historical knowledge, avoiding model degradation caused by sudden environmental changes. In a landslide warning case, through continuous updates in multiple cycles, the model incorporates the pore water pressure changes caused by sudden rainfall into the feature system, greatly improving the determination coefficient between the prediction result and the true displacement curve, and significantly enhancing the prediction robustness under complex meteorological conditions. This "prediction-validation-update" closed-loop architecture enables the model to continuously adapt to the dynamic evolution of the geological system and provides an intelligent analysis tool with spatio-temporal generalization capabilities for geological disaster risk warning.

[0040] In one implementation, the spatio-temporal feature coupling process for the multi-period ground surveying data set in step 102 to generate a spatio-temporal evolution feature set includes:

[0041] Step 1021: Perform surface deformation feature extraction processing on the surface surveying data set of each surveying period to obtain the surface deformation rate data and surface deformation direction data corresponding to each surveying period.

[0042] In specific implementation, the extraction and processing of surface deformation features are achieved by fusing lidar point cloud data and synthetic aperture radar interferometry data. First, differential interferometry processing is performed on the surface elevation data at different time nodes within the same surveying and mapping cycle to generate a differential interferometry phase map. Subsequently, a phase unwrapping algorithm is used to eliminate the phase ambiguity caused by elevation changes and extract the elevation change amount of each pixel within the surveying and mapping cycle. Based on the spatial correlation between the elevation change amount and the time-series monitoring data, the three-dimensional displacement vector of each monitoring point is calculated, where the horizontal displacement component is obtained by calculating the coordinate change amount of the GNSS monitoring station, and the vertical displacement component is directly obtained from the differential elevation data. Finally, the three-dimensional displacement vector is decomposed into horizontal and vertical deformation rates to form a surface deformation rate data grid map covering the target geographical area, and at the same time, a surface deformation direction data matrix is generated according to the azimuth angle distribution of the displacement vector. For example, in the feature extraction of the third surveying and mapping cycle, by processing the radar image data from May to August of a certain year, it is found that the horizontal deformation rate of a certain monitoring point reaches 12.3 mm / month, the direction is 15° south-southeast, and the vertical deformation rate is -5.6 mm / month, indicating a significant oblique sliding trend in this area.

[0043] Step 1022: Perform coverage change comparison processing on the surface coverage type data of adjacent surveying and mapping cycles to generate surface coverage evolution path data, where the surface coverage evolution path data includes the conversion sequence of surface coverage types and the conversion time interval.

[0044] Among them, the coverage change comparison processing adopts a technical solution that combines time-series remote sensing image supervised classification and change detection algorithms, specifically including: performing radiometric correction and geometric registration on the multi-spectral images of the same area in adjacent cycles to ensure spatial resolution and pixel alignment; using a random forest classifier to perform land cover classification on the two-phase images respectively to generate a coverage type distribution map; detecting the areas where the coverage type has changed based on the post-classification comparison method, recording the change type, area ratio, and occurrence period; constructing a coverage type conversion chain for each changed pixel, such as a degradation path from "forest → shrub → bare land", and counting the time interval between adjacent conversion steps. In a typical example, by comparing the data of the second and third surveying and mapping cycles of a certain year, it is found that the coverage type in the middle of a certain slope changes from "dense forest" to "sparse shrub" within two months, and then further degenerates into "bare rock" within one month. This evolution path is encoded as the sequence code F → S → B, and the time interval parameter is recorded as [60, 30] days, and finally integrated into the surface coverage evolution path database.

[0045] Step 1023: Based on the geospatial topological relationship, perform spatial correlation degree calculation processing on the surface deformation rate data, surface deformation direction data, and surface coverage evolution path data to generate geospatial correlation features, where the geospatial correlation features include deformation-coverage coupling coefficients and spatial evolution synchrony indicators.

[0046] In this step, the spatial correlation degree calculation process is realized by constructing a spatial weight matrix and covariance analysis. First, a spatial topological connection relationship between monitoring points is established using the Delaunay triangulation network; then, the spatial autocorrelation Moran's I index is used to quantify the spatial aggregation degree of the deformation rate and coverage evolution, and the deformation-coverage coupling coefficient is calculated; at the same time, the time-phase relationship between the change in the deformation direction and the conversion of the coverage type is analyzed through cross-wavelet transform to generate a spatial evolution synchronization index. For example, in the posterior edge area of a landslide, among the areas where the deformation rate exceeds 20 mm / month, 85% of the pixels undergo coverage type degradation during the same period, the deformation-coverage coupling coefficient reaches 0.78, and the spatial evolution synchronization index shows that there is a significant positive correlation between the acceleration of deformation and the degradation of coverage and the time lag is less than 7 days, indicating that the loss of surface vegetation may be an important inducement for the intensification of deformation.

[0047] Step 1024: Perform multi-dimensional fusion processing on the surface deformation rate data, the surface deformation direction data, the surface coverage evolution path data, and the geospatial correlation characteristics to generate the spatio-temporal evolution feature set.

[0048] In this embodiment, the multi-dimensional fusion processing adopts a feature-level fusion strategy, which specifically includes: performing Z-score standardization on the deformation rate data to eliminate the dimensional difference; converting the deformation direction data into azimuth sine and cosine components to avoid the angle cycle problem; performing one-hot encoding on the coverage evolution path data to generate a multi-dimensional category feature vector; and finally integrating the standardized deformation rate, direction components, encoded coverage path, and correlation coefficients into a unified spatio-temporal feature tensor through a feature splicing layer. For example, in a landslide monitoring case, the dimension of the fused feature tensor is [256×256×8], where the spatial resolution is 10 m, and the 8 channels respectively correspond to the standardized deformation rate, sine and cosine direction components, 3D coverage path encoding, and 2 correlation feature indicators. This feature set completely characterizes the spatio-temporal dynamic characteristics of the landslide body in the third quarter of a certain year.

[0049] In another implementation, in step 103, the dynamic parameter optimization processing is performed on the spatio-temporal evolution feature set based on a preset basic spatio-temporal evolution model to generate a target spatio-temporal evolution model, including:

[0050] Step 1031: Divide the spatio-temporal evolution feature set into a training feature subset and a validation feature subset. The training feature subset contains the spatio-temporal evolution features of the first N - 1 surveying and mapping periods, and the validation feature subset contains the spatio-temporal evolution features of the Nth surveying and mapping period.

[0051] In specific implementation, the partitioning operation follows the principle of temporal cross-validation. For example, when the total number of mapping cycles N = 5, the feature tensors of the first 4 cycles (2022Q4 - 2023Q3) are used as the training feature subset, and the features of the 5th cycle (2023Q4) are used as the validation feature subset. During the partitioning process, it is necessary to ensure that each subset maintains spatial range consistency to avoid introducing biases due to spatial sampling differences. At the same time, the time span of the validation feature subset should be equal to the model prediction step. For example, when the model predicts the evolution in the next three months, the validation subset should contain exactly three months of monitoring data.

[0052] Step 1032: Input the training feature subset into the basic spatio-temporal evolution model for iterative training processing to generate an initial spatio-temporal evolution model. The iterative training processing includes surface deformation feature weight adjustment operations and coverage evolution path matching operations.

[0053] In this embodiment, the iterative training processing adopts a spatio-temporal convolutional neural network architecture. The specific process is as follows: First, spatio-temporal features are extracted through a three-dimensional convolutional layer, and the convolution kernel size is set to 5×5×3 (spatial 5×5, time 3 cycles); in the feature weight adjustment stage, the attention mechanism is used to calculate the importance scores of the deformation rate features and the coverage path features, and dynamically adjust the channel weights of the feature maps; the coverage evolution path matching operation calculates the path similarity loss function by comparing the edit distance between the predicted path and the real path. For example, during the training process, the model automatically enhances the feature weights in the high deformation rate area to 1.5 times the original value, and at the same time sets the weight coefficient of the coverage path matching loss function to 0.3 to ensure that the model synchronously optimizes the deformation prediction and coverage change prediction performance.

[0054] Step 1033: Call the validation feature subset to perform prediction accuracy verification processing on the initial spatio-temporal evolution model to obtain model error distribution data, which includes deformation prediction error rate and coverage trend deviation.

[0055] In actual application, the validation processing adopts the leave-one-out cross-validation strategy, which specifically includes: inputting the validation feature subset into the initial spatio-temporal evolution model to generate a predicted deformation rate field and a coverage path transfer matrix; calculating the deformation prediction error rate of each pixel as (predicted value - measured value) / measured value × 100%; the coverage trend deviation is measured by the Jaccard similarity coefficient to calculate the intersection-to-union ratio of the predicted path and the real path. For example, the deformation prediction error rate in the middle area of the landslide body ≤ 8%, but the error rate in the front edge area reaches 23% due to the influence of sudden rainfall; the coverage path deviation reaches 0.85 in the vegetation stable area and drops to 0.62 in the human activity interference area. This error distribution data reveals the prediction limitations of the model in complex interference areas.

[0056] Step 1034: Perform reverse correction processing on the characteristic mapping parameters of the initial spatiotemporal evolution model according to the model error distribution data to generate the target spatiotemporal evolution model.

[0057] In this step, the inverse correction process uses an adaptive moment estimation algorithm. Specifically, it involves adjusting the convolution kernel weights based on the deformation error rate distribution and applying a higher learning rate to the feature channels corresponding to high-error areas. A threshold trigger mechanism is set for the coverage deviation. When the deviation exceeds 0.4, a dual-path training mode is initiated, simultaneously optimizing the parameters of the main network and the auxiliary correction network. For example, during the parameter correction process, the convolution kernel weights in the leading edge area are updated 2.3 times more than those in the central area. At the same time, a priori knowledge constraint on coverage type is introduced, and the upper limit of the transition probability from bare rock areas to soil erosion areas is set to an exemplary 0.15. The resulting target spatiotemporal evolution model has a significantly lower average error rate on the validation set.

[0058] In another implementation, the calling of the target spatiotemporal evolution model in step 104 to perform evolution prediction processing on the surface mapping dataset of the current mapping cycle to generate the spatiotemporal evolution prediction result of the target geographic area includes:

[0059] Step 1041: Perform real-time feature extraction processing on the surface mapping dataset of the current mapping cycle to obtain current surface deformation features and current coverage evolution features.

[0060] As you can understand, real-time feature extraction and processing utilize a streaming computing framework. The specific process is as follows: radar interferometry data and remote sensing imagery from the latest cycle (e.g., Q4 2023) are accessed, and online phase unwrapping and land classification are performed within a 5-minute time window. A sliding average method is used to eliminate short-term noise interference and generate contour maps of the current deformation rate. The deformation direction vector field is corrected using real-time GNSS displacement monitoring data. An incremental classifier is also used to update the cover type distribution and detect new areas of cover change that occurred within the past week. For example, features extracted on December 15th of a certain year showed that the deformation rate at a monitoring point suddenly increased to 35 mm / day, the direction shifted to 8° east of due south, and a patch of degraded shrub covering hundreds of square meters appeared on the northeast slope.

[0061] Step 1042: Input the current surface deformation characteristics and the current coverage evolution characteristics into the target spatiotemporal evolution model, and generate preliminary prediction results through multi-level feature mapping processing.

[0062] In actual implementation, the multi-level feature mapping process follows the inherent architecture of the model, specifically including: compressing the input features into a high-dimensional latent space representation in the spatial encoding layer; capturing temporal dependencies through a temporal gated recurrent unit; and performing a deconvolution operation in the decoding layer to reconstruct the deformation rate distribution and coverage path transition probability for the next month. For example, the preliminary prediction results show that the current high-deformation area will expand to the adjacent valley area within the next half month, the predicted peak deformation rate is 48 mm / day, the area of the bare rock area is expected to increase by 15%, and the spatial overlap rate between the newly degraded area and the predicted high-deformation area reaches 82%.

[0063] Step 1043: Perform geospatial constraint processing on the preliminary prediction results. The geospatial constraint processing includes a terrain continuity verification operation and a coverage type logic verification operation to obtain the verified prediction results.

[0064] Exemplarily, the terrain continuity verification calculates slope threshold constraints through a digital elevation model to force the deformation direction in areas with a slope > 35° to be consistent with the terrain trend; the coverage type logic verification applies a Markov chain state transition matrix to prohibit conversion types that violate the ecological succession law, such as "bare rock → forest". For example, after verification, the deformation direction in a steep slope area is corrected from the originally predicted vertical downward to the inclined direction along the slope surface, and the abnormal "bare rock → forest" conversion in the coverage path is replaced with a reasonable path of "bare rock → soil erosion".

[0065] Step 1044: Generate the surface deformation prediction distribution data and the surface coverage change trend data according to the verified prediction results, and perform a trend coherence analysis process on the surface deformation prediction distribution data and the historical deformation data to generate a trend correction coefficient.

[0066] In the embodiment of the present invention, the trend coherence analysis uses a dynamic time warping algorithm, specifically including: performing an optimal path matching between the current prediction curve and the actual deformation curves of the past 6 cycles to calculate the slope change consistency coefficient; determining the trend correction coefficient based on the relationship between the consistency coefficient and a preset threshold, and starting a trend compensation mechanism when the coefficient < 0.7. For example, it is found through analysis that the deviation between the currently predicted deformation acceleration trend and the average trend of historical data reaches 28%, and accordingly a trend correction coefficient of 0.85 is generated for subsequent calibration processing.

[0067] Step 1045: Perform dynamic calibration processing on the surface deformation prediction distribution data based on the trend correction coefficient to obtain the spatio-temporal evolution prediction results of the target geographical area.

[0068] Exemplarily, the dynamic calibration process may adopt the weighted average method. For example, the original predicted deformation rate of a certain pixel is 50 mm / day, and the extrapolated value of the historical trend is 42 mm / day. After calibration with a coefficient of 0.85, the final predicted value is 48.8 mm / day. The calibrated prediction result not only retains the short-term mutation signal driven by the current features but also takes into account the long-term trend reflected by the historical rules. For example, calibration can improve the correlation coefficient between the deformation prediction result and the measured value to a certain extent.

[0069] In a preferred embodiment, the dynamic update process of the target spatio-temporal evolution model according to the spatio-temporal evolution prediction result in step 105 to obtain an updated spatio-temporal evolution model includes:

[0070] Step 1051: Obtain the updated actual surface mapping data set after the end of the current mapping period, perform feature extraction processing on the actual surface mapping data set, and generate actual spatio-temporal evolution features.

[0071] In a specific implementation, the actual surface mapping data set includes newly obtained radar interferometry data, multi-spectral images, and on-site monitoring records. The feature extraction processing adopts the same processing flow as steps 1021 - 1024 but is executed based on the latest data: First, perform unwrapping processing on the differential interferometric phase diagram to generate actual surface deformation rate data after eliminating the atmospheric delay error; synchronously perform supervised classification on the remote sensing image to obtain the current coverage type distribution map, and generate actual coverage evolution path data by comparing with the data of the previous period; then, through spatial autocorrelation analysis and time series alignment, construct actual deformation spatial correlation features and actual coverage time correlation features; finally, couple the two types of features to form an actual spatio-temporal evolution feature tensor with a dimension of [256×256×6].

[0072] For example, after the end of the mapping period in the fourth quarter of a certain year, the actual feature extraction results show that the deformation rate in the trailing edge area of a certain landslide reaches 41 mm / month, the coverage path shows an abnormal degradation mode of "dense forest → bare rock", and its spatial correlation feature shows that the spatial overlap rate between the deformation center and the coverage degradation area reaches 92%.

[0073] Step 1052: Compare the spatio-temporal evolution prediction result with the actual spatio-temporal evolution features to generate a model error feature set, and the model error feature set includes a deformation prediction error vector and a coverage trend offset.

[0074] In this embodiment, the differential comparison process adopts a multi-scale verification method: in the spatial dimension, the predicted deformation rate field and the actual deformation rate field are compared pixel by pixel after being gridded, and the root mean square error is calculated as the deformation prediction error vector; in the time dimension, the time nodes of the predicted coverage path and the actual evolution path are aligned through the dynamic time warping algorithm, and the difference in the type conversion time interval is calculated as the coverage trend offset. For example, the predicted deformation rate in a certain landslide front area is 38 mm / month, the actual monitoring value is 45 mm / month, and the error vector value is 7 mm / month; in the prediction of the coverage path, the conversion of "shrub→bare land" is expected to occur on the 25th day, while the actual observation is on the 18th day, resulting in a positive time offset of 7 days. This difference data is encoded into the error feature set.

[0075] Step 1053: Perform an incremental adjustment process on the mapping parameters of the target spatio-temporal evolution model according to the model error feature set to obtain the adjusted mapping parameters. The incremental adjustment process adopts a sliding window weight update mechanism.

[0076] In this embodiment, the specific implementation of the incremental adjustment process includes: setting the time window size to 3 survey periods, and only retaining the error feature data of the most recent period and the previous two periods; calculating the parameter update amount through the elastic weight consolidation algorithm to ensure that the mapping relationship of existing important features is not damaged when new knowledge is incorporated; implementing differential adjustment on the kernel weights of the third and fourth convolutional layers in the convolutional neural network, and the adjustment amplitude is proportional to the spatial distribution gradient of the error vector. For example, for the average error of 6.5 mm / month in the middle area of the landslide, the update coefficient of the corresponding convolutional kernel weight is set to 0.32, while the update coefficient of the error area of 12 mm / month in the front area is increased to 0.56. At the same time, the parameters of the fully connected layer related to the coverage path prediction are frozen to avoid over-adjustment.

[0077] Step 1054: Synchronize the adjusted mapping parameters into the target spatio-temporal evolution model to generate the updated spatio-temporal evolution model, and delete the original mapping parameters in the basic spatio-temporal evolution model that exceed the preset time threshold.

[0078] It can be understood that the parameter synchronization process adopts a version control strategy: establish a model parameter repository, mark the new mapping parameters as version V2.1, and retain the parameter history of the most recent 5 versions; physically delete the original parameters (such as version V1.3 and earlier) that exceed the 24-month validity period; at the same time, update the model metadata record, and establish an index association between the new parameters and the current geographical environment feature library. For example, in the model update in the first quarter of 202X, 12 parameter files of version V1.2 trained in 202(X - 3) were deleted, 8 parameter files of version V2.1 were added, and the updated model showed a significant reduction in the deformation prediction error on the test set.

[0079] In a preferred embodiment, the feature extraction process for the actual surface mapping dataset in step 1051 to generate actual spatio-temporal evolution features includes:

[0080] Step 10511: Perform deformation gradient calculation processing on the surface elevation data in the actual surface mapping dataset to generate actual surface deformation rate data.

[0081] For example, the deformation gradient calculation processing uses synthetic aperture radar interferometry. The specific process is as follows: Perform small baseline set processing on 30 radar images obtained in the current period to construct 63 interferometric pairs; Use StaMPS software for permanent scatterer identification and phase unwrapping to extract pixel-level deformation time series; Calculate the monthly average deformation rate of each pixel through linear regression analysis to generate a deformation rate raster map with a spatial resolution of 10 meters. For example, the deformation time series of a certain monitoring point (coordinates E112.35°, N28.76°) shows that the cumulative settlement from October to December of a certain year reached 58 mm, and the actual deformation rate calculated is 19.3 mm / month.

[0082] Step 10512: Perform coverage change path analysis processing on the surface cover type data in the actual surface mapping dataset to generate actual coverage evolution path data.

[0083] Among them, the coverage change path analysis processing uses an improved hidden Markov model, specifically including: Continuously classify the multi-spectral images with a week as the time unit to generate a coverage type time series; Decode the most likely type conversion path through the Viterbi algorithm; Record the type conversion order of each pixel and the time interval between adjacent conversions. For example, the type change of a certain pixel from the 45th to the 48th week of a certain year is "shrub → bare land → soil erosion", and the time intervals are 14 days and 21 days respectively, and the path coding is S → B → E_14_21.

[0084] Step 10513: Perform spatial overlay processing on the actual surface deformation rate data and the historical surface deformation rate data to generate actual deformation spatial correlation features.

[0085] In this step, the spatial overlay processing uses a spatio-temporal cube analysis method: Construct a three-dimensional data cube (X, Y, T), where X / Y are spatial coordinates and T is the time dimension; Calculate the autocorrelation of each spatial unit in the time series through the Moran index to generate a spatial correlation heat map; Use the Getis-Ord Gi* statistic to identify the hot spots of the deformation rate. For example, the overlay analysis shows that the west side area of a certain landslide shows a high-high spatial clustering pattern (Moran's I = 0.67, p < 0.01) in three consecutive periods, indicating that there is a persistent deformation spatial correlation characteristic in this area.

[0086] Step 10514: Perform time series alignment processing on the actual coverage evolution path data and the historical coverage evolution path data to generate actual coverage time correlation features.

[0087] Among them, the time series alignment processing uses the dynamic time warping algorithm: perform time warping path matching on the coverage paths of each spatial unit, and calculate the cumulative distance of the optimal alignment path; extract the periodic features of the path sequence through Fourier transform to generate time correlation indicators including frequency amplitude and phase angle. For example, the matching result of the coverage path in a certain slope area shows that its evolution period has a significant correlation with the rainfall period (phase difference < 5 days), and this feature is encoded as the time correlation parameter θ = 0.78.

[0088] Step 10515: Couple the actual deformation spatial correlation features and the actual coverage time correlation features to generate the actual spatio-temporal evolution features.

[0089] In this embodiment, the coupling processing uses tensor fusion technology: resample the spatial correlation heat map to a resolution of 256×256 and convert it into a 32-bit floating-point matrix; normalize the time correlation parameter matrix; fuse the two types of feature matrices into a 6-channel spatio-temporal feature tensor through Kronecker product operation. For example, the fused feature tensor shows strong coupling characteristics of spatial correlation value 0.82 and time correlation value 0.7 at the trailing edge area of the landslide, and this feature is used by the subsequent model update process to correct geotechnical mechanics parameters.

[0090] In a preferred embodiment, the step of performing difference comparison processing on the spatio-temporal evolution prediction result and the actual spatio-temporal evolution features in Step 1052 to generate a model error feature set includes:

[0091] Step 10521: Extract the predicted surface deformation rate data in the spatio-temporal evolution prediction result, and perform spatial grid comparison processing with the actual surface deformation rate data to generate the deformation rate difference value corresponding to each spatial grid unit. The grid comparison processing uses bilinear interpolation to unify the predicted grid and the actual grid to the same spatial reference system, and calculates the absolute error and relative error pixel by pixel: absolute error ΔV = |V_pred - V_actual|, relative error δV = ΔV / V_actual × 100%. For example, the predicted value of a 10×10 meter grid unit (row 153, column 227) is 32.5 mm / month, and the actual value is 28.7 mm / month. It is calculated that ΔV = 3.8 mm / month and δV = 13.2%.

[0092] Step 10522: Extract the predicted coverage change trend data from the spatio-temporal evolution prediction results, perform a time node comparison process with the actual coverage evolution path data, and generate the coverage type matching degree corresponding to each time node.

[0093] For example, the time node comparison process uses a sliding window matching method: taking 7 days as the time window unit, statistically calculating the type consistency ratio of the predicted path and the actual path within the corresponding window; calculating the matching degree through the Jaccard similarity coefficient. For example, within the time window from the 15th to the 21st day, the predicted path is "shrub→bare land", the actual path is "shrub→soil erosion", the intersection is {shrub}, and the union is {shrub, bare land, soil erosion}, and the matching degree J = 1 / 3 ≈ 0.33.

[0094] Step 10523: Perform a spatial clustering analysis process on the deformation rate difference value to generate a spatial distribution pattern of deformation errors.

[0095] In this embodiment, the spatial clustering analysis uses the DBSCAN algorithm: set the neighborhood radius ε = 150 meters and the minimum number of samples MinPts = 5; identify high-error clustering areas (ΔV ≥ 8 mm / month) and low-error dispersion areas (ΔV ≤ 3 mm / month); calculate the centroid coordinates and error means of each cluster. For example, in the error analysis of 2023Q4, 3 high-error clusters are identified. Among them, the largest cluster covers an area of 1.2 hectares, with an average ΔV = 11.4 mm / month, and its spatial distribution highly coincides with the known geological fault zone trend.

[0096] Step 10524: Perform a time series analysis process on the coverage type matching degree to generate a time evolution pattern of coverage errors.

[0097] Exemplarily, the time series analysis uses the ARIMA model: construct a matching degree time series on a weekly basis, determine the model order through the autocorrelation function (p = 2, d = 1, q = 1); fit to obtain the trend term and the periodic term, and calculate the distribution characteristics of the prediction residuals. For example, the analysis results show that there is a significant period of 14 days (p < 0.05) in the matching degree sequence, and the residual standard deviation is 0.21. This pattern indicates that there is a systematic time lag problem in the coverage prediction of the model.

[0098] Step 10525: Perform a joint coding process on the spatial distribution pattern of deformation errors and the time evolution pattern of coverage errors to generate the model error feature set.

[0099] In this step, the joint encoding process adopts a multi-modal fusion method: converting the spatial clustering result into a spatial index file in GeoJSON format; encoding the time series analysis result into a high-dimensional feature vector; generating a joint feature matrix through a spatio-temporal cross-attention mechanism. For example, the encoded feature set includes 12 spatial cluster attribute fields, 26 time pattern parameters, and 18 cross-correlation indicators, providing multi-dimensional error representations for model parameter optimization.

[0100] In a preferred embodiment, the incrementally adjusting the mapping parameters of the target spatio-temporal evolution model according to the model error feature set in step 1053 to obtain the adjusted mapping parameters includes:

[0101] Step 10531: Converting the deformation error spatial distribution pattern into a deformation error weight matrix, where each element in the deformation error weight matrix corresponds to an error influence coefficient of a spatial grid cell.

[0102] In a specific implementation, the conversion process adopts a spatial interpolation and normalization method: First, based on the DBSCAN clustering result, determine the spatial boundary of the high-error region, and use the inverse distance weighting method to calculate the error influence coefficient of each 10×10 meter grid cell. The formula is W_ij = exp(-ΔV_ij / σ), where ΔV_ij is the deformation rate difference value of this grid, and σ is the regional error standard deviation; then normalize the calculation result to the [0, 1] interval to generate a 256×256 floating-point weight matrix. For example, for a grid (row 89, column 156) at the center of a high-error cluster, ΔV = 12.3 mm / month, and the regional σ = 4.7 mm / month, and W_ij = 0.08 is calculated, indicating that the parameter adjustment priority of this region is the highest.

[0103] Step 10532: Converting the coverage error time evolution pattern into a coverage error time vector, where each element in the coverage error time vector corresponds to the error accumulation amount of a time node.

[0104] In this embodiment, the conversion process adopts an exponential decay model: taking a week as the time unit, accumulating the Jaccard matching degree error of each time node. For example, the matching degree J_15 = 0.33 in the 15th week, calculate its error contribution to this week and subsequent times, and the accumulated amount E_20 = 0.41 is calculated at the 20th week, forming the error distribution characteristics in the time dimension.

[0105] Step 10533: Based on the deformation error weight matrix, perform gradient descent adjustment processing on the deformation mapping layer parameters in the target spatio-temporal evolution model.

[0106] In this embodiment, the adjustment process implements a differentiated learning rate strategy: the deformation map layer contains three 3D convolution kernel groups, and their weight update amount is ΔW = η × W_ij × ∇L, where η = 0.001 is the base learning rate, W_ij is the corresponding grid error weight, and ∇L is the gradient of the loss function. For example, the learning rate for high-weight regions (W_ij > 0.7) is increased to 0.0015, while the learning rate for low-weight regions (W_ij < 0.3) is reduced to 0.0005, ensuring that the model focuses on correcting parameter deviations in areas with significant spatial correlation.

[0107] Step 10534: Perform time attenuation adjustment processing on the coverage mapping layer parameters in the target spatiotemporal evolution model based on the coverage error time vector.

[0108] The adjustment process uses a sliding time window mechanism: the weight updates of the long short-term memory network units covering the mapping layer follow the rule Δt = β × E_t, where β = 0.002 is the time decay coefficient and E_t is the accumulated error at each time node. For example, the weight adjustment for the time unit corresponding to the 20th week is Δt = 0.002 × 0.41 = 0.00082. Simultaneously, the parameters within the ±2 weeks before and after this time window are adjusted in conjunction to enhance the continuity of the temporal dimension prediction.

[0109] Step 10535: jointly optimize the adjusted deformation mapping layer parameters and the cover mapping layer parameters to generate adjusted mapping parameters.

[0110] In this embodiment, the joint optimization uses an alternating direction multiplier method: coupling constraints are set for the deformation and coverage parameters, and the joint loss function is minimized through iterative solution. Specifically, this involves 10 alternating iterations, where each iteration first optimizes the deformation parameters while fixing the coverage parameters, and then optimizes the coverage parameters while fixing the deformation parameters. For example, after three rounds of iteration, the deformation prediction error decreased by 12%, and the coverage match improved by 9%. The resulting parameter file is 48MB in size, containing 12,288 deformation convolution kernel weights and 9,216 coverage LSTM unit parameters.

[0111] In a preferred embodiment, the step 1054 of synchronizing the adjusted mapping parameters into the target spatiotemporal evolution model to generate the updated spatiotemporal evolution model includes:

[0112] Step 10541: Create a mapping parameter version identifier corresponding to the adjusted mapping parameter, where the mapping parameter version identifier includes a parameter generation timestamp and a spatial grid code.

[0113] In this embodiment, the version identification construction rule is as follows: the timestamp adopts the ISO8601 standard format, and the spatial grid code is generated into 12 - character strings based on the Geohash algorithm. For example, the combination of the timestamp "2024 - 03 - 15T14:23:05Z" and the grid code "ws101mvt6s9c" forms the version ID "20240315142305_ws101mvt6s9c", ensuring that the parameters can be traced back to a specific time and spatial range.

[0114] Step 10542: Bind the adjusted mapping parameters with the version identification of the mapping parameters to generate a set of mapping parameters with version identification.

[0115] In this step, the binding process adopts binary encapsulation technology: insert a 128 - byte identification segment at the head of the parameter file, which includes the ASCII code of the timestamp (24 bytes), the grid code (12 bytes), and the checksum (4 bytes). For example, after binding, the size of a certain parameter file increases to 48.2MB, and the file header contains the identification information "20240315142305_ws101mvt6s9c", and the version attributes can be directly read through a hexadecimal editor.

[0116] Step 10543: Perform version timeliness detection processing on the historical mapping parameter set stored in the target spatio - temporal evolution model. The version timeliness detection processing is based on whether the time difference between the timestamp generated based on the parameters and the current surveying and mapping cycle exceeds a preset threshold.

[0117] Among them, 365 days is set as the expiration threshold for timeliness detection, and the difference is calculated using UNIX timestamps. For example, it is detected that the timestamp corresponding to the version ID "20230316102231_ws101mvt5r2d" is March 16, 2023, and the difference from the current date of March 15, 2024 is 364 days, with only 1 day left until the expiration threshold, and it is marked as the status to be deleted.

[0118] Step 10544: Delete the expired mapping parameters in the historical mapping parameter set whose time difference exceeds the preset threshold, and retain the historical mapping parameters that do not exceed the threshold as reference parameters.

[0119] In this embodiment, the deletion operation implements a dual - mechanism of physical erasure and logical marking: first, remove the expired records in the parameter storage index, and then perform three - time overwrite erasure on the disk storage block. For example, deleting 5 expired versions releases 1.2GB of storage space, and retain the last 3 versions (June 10, 2023; December 5, 2023; February 28, 2024) as reference parameters, with a total size of 720MB.

[0120] Step 10545: Write the set of mapping parameters with version identifiers into the parameter storage space of the target spatio-temporal evolution model, and perform spatial grid coding alignment processing with the reference parameters.

[0121] For example, the storage space management adopts a sharding strategy: divide the 256×256 grid into 64 sub-regions of 32×32, and the parameters of each sub-region are stored independently. The alignment processing ensures that the Geohash prefix of the new parameters is the same as the first 8 bits of the reference parameters. For example, the new parameter version "ws101mvt6s9c" and the reference parameter "ws101mvt5r2d" have the same first 8 bits "ws101mvt", realizing spatially consistent storage.

[0122] Step 10546: Perform compatibility verification processing on all mapping parameters in the parameter storage space. The compatibility verification processing includes spatial grid coding consistency verification and parameter generation timestamp continuity verification.

[0123] For example, the verification algorithm performs three-step verification: 1) Check the Geohash prefix consistency of the parameters in the same sub-region; 2) Verify whether the timestamp sequence is continuous without breaks; 3) Detect the matching of the parameter file header checksum and the content hash value. For example, it is found that there are parameters with mixed encodings of "ws101" and "ws102" in a certain sub-region, triggering an exception alarm and isolating the conflicting files.

[0124] Step 10547: When multiple valid mapping parameter versions are detected under the same spatial grid coding, extract the parameter generation timestamps of each version and perform difference sorting processing with the timestamp of the current surveying and mapping period.

[0125] In the embodiment of the present invention, the sorting algorithm is sorted in ascending order based on the absolute value of the time difference. For example, there are three versions under a certain grid coding: 2024-03-10 (time difference 5 days), 2024-02-28 (time difference 16 days), 2024-01-15 (time difference 59 days), and the sorting result is [2024-03-10, 2024-02-28, 2024-01-15].

[0126] Step 10548: Select the mapping parameter version with the smallest time difference from the current surveying and mapping period according to the difference sorting result, and mark the remaining version parameters as pending archival status.

[0127] Exemplarily, the selection strategy sets 7 days as the valid window: if the minimum difference ≤ 7 days, directly select it; otherwise, trigger the parameter reconstruction process. For example, select the 2024-03-10 version (time difference 5 days) as the main parameter, move the other two versions to the archival area, and set the retention period to one month.

[0128] Step 10549: Perform spatial overlay fusion processing on the selected mapping parameter version and the reference parameters to generate a fused updated mapping parameter set; overwrite the original parameter storage area of the target spatio-temporal evolution model with the updated mapping parameter set to complete the model update operation and generate the updated spatio-temporal evolution model.

[0129] In the embodiment of the present invention, the fusion processing adopts the weighted average method: the weight of the new parameter is 0.7, and the weight of the reference parameter is 0.3. The formula is W_fused = 0.7W_new + 0.3W_base. For example, the original value of a certain convolution kernel parameter is [0.12, -0.05, 0.33], the reference value is [0.15, -0.08, 0.29], and the fused value is [0.129, -0.059, 0.318]. Among them, the updated model shows a significant improvement in prediction accuracy on the test set, and the storage occupancy is significantly optimized.

[0130] In another preferred embodiment, in the evolution prediction process of synchronizing the updated spatio-temporal evolution model to the next mapping cycle in step 105, it includes:

[0131] Step 10551: At the start time node of the next mapping cycle, extract the updated mapping parameter set in the original parameter storage area of the target spatio-temporal evolution model; identify the mapping parameter version identifiers of the parameters in the extracted updated mapping parameter set, and parse the parameter generation timestamp and spatial grid encoding included in the mapping parameter version identifier.

[0132] In a specific implementation, the parameter extraction operation is executed through a version control interface: at 08:00 on April 1, 2024 (the start time of the next cycle), load all parameter files marked as "active" from the distributed storage cluster; use a regular expression matching algorithm to parse the version identifier in the file name. For example, extract the generation timestamp "2024-03-29T15:02:17Z" and the 12-bit Geohash encoding "ws101mvt6s9c" from "20240329150217_ws101mvt6s9c.bin". During the parsing process, data integrity verification is implemented, and the CRC32 checksum is used to verify the consistency of the file header and content, and the files that fail the verification are excluded.

[0133] Step 10552: Perform a matching degree calculation process on the parameter generation timestamp and the prediction start time of the next mapping cycle to generate a time matching coefficient matrix; perform an effectiveness screening process on the extracted updated mapping parameter set according to the time matching coefficient matrix, and retain the valid parameters with a time matching coefficient greater than the set threshold.

[0134] For example, the formula for calculating the time matching coefficient is:

[0135] $C_t = 1 - |T_{gen} - T_{start}| / (T_{end} - T_{start})$。

[0136] Wherein, $T_{gen}$ is the parameter generation timestamp, $T_{start}$ is the start time of the period, and $T_{end}$ is the expiration time of the parameter. Set the threshold $C_{threshold}=0.85$, and filter out the parameter files with $C_t\geq0.85$. For example, a certain parameter is generated at 2024 - 03 - 28T14:00:00Z, the start time of the period is 2024 - 04 - 01T00:00:00Z, and the expiration date is 2024 - 06 - 30T00:00:00Z. Calculate $C_t = 0.91$, which meets the validity condition.

[0137] Step 10553: Perform grid - by - grid matching processing on the spatial grid encoding of the valid parameters and the geospatial grid division data of the next mapping period.

[0138] In the embodiment of the present invention, the matching processing uses spatial index acceleration technology: load the MBR (Minimum Bounding Rectangle) spatial index of the 256×256 grid of the target area, decode the 12 - bit Geohash encoding of each parameter into a longitude - latitude range, and judge the inclusion relationship with the grid spatial range. For example, the parameter encoding "ws101mvt6s9c" corresponds to a grid cell with a longitude range of 112.34° - 112.35° and a latitude range of 28.75° - 28.76° after decoding, which completely coincides with the grid in row 153 and column 227 of the target area, and is marked as a successful match.

[0139] Step 10554: When it is detected that the target spatial grid encoding does not correspond to a valid parameter, perform spatial interpolation and completion processing using the valid parameters of adjacent spatial grid encodings; load the completed valid parameters into the real - time prediction operation module of the basic spatio - temporal evolution model in the order of spatial grid encoding.

[0140] In the embodiment of the present invention, inverse distance weighting method is used for spatial interpolation: taking the target grid as the center, search for all valid parameter grids within a radius of 300 meters. An exemplary weight calculation formula is $W_d = 1 / (d^2+\epsilon)$, where $d$ is the distance from the grid center, and $\epsilon = 1e - 5$ to prevent division - by - zero errors. For example, a missing grid (row 89, column 201) is interpolated through the parameters of 4 adjacent valid grids, and the weights are 0.42, 0.31, 0.19, and 0.08 respectively. Finally, the generated parameter value is the weighted average of the adjacent parameters.

[0141] Step 10555: Generate a mapping relationship index table between the mapping parameter version identifier of the valid parameters and the time node of the next mapping period. The mapping relationship index table includes spatial grid encoding, parameter version identifier, and loading timestamp.

[0142] Among them, the index table is constructed using a columnar storage structure: each row record contains three fields: Geohash encoding (12 bytes), version identifier (36 bytes), and loading timestamp (8 bytes). For example, the record "ws101mvt6s9c |20240329150217_ws101mvt6s9c | 20240401080000" indicates that the grid parameter version was loaded at 08:00:00 on April 1, 2024, and the total index table size is approximately 256×256×56≈3.5MB.

[0143] Step 10556: During the evolution prediction process of the next surveying and mapping cycle, continuously monitor the time difference between the timestamps of each parameter version identifier in the mapping relationship index table and the current prediction time node.

[0144] Among them, the monitoring mechanism implements a sliding window check: every time a prediction step (e.g., 15 minutes) is completed, calculate the difference ΔT = |T_current - T_load| between the current time T_current and the parameter loading time T_load. For example, when making a prediction at 2024-04-01T10:30:00, the loading time of a certain parameter is 2024-04-01T08:00:00, and ΔT = 2.5 hours, which does not exceed the synchronization threshold (default 24 hours).

[0145] Step 10557: When the time difference exceeds the preset synchronization threshold, trigger a parameter version update request and extract the latest mapping parameter version from the parameter storage area of the updated spatio-temporal evolution model.

[0146] In this embodiment, the threshold trigger logic is set hierarchically: the threshold for the core area (such as the middle of the landslide body) is set to 6 hours, and the threshold for the edge area is set to 24 hours. For example, when the ΔT of a certain core grid parameter reaches 6 hours and 01 minute, an update request is automatically sent to the parameter server to retrieve the parameter version with the latest generation time (such as 20240401075959_ws101mvt7a2b).

[0147] Step 10558: Replace the extracted latest mapping parameter version into the real-time prediction operation module according to the spatial grid encoding, and synchronously update the corresponding entry in the mapping relationship index table.

[0148] In this step, the replacement operation implements atomic update: first, lock the prediction calculation thread of the target grid, write the new parameter into the temporary memory area, verify the checksum, then switch the pointer to point to the new parameter, and finally update the timestamp field of the index table. For example, update the parameter version of grid ws101mvt7a2b from 20240329150217 to 20240401075959, and the index table loading timestamp is synchronously changed to 2024-04-01T10:31:22.

[0149] Step 10559: After completing all prediction processes for the next surveying and mapping cycle, associate and store the version identifier of the mapping parameters actually used and the corresponding prediction results in the model log database.

[0150] Exemplarily, the storage structure is designed as a relational database table: the main table records the prediction task ID, time range, and spatial range; the detail table stores the parameter version identifier, predicted value, and actual value (subsequent updates) for each grid. For example, the record of task ID #20240401_landslide contains 256×256 = 65536 detail data entries. Each record field includes [grid code, parameter version, predicted deformation rate, predicted coverage path]. The total data volume is approximately 65MB, and after column compression, it is stored as 8.7MB.

[0151] In an extensible embodiment, the method further includes:

[0152] Extract the model error feature set in the updated spatio-temporal evolution model, perform error source tracing analysis on the model error feature set, and generate error source distribution data;

[0153] Perform adaptive adjustment processing on the learning rate parameter of the updated spatio-temporal evolution model according to the error source distribution data, and generate dynamic learning rate configuration parameters;

[0154] Perform incremental optimization processing on the mapping parameters of the updated spatio-temporal evolution model based on the dynamic learning rate configuration parameters, and generate optimized mapping parameters;

[0155] Perform fusion verification processing on the optimized mapping parameters and the historical parameter versions, and generate a parameter compatibility evaluation result;

[0156] Perform selective overwrite processing on the optimized mapping parameters according to the parameter compatibility evaluation result, generate the final updated spatio-temporal evolution model, and synchronize it to the subsequent surveying and mapping cycle.

[0157] During the iterative optimization process of the landslide monitoring system, extract the model error feature set in the updated spatio-temporal evolution model, perform error source tracing analysis on the model error feature set, and generate error source distribution data. The error source tracing analysis uses the random forest feature importance evaluation method. By constructing an error source classification model, calculate the contribution degree index of each input feature to the prediction error, and generate an error source distribution heat map containing spatial location, time stage, and feature type.

[0158] For example, in the error analysis of the second quarter of 2024, it was found that 65% of the deformation prediction errors in the front area of the landslide were due to the fact that the mutation characteristics of the surface coverage type were not effectively learned. The importance score of the coverage evolution path characteristics in this area reached 0.87, which was significantly higher than that of the terrain continuity characteristics (0.23). According to the distribution data of the error sources, adaptive adjustment processing was performed on the learning rate parameters of the updated spatio-temporal evolution model to generate dynamic learning rate configuration parameters. The specific implementation was as follows: for the feature channels corresponding to the error sources with high contribution degrees, the initial learning rate was increased from 0.001 to 0.0025, and at the same time, the learning rate attenuation was implemented for the feature channels with low contribution degrees to 0.0003.

[0159] Based on the dynamic learning rate configuration parameters, incremental optimization processing was performed on the mapping parameters of the updated spatio-temporal evolution model to generate optimized mapping parameters. The random gradient descent algorithm with momentum term was used in this process. In 8 consecutive training cycles, the update amplitude of the weights of the fully connected layer corresponding to the coverage path characteristics in the front area reached 1.8 times the original value. The optimized mapping parameters were fused and verified with the historical parameter versions to generate a parameter compatibility evaluation result. The verification method included: calculating the cosine similarity index between the old and new parameter vectors, and setting the threshold > 0.75 as the compatibility standard; the verification of the parameters in the middle area of the landslide showed that the similarity reached 0.82, while the similarity in the front area decreased to 0.68 due to the drastic adjustment of the feature weights, triggering the parameter rollback protection mechanism. According to the parameter compatibility evaluation result, selective coverage processing was performed on the optimized mapping parameters to generate the final updated spatio-temporal evolution model and synchronize it to the subsequent surveying and mapping cycles. The specific implementation was as follows: directly covering the new parameters in the compatible areas, and retaining the mixed configuration of 70% old parameters and 30% new parameters in the incompatible areas, ensuring that in the prediction task of the third quarter of 2024, the prediction error in the front area was reduced to 9.3% and there was no parameter conflict warning.

[0160] In an extensible embodiment, the method further includes:

[0161] Obtain the geospatial grid division data of the next surveying and mapping cycle, perform multi-scale recombination processing on it to generate a nested spatial grid structure;

[0162] Based on the time node division data, segment the prediction time step length to generate a multi-granularity time prediction sequence;

[0163] Align the nested spatial grid structure with the multi-granularity time prediction sequence to generate a spatio-temporal matching index table;

[0164] Call the updated spatio-temporal evolution model to perform independent prediction processing on the mapping relationships in the index table to generate a multi-scale spatio-temporal prediction result set;

[0165] Perform cross-scale fusion processing on the multi-scale spatio-temporal prediction result set to generate target spatio-temporal prediction data with a unified resolution;

[0166] Perform real-time comparison processing on the target spatio-temporal prediction data and the actual data to generate a cross-scale error distribution map and calibrate the hierarchical weights.

[0167] In the extended scheme for improving the landslide spatio-temporal prediction accuracy, obtain the geospatial grid division data for the next survey period, perform multi-scale reorganization processing on it to generate a nested spatial grid structure. The multi-scale reorganization uses a quadtree space segmentation algorithm to divide the basic 10-meter grid into three-level sub-grid systems of 5 meters, 20 meters, and 40 meters, forming a multi-layer nested structure including 1024×1024 (5 meters), 512×512 (10 meters), 256×256 (20 meters), and 128×128 (40 meters).

[0168] Based on the time node division data, perform segmented processing on the prediction time step length to generate a multi-granularity time prediction sequence. The specific implementation is as follows: decompose the three-month prediction cycle into three-level time series of daily granularity (90 steps), weekly granularity (12 steps), and monthly granularity (3 steps), and each level of the sequence maintains temporal continuity through linear interpolation. Align the nested spatial grid structure with the multi-granularity time prediction sequence to generate a spatio-temporal matching index table. This index table contains spatial level coding, time granularity markers, and data storage pointers. For example, the index item "L3_T2_S45" represents the storage address of level 3 (20-meter grid), time granularity 2 (weekly step), and spatial block 45. Call the updated spatio-temporal evolution model to perform independent prediction processing on the mapping relationship in the index table to generate a multi-scale spatio-temporal prediction result set. The prediction process implements parallel computing, and each scale prediction task is assigned to different computing units of the GPU cluster. The global trend prediction is preferentially completed at the 40-meter coarse-grained level, and its result provides boundary constraints for the fine-grained prediction.

[0169] Perform cross-scale fusion processing on the multi-scale spatio-temporal prediction result set to generate target spatio-temporal prediction data with a unified resolution. The fusion algorithm uses the multi-resolution analysis method of wavelet transform to perform weighted synthesis on the frequency domain characteristics of the prediction results at each scale. The weight coefficient of the 20-meter level is set to 0.6, the 10-meter level is 0.3, and the 5-meter level is 0.1. Perform real-time comparison processing on the target spatio-temporal prediction data and the actual data to generate a cross-scale error distribution map and calibrate the hierarchical weights. The calibration process dynamically adjusts the fusion weights by calculating the determination coefficient between the prediction results at each scale and the actual values. For example, when the error at the 5-meter level exceeds 15%, its weight is automatically reduced to 0.05 to ensure the stable maintenance of the global deformation prediction accuracy in the flood season prediction in 2024.

[0170] In an extensible embodiment, the method further includes:

[0171] Extract the historical prediction result set and the actual surveying and mapping data set, and perform spatio-temporal dimension expansion processing on them respectively to generate a spatio-temporal cube of prediction results and a spatio-temporal cube of actual data;

[0172] Perform unit-by-unit difference calculation on the two spatio-temporal cubes to generate an error spatio-temporal cube;

[0173] Perform spatio-temporal pattern mining on the error spatio-temporal cube to generate an error spatio-temporal evolution pattern;

[0174] Perform spatio-temporal adaptive correction on the model mapping parameters according to the error spatio-temporal evolution pattern to generate a set of spatio-temporal correction parameters;

[0175] Overlay and fuse the spatio-temporal correction parameter set with the original parameters to generate a spatio-temporal enhanced update model and synchronize it to subsequent cycles.

[0176] In the improvement scheme for enhancing the spatio-temporal generalization ability of the model, it involves extracting the historical prediction result set and the actual surveying and mapping data set, and performing spatio-temporal dimension expansion processing on them respectively to generate a spatio-temporal cube of prediction results and a spatio-temporal cube of actual data.

[0177] The spatio-temporal dimension expansion processing uses tensor reshaping technology to reconstruct the data of 12 consecutive surveying cycles into a four-dimensional tensor [N, H, W, T], where N = 256×256 spatial grids, T = 12 time nodes, and each unit stores a composite value of the deformation rate and the coverage path encoding. Perform unit-by-unit difference calculation on the two spatio-temporal cubes to generate an error spatio-temporal cube. The difference calculation implements a double-threshold mechanism: units with a deformation rate difference > 8 mm / month or a coverage path matching degree < 0.6 are marked as significant error units. Perform spatio-temporal pattern mining on the error spatio-temporal cube to generate an error spatio-temporal evolution pattern. The mining algorithm uses three-dimensional density clustering (3D-DBSCAN), sets the neighborhood radius ε = 3 grids × 3 grids × 2 cycles, and identifies 6 persistent error clusters. The largest error cluster continuously appears in the landslide trailing edge area during the period from 2023Q4 to 2024Q2, with a spatial range of 1.2 hectares and a time span of 9 months.

[0178] Perform spatio-temporal adaptive correction processing on the model mapping parameters according to the spatio-temporal evolution pattern of errors to generate a set of spatio-temporal correction parameters. The correction processing adopts a spatio-temporal attention mechanism, adding a spatio-temporal position encoding layer in the Transformer architecture, so that the model's attention to high-error spatio-temporal units is increased to 2.3 times the benchmark value. Superimpose and fuse the set of spatio-temporal correction parameters with the original parameters to generate a spatio-temporal enhanced update model and synchronize it to subsequent cycles. The fusion method uses a residual connection structure, and the formula is W_new = W_orig + λΔW, where λ = 0.35 is the spatio-temporal correction coefficient and ΔW is the correction parameter increment. Through the analysis in the third quarter of 2024, this scheme significantly improves the prediction accuracy in the area of persistent error clusters. The correlation coefficient between the spatio-temporal attention weights and the measured error distribution remains at a high level, significantly enhancing the model's ability to capture complex spatio-temporal evolution laws.

[0179] In the embodiment of the present invention, through the spatio-temporal coupling analysis and dynamic model optimization mechanism of multi-period ground surveying data, the refined modeling and adaptive prediction ability of the geographical evolution process are realized.

[0180] First, by integrating the multi-dimensional correlation features of elevation, coverage type, and deformation data within at least three consecutive cycles, the limitations of single-time-point or single-parameter analysis are broken through, and the non-linear spatio-temporal coupling law in surface evolution can be accurately captured.

[0181] Second, based on the spatio-temporal model construction method of dynamic parameter optimization, the geographical space correlation features and coverage evolution trends are effectively fused, enabling the model to have the adaptive representation ability for complex geographical processes.

[0182] In addition, by establishing a closed-loop feedback mechanism between the prediction results and the model parameters, the real-time iterative optimization of the model parameters is realized, significantly improving the long-term prediction stability under different geographical scenarios.

[0183] Designed in this way, the problem that traditional static models are difficult to adapt to dynamic geographical environment changes is solved. Also, through the deep coupling of spatio-temporal features of multi-source data, the associated evolution pattern of potential surface deformation and coverage change is effectively identified, providing high-confidence decision support for applications such as geological disaster early warning and ecological evolution analysis.

[0184] The embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the spatio-temporal evolution modeling method driven by ground surveying is implemented.

[0185] The embodiment of the present invention provides a processor, and the processor is used to run a program, wherein when the program runs, the spatio-temporal evolution modeling method driven by ground surveying is executed.

[0186] In the embodiment of the present invention, as Figure 2As shown, the spatio-temporal evolution modeling system 100 includes at least one processor 101, and at least one memory 102 and a bus 103 connected to the processor 101; wherein, the processor 101 and the memory 102 complete mutual communication through the bus 103; the processor 101 is used to call program instructions in the memory 102 to execute the above-mentioned spatio-temporal evolution modeling method driven by ground surveying.

[0187] The present invention is described with reference to the flowcharts and / or block diagrams of methods, spatio-temporal evolution modeling systems (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0188] In a typical configuration, the spatio-temporal evolution modeling system includes one or more processors (CPUs), a memory, and a bus. The spatio-temporal evolution modeling system may also include an input / output interface, a network interface, etc.

[0189] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one storage chip. The memory is an example of a computer-readable medium.

[0190] A computer-readable medium includes permanent and non-permanent, removable and non-removable media that can implement information storage by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage computer-readable storage media or any other non-transmission medium that can be used to store information that can be accessed by a spatio-temporal evolution modeling system. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0191] It should also be noted that the term "comprises", "comprising", or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or computer-readable storage medium that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or computer-readable storage medium. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or computer-readable storage medium that comprises the element.

[0192] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0193] The above are only embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. A method for spatio-temporal evolution modeling driven by ground surveying and mapping, characterized in that The method includes: Obtaining a multi-period ground surveying data set of a target geographical area, where the multi-period ground surveying data set includes surface surveying data sets of at least three consecutive surveying periods, and each surface surveying data set is composed of surface elevation data, surface coverage type data, and surface deformation monitoring data under the same geographical coordinate system; Performing spatio-temporal feature coupling processing on the multi-period ground surveying data set to generate a spatio-temporal evolution feature set, where the spatio-temporal evolution feature set includes surface deformation features, geospatial association features, and surface coverage evolution features corresponding to each surveying period; Geospatial association features refer to the correlation indexes of deformation and terrain parameters generated by spatial autocorrelation analysis or interpolation methods; Based on a preset basic spatio-temporal evolution model, performing dynamic parameter optimization processing on the spatio-temporal evolution feature set to generate a target spatio-temporal evolution model, where the target spatio-temporal evolution model is used to represent the mapping relationship between surface surveying data and geospatial evolution patterns; Invoking the target spatio-temporal evolution model to perform evolution prediction processing on the surface surveying data set of the current surveying period to generate a spatio-temporal evolution prediction result of the target geographical area, where the spatio-temporal evolution prediction result includes surface deformation prediction distribution data and surface coverage change trend data; Performing dynamic update processing on the target spatio-temporal evolution model according to the spatio-temporal evolution prediction result to obtain an updated spatio-temporal evolution model, and synchronizing the updated spatio-temporal evolution model to the evolution prediction processing of the next surveying period; The performing spatio-temporal feature coupling processing on the multi-period ground surveying data set to generate a spatio-temporal evolution feature set includes: Performing surface deformation feature extraction processing on the surface surveying data set of each surveying period to obtain surface deformation rate data and surface deformation direction data corresponding to each surveying period; Performing coverage change comparison processing on the surface coverage type data of adjacent surveying periods to generate surface coverage evolution path data, where the surface coverage evolution path data includes the conversion sequence of surface coverage types and the conversion time interval; Based on the geospatial topological relationship, performing spatial correlation degree calculation processing on the surface deformation rate data, surface deformation direction data, and surface coverage evolution path data to generate geospatial association features, where the geospatial association features include deformation-coverage coupling coefficients and spatial evolution synchrony indexes; The deformation-coverage coupling coefficient is calculated by quantifying the spatial aggregation degree of deformation rate and coverage evolution using the spatial autocorrelation Moran's I index, and the spatial evolution synchrony index is generated by cross-wavelet transform analysis of the time-phase relationship between deformation direction changes and coverage type conversions; Performing multi-dimensional fusion processing on the surface deformation rate data, the surface deformation direction data, the surface coverage evolution path data, and the geospatial association features to generate the spatio-temporal evolution feature set.

2. The method according to claim 1, wherein The performing dynamic parameter optimization processing on the spatio-temporal evolution feature set based on a preset basic spatio-temporal evolution model to generate a target spatio-temporal evolution model includes: Divide the spatio-temporal evolution feature set into a training feature subset and a verification feature subset. The training feature subset contains the spatio-temporal evolution features of the first N - 1 mapping periods, and the verification feature subset contains the spatio-temporal evolution features of the Nth mapping period; Input the training feature subset into the basic spatio-temporal evolution model for iterative training to generate an initial spatio-temporal evolution model. The iterative training includes operations for adjusting the weights of surface deformation features and matching the coverage evolution paths; Call the verification feature subset to perform a prediction accuracy verification process on the initial spatio-temporal evolution model to obtain model error distribution data, which includes deformation prediction error rates and coverage trend deviation degrees; Perform a reverse correction process on the feature mapping parameters of the initial spatio-temporal evolution model according to the model error distribution data to generate the target spatio-temporal evolution model.

3. The method according to claim 2, wherein The calling of the target spatio-temporal evolution model to perform an evolution prediction process on the surface mapping data set of the current mapping period to generate the spatio-temporal evolution prediction result of the target geographical area includes: Perform real-time feature extraction on the surface mapping data set of the current mapping period to obtain the current surface deformation features and the current coverage evolution features; Input the current surface deformation features and the current coverage evolution features into the target spatio-temporal evolution model to generate a preliminary prediction result through multi-level feature mapping; Perform geographical space constraint processing on the preliminary prediction result. The geographical space constraint processing includes terrain continuity verification operations and coverage type logic verification operations to obtain a verified prediction result; Generate the surface deformation prediction distribution data and the surface coverage change trend data according to the verified prediction result, and perform a trend coherence analysis process on the surface deformation prediction distribution data and the historical deformation data to generate a trend correction coefficient; Perform dynamic calibration processing on the surface deformation prediction distribution data based on the trend correction coefficient to obtain the spatio-temporal evolution prediction result of the target geographical area.

4. The method according to claim 1, characterized in that, The dynamic update process of the target spatio-temporal evolution model according to the spatio-temporal evolution prediction result to obtain an updated spatio-temporal evolution model includes: Obtain the updated actual surface mapping data set after the end of the current mapping period, and perform feature extraction on the actual surface mapping data set to generate actual spatio-temporal evolution features; Perform a difference comparison process on the spatio-temporal evolution prediction result and the actual spatio-temporal evolution features to generate a model error feature set, which includes a deformation prediction error vector and a coverage trend offset; Perform an incremental adjustment process on the mapping parameters of the target spatio-temporal evolution model according to the model error feature set to obtain adjusted mapping parameters. The incremental adjustment process uses a sliding window weight update mechanism; Synchronize the adjusted mapping parameters to the target spatio-temporal evolution model to generate the updated spatio-temporal evolution model, and delete the original mapping parameters in the basic spatio-temporal evolution model that exceed the preset time threshold.

5. The method according to claim 4, characterized in that, The performing of feature extraction on the actual surface mapping data set to generate actual spatio-temporal evolution features includes: Perform deformation gradient calculation and processing on the surface elevation data in the actual surface mapping dataset to generate actual surface deformation rate data; Perform coverage change path analysis and processing on the surface coverage type data in the actual surface mapping dataset to generate actual coverage evolution path data; Perform spatial superposition processing on the actual surface deformation rate data and the historical surface deformation rate data to generate actual deformation spatial correlation features; Perform time series alignment processing on the actual coverage evolution path data and the historical coverage evolution path data to generate actual coverage time correlation features; Perform coupling processing on the actual deformation spatial correlation features and the actual coverage time correlation features to generate the actual spatio-temporal evolution features.

6. The method according to claim 5, wherein The difference comparison processing of the spatio-temporal evolution prediction result and the actual spatio-temporal evolution features generates a model error feature set, including: Extract the predicted surface deformation rate data in the spatio-temporal evolution prediction result, and perform spatial grid comparison processing with the actual surface deformation rate data to generate the deformation rate difference value corresponding to each spatial grid unit; Extract the predicted coverage change trend data in the spatio-temporal evolution prediction result, and perform time node comparison processing with the actual coverage evolution path data to generate the coverage type matching degree corresponding to each time node; Perform spatial clustering analysis processing on the deformation rate difference values to generate a deformation error spatial distribution pattern; Perform time series analysis processing on the coverage type matching degrees to generate a coverage error time evolution pattern; Perform joint coding processing on the deformation error spatial distribution pattern and the coverage error time evolution pattern to generate the model error feature set; The incremental adjustment processing of the mapping parameters of the target spatio-temporal evolution model according to the model error feature set obtains the adjusted mapping parameters, including: Convert the deformation error spatial distribution pattern into a deformation error weight matrix, and each element in the deformation error weight matrix corresponds to an error influence coefficient of a spatial grid unit; Convert the coverage error time evolution pattern into a coverage error time vector, and each element in the coverage error time vector corresponds to an error accumulation amount of a time node; Perform gradient descent adjustment processing on the deformation mapping layer parameters in the target spatio-temporal evolution model based on the deformation error weight matrix; Perform time decay adjustment processing on the coverage mapping layer parameters in the target spatio-temporal evolution model based on the coverage error time vector; Perform joint optimization processing on the adjusted deformation mapping layer parameters and coverage mapping layer parameters to generate the adjusted mapping parameters.

7. The method according to claim 4, wherein The synchronization of the adjusted mapping parameters to the target spatio-temporal evolution model generates the updated spatio-temporal evolution model, including: Create a mapping parameter version identifier corresponding to the adjusted mapping parameters, and the mapping parameter version identifier includes a parameter generation timestamp and a spatial grid code; Perform binding processing on the adjusted mapping parameters and the mapping parameter version identifier to generate a set of mapping parameters with version identifiers; Perform version timeliness detection processing on the set of historical mapping parameters stored in the target spatio-temporal evolution model. The version timeliness detection processing is based on whether the time difference between the timestamp generated based on the parameters and the current mapping period exceeds a preset threshold. Delete the expired mapping parameters in the set of historical mapping parameters whose time difference exceeds the preset threshold, and retain the historical mapping parameters that do not exceed the threshold as reference parameters. Write the set of mapping parameters with version identifiers into the parameter storage space of the target spatio-temporal evolution model, and perform spatial grid coding alignment processing with the reference parameters. Perform compatibility verification processing on all mapping parameters in the parameter storage space. The compatibility verification processing includes spatial grid coding consistency verification and parameter generation timestamp continuity verification. When it is detected that there are multiple valid mapping parameter versions under the same spatial grid coding, extract the parameter generation timestamps of each version and perform difference sorting processing with the timestamp of the current mapping period. Select the mapping parameter version with the smallest time difference from the current mapping period timestamp according to the difference sorting result, and mark the remaining version parameters as pending archival status. Perform spatial overlay fusion processing on the selected mapping parameter version and the reference parameters to generate a fused updated set of mapping parameters. Overwrite the original parameter storage area of the target spatio-temporal evolution model with the updated set of mapping parameters to complete the model update operation and generate the updated spatio-temporal evolution model.

8. The method according to claim 1, characterized in that In the evolution prediction processing of synchronizing the updated spatio-temporal evolution model to the next mapping period, it includes: At the starting time node of the next mapping period, extract the updated set of mapping parameters from the original parameter storage area of the target spatio-temporal evolution model. Identify the mapping parameter version identifiers of each parameter in the extracted updated set of mapping parameters, and parse the parameter generation timestamp and spatial grid coding included in the mapping parameter version identifier. Perform matching degree calculation processing on the parameter generation timestamp and the prediction start time of the next mapping period to generate a time matching coefficient matrix. Perform validity screening processing on the extracted updated set of mapping parameters according to the time matching coefficient matrix, and retain the valid parameters whose time matching coefficient is greater than the set threshold. Perform grid-by-grid matching processing on the spatial grid coding of the valid parameters and the geographical spatial grid division data of the next mapping period. When it is detected that there are no valid parameters corresponding to the target spatial grid coding, perform spatial interpolation and complementation processing using the valid parameters of adjacent spatial grid coding. Load the complemented valid parameters into the real-time prediction operation module of the basic spatio-temporal evolution model in the order of spatial grid coding. Generate an index table of the mapping relationship between the mapping parameter version identifier of the valid parameters and the time nodes of the next mapping period. The mapping relationship index table includes spatial grid coding, parameter version identifier, and loading timestamp. During the evolution prediction processing of the next mapping period, continuously monitor the time difference between the timestamp of each parameter version identifier in the mapping relationship index table and the current prediction time node. When the time difference exceeds a preset synchronization threshold, a parameter version update request is triggered and the latest mapped parameter version is extracted from the parameter storage area of the updated spatio-temporal evolution model; The extracted latest mapped parameter version is replaced in the real-time prediction operation module according to the spatial grid encoding, and the corresponding entry in the mapping relationship index table is synchronously updated; After all prediction processes in the next mapping cycle are completed, the mapped parameter version identifier actually used and the corresponding prediction results are associated and stored in the model log database.

9. A spatio-temporal evolution modeling system, characterized in that, It includes a processor, a memory and a bus connected to the processor; wherein, the processor and the memory complete communication with each other through the bus; the processor is used to call program instructions in the memory to execute the ground mapping-driven spatio-temporal evolution modeling method according to any one of claims 1-8.

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