GIS-based geological disaster monitoring point data expression method and system

Abstract

The invention relates to the technical field of data expression, in particular to a GIS-based geological disaster monitoring point data expression method and system, and the method comprises the following steps: obtaining the detection data of geological disaster monitoring points, calling the space coordinates of the monitoring points and the corresponding monitoring data, calculating the spatial correlation according to the spatial distance between the monitoring points, and calculating the spatial correlation; and calculating monitoring data of the uncollected points according to the spatial correlation, and generating an interpolation monitoring value. According to the method, the monitoring data of the non-collection points are calculated according to the spatial correlation among the monitoring points, so that the integrity of the spatial data is improved, monitoring information loss caused by incomplete data collection is avoided, the local density of the monitoring points is analyzed, different grid scales are divided according to density characteristics, and the partition expression precision of the data is improved; based on the space coordinates, the timestamps and the disaster intensity data of the monitoring points, the multi-level visual expression is constructed, so that the monitoring information is intuitive, and the interpretability of the disaster monitoring data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data expression, and in particular to a method and system for expressing geological disaster monitoring point data based on GIS. Background Art

[0002] The field of data expression technology involves the organization, encoding, storage, conversion and visualization of data to achieve efficient data management and information transmission. This field covers a variety of data modeling methods, structured and unstructured data expression methods, data format conversion, spatial data expression, visualization technology and information retrieval optimization. It is widely used in geographic information systems, computer graphics, database management, machine learning, engineering simulation and other fields to improve data readability, operability and decision support capabilities through data structure optimization and graphical expression.

[0003] Among them, the geological disaster monitoring point data expression method is used to organize and visualize the spatial and attribute data of geological disaster monitoring points to support monitoring, analysis, early warning and management. This method usually combines GIS, three-dimensional modeling, and spatial database technology to achieve multi-level expression of geological disaster information, including monitoring point locations, geological environmental parameters, disaster evolution trends, etc. Its main uses include disaster risk assessment, monitoring data sharing, decision support and emergency management, and improve the scientificity and real-time nature of geological disaster monitoring.

[0004] The traditional expression method has defects in data integrity and fails to effectively fill the data gaps of uncollected points, resulting in missing information in areas with uneven distribution of monitoring points, affecting the spatial continuity of the overall data. The data organization method does not extract the characteristic principal components, resulting in high data redundancy, affecting the calculation efficiency. At the same time, the local density-based grid division method is not adopted when classifying data, and it is impossible to perform differentiated processing for high-density and low-density areas, resulting in over-simplification of data expression in high-density areas and insufficient details in low-density areas, affecting the refined expression ability of monitoring data. In terms of visual expression, after the data is mapped to the GIS platform, it lacks the ability to display the hierarchical space and time, making it difficult to intuitively display the spatiotemporal change trend of monitoring data, affecting the practicality of the data in disaster monitoring, risk assessment and decision analysis. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for expressing geological disaster monitoring point data based on GIS.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for expressing geological disaster monitoring point data based on GIS, comprising the following steps: S1: Obtain the detection data of the geological disaster monitoring point, call the spatial coordinates of the monitoring point and the corresponding monitoring data, calculate the spatial correlation according to the spatial distance between the monitoring points, calculate the monitoring data of the uncollected points according to the spatial correlation, and generate the interpolated monitoring value; S2: Based on the interpolated monitoring value, obtain the time series data of the monitoring point, align the data according to the time series, analyze the change trend of adjacent moments, call the state estimation value of the time series, adjust the interpolated monitoring value according to the change trend of the time series, and obtain the time series adjusted monitoring value; S3: Based on the time series, the monitoring value is adjusted, and the distance from each monitoring point to the cluster center is calculated according to the set number of clusters, and the monitoring points are classified into corresponding clusters according to the shortest distance, and the classified monitoring data is called to calculate the feature change range, extract the feature principal component, and generate compressed monitoring data based on the feature principal component dimensionality reduction; S4: Based on the compressed monitoring data, calculate the local density of the monitoring points, analyze the spatial distribution characteristics, divide the grid according to the local density, and re-divide the grid according to the division scale of the differentiated area to obtain gridded monitoring data.

[0007] The improvements of the present invention are that the interpolated monitoring values ​​include spatial prediction points, monitoring parameter interpolation values ​​and spatial correlation weights, the timing adjusted monitoring values ​​include time alignment data, state estimation values ​​and dynamically completed monitoring parameters, the compressed monitoring data include cluster center points, characteristic principal components and dimensionality reduction monitoring parameter sets, and the gridded monitoring data include high-density regional grids, low-density regional grids and disaster risk distribution units.

[0008] The present invention is improved in that the step of obtaining the interpolation monitoring value is specifically as follows: S101: Acquire detection data of geological disaster monitoring points, call the spatial coordinates of the monitoring points and corresponding monitoring data, including spatial coordinates, geological activity intensity, rainfall and soil moisture, establish a monitoring point set according to the distribution range of the spatial coordinates, filter outliers according to the parameter distribution in the monitoring point set, call the outliers to perform deviation calculation, adjust the parameter interval of the monitoring point data according to the calculated deviation, and obtain a monitoring data set; S102: Based on the monitoring data set, calculate the spatial distance between the monitoring points, call the spatial coordinates of the monitoring points, calculate the correlation weights between the monitoring points according to the distances between the coordinate points, compare the correlation weights of the monitoring points with the preset correlation threshold, screen the monitoring point pairs with strong spatial correlation, call the screened monitoring points, calculate the correlation contribution values, adjust the influence weights between the monitoring points according to the contribution values, and obtain the spatial correlation weights; S103: Based on the spatial correlation weight, the spatial coordinates of the uncollected points are called, and the monitoring parameters of the uncollected points are calculated according to the monitoring data of the collected monitoring points and the correlation weight, so as to generate interpolated monitoring values.

[0009] The present invention is improved in that the formula for calculating the monitoring parameters of the uncollected points is: ; in, represents the interpolation of monitoring parameters of uncollected point j, represents the spatial correlation weight between monitoring point i and uncollected point j, represents the monitoring data value of the i-th collected monitoring point, and Respectively represent the spatial coordinate position values ​​of the i-th collected monitoring point and the uncollected monitoring point, represents a numerically stable constant, Represents the number of collected monitoring points.

[0010] The present invention is improved in that the step of obtaining the timing adjustment monitoring value is specifically as follows: S201: Based on the interpolated monitoring value, combined with the monitoring point time series data index, the monitoring point coordinates and monitoring data in the time series are called, the monitoring point data list is sorted according to the time sequence, the time step is calculated according to the collection time interval of adjacent time points, the time step is called to filter the time interval with a large data fluctuation range, and the data sampling density is adjusted according to the time interval to obtain a time series data set; S202: Based on the time series data set, call the monitoring data in the time series, calculate the value change rate of the monitoring point at different time points, call the change rate to calculate the similarity of adjacent time points, filter the time period with stable data change trend in the time series according to the similarity, call the data in the stable time period to generate a smooth change curve, and obtain the time series trend feature; S203: Based on the time series trend characteristics and interpolated monitoring values, calculate the deviation of the monitoring data in the time series, call the deviation to adjust the state estimation value of the monitoring data, correct the monitoring data in the time series according to the state estimation value, call the corrected data to generate a time series interpolation result, and obtain the time series adjusted monitoring value.

[0011] The present invention is improved in that the step of obtaining the compressed monitoring data is specifically as follows: S301: Based on the time series, the monitoring values ​​are adjusted to obtain the spatial coordinates, disaster intensity and rainfall monitoring indicators of the monitoring points, the spatial coordinates of the monitoring points are used to calculate the Euclidean distance between the monitoring points, a monitoring point distance matrix is ​​constructed based on the Euclidean distance, the distance matrix is ​​used to filter pairs of monitoring points with close spatial distances, and a spatial relationship mapping of the monitoring points is established based on the screening results to obtain the spatial distance distribution of the monitoring points; S302: Based on the spatial distance distribution of the monitoring points, the set number of clusters is called to calculate the distance from the monitoring point to each cluster center, the cluster center with the shortest distance is selected according to the calculation result, the screening result is called to classify the monitoring points, the distribution range of the monitoring points in each cluster is adjusted according to the classification result, the adjusted cluster information is called to establish the classification result of the monitoring points, and the spatial cluster distribution of the monitoring points is obtained; S303: Based on the spatial cluster distribution of the monitoring points, the classified monitoring data is called to calculate the feature change range, the key influencing factors of the monitoring points are extracted according to the feature change range, the key influencing factors are called to perform dimensionality reduction operations, the characteristic principal components are screened according to the dimensionality reduction operation results, the characteristic principal components are called to reconstruct the monitoring point data table, and compressed monitoring data are generated.

[0012] The present invention is improved in that the steps of acquiring the grid monitoring data are specifically as follows: S401: Based on the compressed monitoring data, the spatial coordinates and disaster intensity data of the monitoring points are obtained, the spatial coordinates of the monitoring points are used to calculate the distance between adjacent monitoring points, the number density of local monitoring points is calculated based on the distance, the number density of monitoring points is used to filter the areas where the concentration of monitoring points in the area exceeds a preset density threshold, and the local density distribution of the monitoring points is established based on the screening results to obtain the local density value of the monitoring points; S402: Based on the local density value of the monitoring point, call the spatial distribution data of the monitoring point in the area, divide the dense area and the sparse area according to the local density range, call the distribution boundaries of the dense area and the sparse area to calculate the area division threshold, adjust the spatial range of each area according to the division threshold, call the adjusted area division result to establish a differentiated density area, and obtain the area density division result; S403: Based on the regional density division result, grid division is performed according to the local density, the grid scale is calculated, a small grid division scale is set for the dense area, and a large grid division scale is set for the sparse area, grid division information is generated according to the grid scale of each area, and the grid division scheme is called to readjust the grid range to which the monitoring point belongs, and the spatial mapping relationship of the monitoring data is reconstructed according to the adjustment result to obtain gridded monitoring data; The formula for calculating the grid size is: ; in, Representative The grid size value of the region, represents the scaling constant, Representative The local density value of the region, represents a small constant that prevents the denominator from being zero, Representative The average disaster intensity value of the region is represents the average value of regional disaster intensity, Represents the standard deviation of regional disaster intensity.

[0013] The present invention is improved in that the method further comprises: S5: Based on the grid monitoring data, the data is mapped to the GIS platform according to the spatial coordinates, the map hierarchical processing of the GIS platform is called, the monitoring point data is displayed according to the spatial distribution and time change, and the geological disaster data visualization expression information is generated; The geological disaster data visualization expression information includes spatial mapping monitoring points, time evolution curves and disaster intensity distribution maps.

[0014] The present invention is improved in that the steps of obtaining the visual expression information of geological disaster data are specifically as follows: S501: Based on the gridded monitoring data, the spatial coordinates, timestamps and disaster intensity data of the monitoring points are obtained, the spatial coordinate data are called to match the geographic reference coordinate system of the GIS platform, the longitude and latitude conversion values ​​of the monitoring points are calculated according to the geographic reference coordinate system, the converted longitude and latitude data are called to be mapped to the spatial database of the GIS platform, and the GIS mapping monitoring data is obtained; S502: Based on the GIS mapping monitoring data, call the map level information of the GIS platform, calculate the map display range according to the spatial distribution of the monitoring points, call the map display range data to match the spatial coordinates of the monitoring points, adjust the map display ratio according to the distribution density of the spatial coordinates, divide the map level according to the adjusted ratio, and obtain the map level monitoring data; S503: Based on the hierarchical monitoring data of the map, call the timestamp and disaster intensity data of the monitoring point, calculate the time change trend of the monitoring point according to the timestamp, call the time change trend data to match the spatial coordinates, display the monitoring point data according to the spatial distribution and time change, and obtain the visual expression information of the geological disaster data.

[0015] A GIS-based geological disaster monitoring point data expression system, the GIS-based geological disaster monitoring point data expression system is used to execute the above-mentioned GIS-based geological disaster monitoring point data expression method, the system comprises: The data interpolation processing module obtains the detection data of the geological disaster monitoring points, calculates the monitoring data of the uncollected points based on the spatial correlation, and generates the interpolated monitoring values; The timing optimization processing module aligns the data according to the time series based on the interpolation monitoring value, analyzes the change trend of adjacent moments, calls the state estimation value of the time series, adjusts the interpolation monitoring value according to the change trend of the time series, and obtains the timing adjustment monitoring value; The data compression processing module adjusts the monitoring value based on the time series, classifies the monitoring points into corresponding clusters according to the shortest distance, calls the classified monitoring data to calculate the feature change range, extracts the feature principal component, reduces the dimension based on the feature principal component, and generates compressed monitoring data; The grid division module performs grid division according to the local density based on the compressed monitoring data, and re-divides the grid according to the division scale of the differentiated area to obtain gridded monitoring data; The data mapping expression module maps the data to the GIS platform based on the gridded monitoring data according to the spatial coordinates, displays the monitoring point data according to the spatial distribution and time changes, and generates visual expression information of geological disaster data.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the monitoring data of uncollected points are calculated based on the spatial correlation between monitoring points, so that the integrity of spatial data is improved, the missing of monitoring information caused by incomplete data collection is avoided, the interpolation monitoring value is dynamically adjusted in combination with time series data, the spatiotemporal consistency of monitoring data is enhanced, and the influence of single collection errors is reduced. By extracting characteristic principal components, the core characteristic information is retained while reducing data redundancy, the computing efficiency and storage efficiency are improved, the local density of monitoring points is analyzed, and different grid scales are divided according to density characteristics, so that high-density areas have refined processing capabilities, and low-density areas maintain an overall overview, the partition expression accuracy of data is improved, and it is ensured that the data expression can cover a large area and accurately characterize key areas. Based on the spatial coordinates, timestamps and disaster intensity data of the monitoring points, a multi-level visual expression is constructed, so that the monitoring information is intuitive, can reflect the changing trends in space and time, improve the interpretability of disaster monitoring data, and support multi-level data analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flow chart of the method of the present invention; Figure 2 A flow chart for obtaining interpolation monitoring values ​​for the present invention; Figure 3 A flow chart for obtaining a timing adjustment monitoring value for the present invention; Figure 4 A flow chart for obtaining compressed monitoring data according to the present invention; Figure 5 A flowchart for obtaining grid monitoring data according to the present invention; Figure 6 A flowchart for obtaining visual expression information of geological disaster data in the present invention; Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 The present invention provides a technical solution: a method for expressing geological disaster monitoring point data based on GIS, comprising the following steps: S1: Obtain the detection data of the geological disaster monitoring point, call the spatial coordinates of the monitoring point and the corresponding monitoring data, including spatial coordinates, geological activity intensity, rainfall and soil moisture, calculate the spatial correlation according to the spatial distance between the monitoring points, calculate the monitoring data of the uncollected points according to the spatial correlation, and generate the interpolated monitoring value; S2: Based on the interpolated monitoring value, obtain the time series data of the monitoring point, call the monitoring point coordinates and monitoring data in the time series, calculate the similarity between the data of multiple time points, align the data according to the time series, analyze the change trend of adjacent moments, call the state estimation value of the time series, adjust the interpolated monitoring value according to the change trend of the time series, and dynamically adjust it in combination with the observed data and the estimated data to obtain the time series adjusted monitoring value; S3: Based on the time series, the monitoring values ​​are adjusted to obtain the spatial coordinates, disaster intensity and rainfall monitoring indicators of the monitoring points. The Euclidean distance between the monitoring points is calculated according to the spatial coordinates. The distance from each monitoring point to the cluster center is calculated according to the set number of clusters. The monitoring points are classified into corresponding clusters according to the shortest distance. The classified monitoring data is called to calculate the feature change range, the feature principal components are extracted, and the dimension reduction is based on the feature principal components to generate compressed monitoring data. S4: Based on the compressed monitoring data, the spatial coordinates and disaster intensity data of the monitoring points are obtained, the local density of the monitoring points is calculated, the spatial distribution characteristics are analyzed, and the high-density area and low-density area are divided according to the local density. The high-density area is divided into small grids and the low-density area is divided into large grids. The grid is re-divided according to the division scale of the differentiated area to obtain the gridded monitoring data; S5: Based on the grid monitoring data, the spatial coordinates, timestamps and disaster intensity data of the monitoring points are obtained, the data are mapped to the GIS platform according to the spatial coordinates, the map hierarchical processing of the GIS platform is called, the monitoring point data is displayed according to the spatial distribution and time changes, and the visual expression information of geological disaster data is generated; Interpolation monitoring values ​​include spatial prediction points, monitoring parameter interpolation and spatial correlation weights; time-series adjustment monitoring values ​​include time-aligned data, state estimation values ​​and dynamically completed monitoring parameters; compressed monitoring data include cluster center points, characteristic principal components and dimensionality reduction monitoring parameter sets; gridded monitoring data include high-density regional grids, low-density regional grids and disaster risk distribution units; visualization expression of geological disaster data includes spatial mapping monitoring points, time evolution curves and disaster intensity distribution maps.

[0021] See also Figure 2 , the specific steps for obtaining the interpolation monitoring value are: S101: Acquire detection data of geological disaster monitoring points, call the spatial coordinates of the monitoring points and corresponding monitoring data, including spatial coordinates, geological activity intensity, rainfall and soil moisture, establish a monitoring point set according to the distribution range of the spatial coordinates, filter outliers according to the parameter distribution in the monitoring point set, call the outliers to perform deviation calculation, adjust the parameter interval of the monitoring point data according to the calculated deviation, and obtain a monitoring data set; The detection data of geological disaster monitoring points are obtained, including calling the spatial coordinates, geological activity intensity, rainfall and soil moisture of each monitoring point. According to the spatial coordinate values ​​of each monitoring point, the monitoring points are divided into multiple areas according to the longitude and latitude range, and a monitoring point set is established in each area. The monitoring point data sets in different areas are sorted and stored. For the data of each monitoring point, the time series changes of geological activity intensity, rainfall and soil moisture are calculated respectively, and the maximum value, minimum value, mean value and standard deviation in the past period of time are extracted to form a data feature set. For each data in the data feature set, the triple standard deviation method is used to screen outliers, that is, for each monitoring point data value , if it exceeds the mean value of the monitoring point Three times the standard deviation Range, i.e. , then the data is judged as an outlier, all outliers are recorded and an outlier data set is established, and the deviation of each outlier data point relative to the normal data is calculated. The deviation calculation adopts the absolute error calculation, that is, , and further adjust the weight of outliers according to the historical data trend of each monitoring point to reduce the impact of outliers on the overall monitoring data set, where the outlier weight The setting basis is the degree of deviation between the outlier and the mean, and the weight value is set according to the deviation ratio. For example, if the deviation between an outlier and the mean is , then its weight is set to , reducing the impact of outliers in an exponential decay manner. Relatively speaking, the weight of normal values 1, ensuring that most data contributes to the main information. , mean , standard deviation ,but , the corresponding weight calculation , that is, when calculating the corrected data, the influence of the abnormal data is negligible, and the corrected monitoring data value is calculated by weighted average, that is, , and finally obtain the corrected monitoring data set.

[0022] S102: Based on the monitoring data set, the spatial distance between the monitoring points is calculated, the spatial coordinates of the monitoring points are called, the correlation weights between the monitoring points are calculated according to the distances between the coordinate points, and the monitoring point correlation weights are compared with the preset correlation thresholds to screen the monitoring point pairs with strong spatial correlation, and the screened monitoring points are called to calculate the correlation contribution values, and the influence weights between the monitoring points are adjusted according to the contribution values ​​to obtain the spatial correlation weights; Based on the corrected monitoring data set, the spatial distance between each monitoring point is calculated, the spatial coordinates of each monitoring point are called, and the Euclidean distance formula is used to calculate the straight-line distance between each monitoring point. The distance calculation formula is: ,in For monitoring point With monitoring point The spatial distance between For monitoring point The spatial coordinate value of For monitoring point The spatial coordinate values ​​of are used to calculate the distance matrix between all monitoring points, and the correlation weights between the monitoring points are calculated based on the distance matrix. The correlation weights are calculated using the inverse proportional function, that is, ,in To prevent the smallest value of division by zero error, a correlation threshold is preset The setting basis is the average distance between monitoring points and the spatial variability of monitoring data, usually based on the coefficient of variation of monitoring data. Calculate, if the coefficient of variation is large, then appropriately increase the threshold to exclude points with low correlation at long distances, and set the coefficient of variation of monitoring points in a certain area , you can set , then, with In contrast, if , then determine the monitoring point and monitoring points For monitoring point pairs with strong spatial correlation, all monitoring point pairs that meet this condition are screened out, and the data of these monitoring points are called. The correlation contribution value is calculated based on the difference in monitoring data between each pair of monitoring points. The contribution value is calculated using the covariance formula ,Finally, based on the calculated contribution value, the influence weights between monitoring points are adjusted and the spatial correlation weight matrix is ​​updated.

[0023] S103: Based on the spatial correlation weight, the spatial coordinates of the uncollected points are called, and the monitoring parameters of the uncollected points are calculated according to the monitoring data of the collected monitoring points and the correlation weight, so as to generate interpolated monitoring values; The formula for calculating the monitoring parameters of uncollected points is: ; in, represents the interpolation of monitoring parameters of uncollected point j, represents the spatial correlation weight between monitoring point i and uncollected point j, represents the monitoring data value of the i-th collected monitoring point, and Respectively represent the spatial coordinate position values ​​of the i-th collected monitoring point and the uncollected monitoring point, represents a numerically stable constant, The number of monitoring points represented; Based on the spatial correlation weight, the spatial coordinates of the uncollected points are called, and the monitoring parameters of the uncollected points are calculated according to the data of the collected monitoring points and the correlation weight. The calculation process first calls the spatial coordinates and monitoring data values ​​of all collected monitoring points, and weights the data of each monitoring point according to the spatial correlation weight. The interpolation calculation formula is: ; in, Represents the numerical stability constant, which is set based on the numerical range of the spatial coordinates. In order to prevent the extremely small denominator from causing abnormal oscillation in the interpolation calculation, it is usually set 10% of the minimum space spacing is used as a stable term, and the minimum space spacing is set to 2. .

[0024] There are 5 monitoring points in the area, and their monitoring data values ​​are as follows: ; The corresponding spatial correlation weights are: ; Space coordinates: ; set up , then calculate: ; ; ; Final calculation: ; Therefore, no points were collected The interpolation calculation result of the monitoring parameters is 0.849.

[0025] See also Figure 3 , the specific steps for obtaining the timing adjustment monitoring value are: S201: Based on the interpolated monitoring value, combined with the monitoring point time series data index, the monitoring point coordinates and monitoring data in the time series are called, the monitoring point data list is sorted according to the time sequence, the time step is calculated according to the collection time interval of adjacent time points, the time step is called to filter the time interval with a large data fluctuation range, and the data sampling density is adjusted according to the time interval to obtain the time series data set; Based on the interpolated monitoring value, combined with the monitoring point time series data index, the monitoring point coordinates and monitoring data in the time series are called, and the data of each monitoring point is sorted into a monitoring point data list in chronological order. The time step is calculated based on the collection time of adjacent time points. The calculation method of each time step is the time difference between two adjacent sampling points, that is, ,in and Represent the timestamps of two consecutive time points respectively. The time step set is calculated for the data of all monitoring points, and the distribution range of the time step is counted to screen out the time interval with a large data fluctuation range. The basis for judging the fluctuation range is the coefficient of variation (CV) of adjacent time steps, which is calculated as follows: ,in is the standard deviation of the time step, Set the threshold as the mean of the time steps Used to determine the interval with large fluctuations. , then it is determined that the time interval fluctuates greatly, and the threshold According to the distribution range of historical data, for example, if the mean time step length in a certain area is Minutes, standard deviation Minutes, then the coefficient of variation If you set , then the time interval is judged to have large fluctuations, and the data sampling density is adjusted for the time interval with large fluctuations. If the time step exceeds the set threshold, such as the maximum allowed step If the time step is longer than the set time step, data sampling points are added within the interval and additional time sampling points are generated by linear interpolation so that the time step meets the set range, and finally a time series data set is obtained.

[0026] S202: Based on the time series data set, call the monitoring data in the time series, calculate the value change rate of the monitoring point at different time points, call the change rate to calculate the similarity of adjacent time points, select the time period with stable data change trend in the time series according to the similarity, call the data in the stable time period to generate a smooth change curve, and obtain the time series trend characteristics; Based on the time series data set, the monitoring data in the time series is called to calculate the value change rate of the monitoring point at different time points. The change rate of each monitoring point is calculated as follows: ,in and The time and The monitoring data values ​​are calculated, and the change rate set of all monitoring points at different time points is calculated. The similarity of adjacent time points is calculated based on the change rate. The similarity calculation uses the cosine similarity formula ,in and Set the similarity threshold as the change rate of two adjacent time points respectively. ,like , then the data change trend in this time period is determined to be stable, and the threshold According to the mean value of the rate of change, for example, if the mean value of the rate of change in a certain area is , the standard deviation is , then set In order to screen out stable time periods, all time period data that meet the similarity threshold are called to generate a smooth change curve. The smooth curve is generated using the weighted moving average method, and the weight is set according to the similarity. If the similarity at a certain time point is high, a higher weight is given, and finally the time series trend characteristics are obtained.

[0027] S203: Based on the time series trend characteristics and the interpolated monitoring value, the deviation of the monitoring data in the time series is calculated, the state estimation value of the monitoring data is adjusted by calling the deviation, the monitoring data in the time series is corrected according to the state estimation value, and the corrected data is called to generate the time series interpolation result to obtain the time series adjusted monitoring value; Based on the time series trend characteristics and interpolated monitoring values, the deviation of the monitoring data in the time series is calculated. The deviation is calculated as follows: ,in To monitor data values, is the time series trend feature in time The value of , calculates the deviation set of all time points, and calls the deviation to adjust the state estimation value of the monitoring data. The adjustment of the state estimation value is based on the deviation compensation coefficient , which is calculated as ,in is the maximum absolute value of the deviation at all time points. If the deviation at a certain time point is large, the compensation coefficient is small to reduce the weight influence of the time point. If the maximum deviation in a certain time period is set to 5 and the deviation at a certain time point is 2, the compensation coefficient at that time point is calculated as , the data correction after compensation is calculated as , the monitoring data in the time series is corrected according to the state estimation value, and the corrected data is called to generate the time series interpolation result, and finally the time series adjusted monitoring value is obtained.

[0028] See also Figure 4 ,The specific steps for obtaining compressed monitoring data are: S301: Based on the time series adjustment monitoring value, the spatial coordinates, disaster intensity and rainfall monitoring indicators of the monitoring points are obtained, the spatial coordinates of the monitoring points are called to calculate the Euclidean distance between the monitoring points, and the monitoring point distance matrix is ​​constructed according to the Euclidean distance. The distance matrix is ​​called to filter the monitoring point pairs with close spatial distances, and the spatial relationship mapping of the monitoring points is established according to the screening results to obtain the spatial distance distribution of the monitoring points; Based on the time series adjustment monitoring value, the spatial coordinates of the monitoring points, the disaster intensity and rainfall monitoring indicators are obtained, and the spatial coordinates of the monitoring points are called to calculate the Euclidean distance between the monitoring points. The calculation method of the Euclidean distance is ,in For monitoring point With monitoring point The Euclidean distance between and Monitoring points and The spatial coordinate values ​​are used to calculate the Euclidean distance between all monitoring points, construct a monitoring point distance matrix, and call the distance matrix to filter monitoring point pairs with close spatial distances. The screening criteria are based on the preset distance threshold. , which is set to 70% to 90% of the average distance between monitoring points in the area. For example, if the average distance between all monitoring points in a certain area is 10 kilometers, then Kilometres is used to screen out monitoring points that are close to each other, and the screening results are used to establish a spatial relationship mapping of the monitoring points, build the connection relationship between adjacent monitoring points, and finally obtain the spatial distance distribution of the monitoring points.

[0029] S302: Based on the spatial distance distribution of the monitoring points, the set number of clusters is called to calculate the distance from the monitoring point to each cluster center, the cluster center with the shortest distance is selected according to the calculation result, the screening result is called to classify the monitoring points, the distribution range of the monitoring points in each cluster is adjusted according to the classification result, the adjusted cluster information is called to establish the classification result of the monitoring points, and the spatial cluster distribution of the monitoring points is obtained; Based on the spatial distance distribution of the monitoring points, the set number of clusters is called to calculate the distance from the monitoring point to the center of each cluster. The calculation method also uses the Euclidean distance formula ,in For monitoring point To cluster center The distance is the spatial coordinate of the cluster center, calculate the distance set from all monitoring points to the cluster center, and select the cluster center with the shortest distance based on the calculation results. The selection of the shortest distance is based on the set cluster convergence threshold , which is set by the mean of the distance between monitoring points within a cluster plus the standard deviation. For example, if the mean distance between monitoring points within the initial clusters in a certain area is 8 kilometers and the standard deviation is 2 kilometers, then , in order to screen out the most reasonable cluster affiliation, call the screening results to classify the monitoring points, and adjust the distribution range of the monitoring points in each cluster according to the classification results. If the number of monitoring points in a cluster exceeds the preset upper limit , then the exceeded monitoring points are re-divided into adjacent clusters. The setting is based on the regional monitoring point density, which is generally 120% of the regional average density. For example, if the average monitoring point density in a certain area is 30 monitoring points / cluster, then set To ensure the reasonable distribution of clusters, call the adjusted cluster information to build S303: Based on the spatial cluster distribution of monitoring points, the classified monitoring data is called to calculate the feature change range, the key influencing factors of the monitoring points are extracted according to the feature change range, the key influencing factors are called to perform dimensionality reduction operation, the characteristic principal components are selected according to the dimensionality reduction operation results, the characteristic principal components are called to reconstruct the monitoring point data table, and compressed monitoring data is generated; Based on the spatial cluster distribution of monitoring points, the classified monitoring data is called to calculate the characteristic change range. The characteristic change range is calculated as follows: ,in For the monitoring data set of each monitoring point, calculate the characteristic change range set of all monitoring points, extract the key influencing factors of the monitoring points based on the characteristic change range, and extract the key influencing factors based on the contribution threshold , the contribution is calculated as ,in Features The variance of That is, the features with a contribution of more than 10% are selected as key influencing factors, and the key influencing factors are called for dimensionality reduction operations. The dimensionality reduction operation is based on the variance contribution rate of the main components of the features. The threshold for main component screening is The principal component whose cumulative variance contribution rate reaches 90% is set. For example, if the variance contribution rates of the first three principal components are 50%, 30% and 10% respectively, the first three principal components are retained for dimensionality reduction, and the characteristic principal components are selected according to the dimensionality reduction operation results. The characteristic principal components are called to reconstruct the monitoring point data table, and finally the compressed monitoring data is generated.

[0030] See also Figure 5 ,The specific steps for obtaining grid monitoring data are: S401: Based on the compressed monitoring data, the spatial coordinates and disaster intensity data of the monitoring points are obtained, the spatial coordinates of the monitoring points are used to calculate the distance between adjacent monitoring points, the number density of local monitoring points is calculated based on the distance, the number density of monitoring points is used to filter the areas where the concentration of monitoring points in the area exceeds the preset density threshold, and the local density distribution of monitoring points is established based on the screening results to obtain the local density value of the monitoring points; Based on the compressed monitoring data, the spatial coordinates of the monitoring points and the disaster intensity data are obtained, and the spatial coordinates of the monitoring points are called to calculate the distances between adjacent monitoring points. The calculation process uses the Euclidean distance formula. ,in and are the coordinates of monitoring points i and j respectively. The distances between all monitoring points are calculated pairwise to construct a distance matrix. The number density of each monitoring point is calculated based on this matrix. The number density is defined as the number of monitoring points per unit area. The calculation formula is: ,in is the distance between monitoring point i and The number of monitoring points within the range, The distance to the nearest neighbor monitoring point is calculated, and the number density of monitoring points is called to filter the monitoring points in the area where the concentration degree exceeds the preset density threshold. For areas with dense monitoring points, this threshold is set according to the average density of the area. For example, if the average monitoring point density in an area is 100 / km2, the density threshold can be set to 120 / km2. If the calculated density of an area is 140 / km2, which exceeds the threshold, the area is divided into a dense monitoring point area. The local density distribution of monitoring points is established based on the screening results, and finally the local density value of the monitoring points is obtained.

[0031] S402: Based on the local density value of the monitoring point, call the spatial distribution data of the monitoring point in the area, divide the dense area and the sparse area according to the local density range, call the distribution boundaries of the dense area and the sparse area to calculate the area division threshold, adjust the spatial range of each area according to the division threshold, call the adjusted area division result to establish a differentiated density area, and obtain the area density division result; Based on the local density value of the monitoring point, the spatial distribution data of the monitoring points in the area is called, and the dense area and sparse area are divided according to the local density range. The division process depends on the density comparison and the density interval threshold is set. If the density of a certain area is greater than this threshold, it is divided into a dense area, otherwise it is a sparse area. The threshold setting is based on the statistical analysis of the average density of the area. For example, if the average density of the area is 100 monitoring points / square kilometer, the threshold can be set to 80 monitoring points / square kilometer. The boundaries of dense and sparse areas are calculated to determine the optimal spatial distribution. Morphological operations are used to determine the regional boundaries, and the spatial range of each area is adjusted. The adjusted regional division results are called to establish differentiated density areas, and finally the regional density division results are obtained.

[0032] S403: Based on the regional density division result, grid division is performed according to the local density, the grid scale is calculated, a small grid division scale is set for the dense area, and a large grid division scale is set for the sparse area, grid division information is generated according to the grid scale of each area, and the grid division scheme is called to readjust the grid range to which the monitoring point belongs, and the spatial mapping relationship of the monitoring data is reconstructed according to the adjustment result to obtain gridded monitoring data; The formula for calculating the grid size is: ; in, Representative The grid size value of the region, represents the scaling constant, Representative The local density value of the region, represents a small constant that prevents the denominator from being zero, Representative The average disaster intensity value of the region is represents the average value of regional disaster intensity, represents the standard deviation of regional disaster intensity; Based on the regional density division results, the grid is divided according to the local density, and the formula for calculating the grid scale is: ; in, Representative The grid size value of the region, is the resizing constant, set to 1000 to ensure the scale is moderate. It is The local density value of the region, is a small amount to prevent division by zero errors, set to 0.01, It is The average disaster intensity value of the region is and They are the average and standard deviation of the disaster intensity in all regions respectively. The dense area is called to set a small grid division scale, and the sparse area is called to set a large grid division scale. The grid scale of each region is calculated according to the above formula to generate grid division information. The grid division scheme is called to readjust the grid range to which the monitoring point belongs. The spatial mapping relationship of the monitoring data is reconstructed based on the adjustment results, and finally the gridded monitoring data is obtained.

[0033] The monitoring point density and disaster intensity data of 5 sub-areas in a certain area are set as follows: local density ( ): , , , , (Unit: monitoring point / square kilometer); average disaster intensity in the area (Unit: disaster level); standard deviation of disaster intensity in the region ; The mean disaster intensity of each sub-region : , , , , ; Set adjustment constants , .

[0034] Enter the formula to calculate: ; ; ; ; ; Calculation results: Dense areas ( ): , ; Medium density areas ( ): , ; Sparse area ( ): ; A smaller grid scale is used in dense areas, a medium grid scale is used in medium-density areas, and a larger grid scale is used in sparse areas. The spatial mapping relationship of the monitoring data is reconstructed based on the adjustment results, and finally gridded monitoring data is obtained.

[0035] See also Figure 6,The specific steps for obtaining the visualization expression information of geological hazard data are as follows: S501: Based on the grid monitoring data, the spatial coordinates, timestamps and disaster intensity data of the monitoring points are obtained, the spatial coordinate data are called to match the geographic reference coordinate system of the GIS platform, the longitude and latitude conversion values ​​of the monitoring points are calculated according to the geographic reference coordinate system, the converted longitude and latitude data are called to be mapped to the spatial database of the GIS platform, and the GIS mapping monitoring data is obtained; Based on the grid monitoring data, call the stored spatial coordinates of the monitoring points, extract the timestamp and disaster intensity data, traverse the monitoring point data set, extract the spatial coordinate information of each monitoring point, including the longitude and latitude values, call the geographic reference coordinate system of the GIS platform, and convert the spatial coordinates of the monitoring points according to the coordinate system format requirements. The conversion method is to call the coordinate system parameters, calculate the conversion value of the geographic coordinates (WGS84) and the GIS platform coordinate system (GCJ02 or BD09), and perform projection transformation on the longitude and latitude data. Set the conversion accuracy threshold to 0.000001 to ensure that the coordinate deviation after conversion is less than the threshold, and call the conversion. The longitude and latitude data are obtained, and the converted coordinate information is mapped to the spatial database of the GIS platform. A monitoring point information table is established in the GIS database. The fields include the monitoring point number, longitude and latitude coordinates, timestamp and disaster intensity value. The coordinate mapping operation is performed on all monitoring points in turn to ensure that all monitoring point data are mapped in the GIS platform. The mapped data set is traversed, the mapping completion is checked, and the mapping error is calculated. The error calculation method is to call the difference between the original coordinates and the converted coordinates. The error threshold is set to 0.0001. If the error exceeds the threshold, the coordinate conversion is re-executed and the GIS database is updated to finally obtain the GIS mapped monitoring data.

[0036] S502: Based on GIS mapping monitoring data, call the map level information of the GIS platform, calculate the map display range according to the spatial distribution of the monitoring points, call the map display range data to match the spatial coordinates of the monitoring points, adjust the map display ratio according to the distribution density of the spatial coordinates, divide the map level according to the adjusted ratio, and obtain the map level monitoring data; Based on GIS mapping monitoring data, call the map level information of the GIS platform, traverse all monitoring point data, call the spatial coordinate data, calculate the geographical distribution range of the monitoring points, and the calculation method is to extract the minimum and maximum longitude and latitude values ​​of the monitoring points, calculate the longitude span and latitude span, and determine the map display range based on the span, set the minimum and maximum boundaries of the map display range, so that the monitoring point data are completely covered within the map display range, call the map display range data to match the spatial coordinates of the monitoring points, traverse the monitoring point data, and calculate the distribution density of the spatial coordinates within the map range. The calculation method is to divide the map range into 10 ×10 grid area, count the number of monitoring points in each grid, calculate the average monitoring point density of each grid, adjust the map display ratio according to the monitoring point density, set the map zoom ratio threshold to 1:5000 to 1:50000, if the density of monitoring points in a certain area grid exceeds 50, then reduce the map scale to 1:5000, if the density is less than 5, then enlarge the map scale to 1:50000, divide the map level according to the adjusted ratio, and set a multi-level scale range, each range corresponds to a different level, establish a hierarchical index for the adjusted map data, and finally obtain the map hierarchical monitoring data.

[0037] S503: Based on the hierarchical monitoring data of the map, the timestamp and disaster intensity data of the monitoring point are called, the time change trend of the monitoring point is calculated according to the timestamp, the time change trend data is called to match the spatial coordinates, the monitoring point data is displayed according to the spatial distribution and time change, and the visual expression information of the geological disaster data is obtained; Based on the hierarchical monitoring data of the map, the timestamp and disaster intensity data of the monitoring points are called, the monitoring point data set is traversed, the time series data of each monitoring point is extracted, and the time change trend of the monitoring point is calculated. The calculation method is to sort the disaster intensity data of the same monitoring point according to the timestamp, calculate the intensity difference of adjacent time points, and calculate its change rate. The change rate threshold is set to ±10%. If the change rate of three consecutive time points exceeds the threshold, the monitoring point is marked as an abnormal point. The time change trend data is called to match the spatial coordinates, and the time trend of the monitoring point data in the same area is compared. The time change mean of multiple monitoring points in the same area is calculated. The calculation method is to take a weighted average of the monitoring data with the same timestamp. The weighting factor is set as the normalized value of the spatial correlation weight. The monitoring point data is displayed according to the spatial distribution and time change. A time series visualization layer is established in the GIS platform. The monitoring data is sorted by timestamp, and the change trend of the disaster intensity is displayed in the form of a dynamic heat map or color gradient change. Finally, the visualization expression information of geological disaster data is obtained.

[0038] See also Figure 7A GIS-based geological disaster monitoring point data expression system, the GIS-based geological disaster monitoring point data expression system is used to execute the above-mentioned GIS-based geological disaster monitoring point data expression method, the system includes: The data interpolation processing module obtains the detection data of the geological disaster monitoring point, calls the spatial coordinates of the monitoring point and the corresponding monitoring data, calculates the spatial correlation according to the spatial distance between the monitoring points, calculates the monitoring data of the uncollected points according to the spatial correlation, and generates the interpolated monitoring value; The timing optimization processing module obtains the time series data of the monitoring point based on the interpolated monitoring value, aligns the data according to the time series, analyzes the change trend of adjacent moments, calls the state estimation value of the time series, adjusts the interpolated monitoring value according to the change trend of the time series, and obtains the timing adjustment monitoring value; The data compression processing module adjusts the monitoring value based on the time series, calculates the distance from each monitoring point to the cluster center according to the set number of clusters, classifies the monitoring points into corresponding clusters according to the shortest distance, calls the classified monitoring data to calculate the feature change range, extracts the feature principal components, and generates compressed monitoring data based on the feature principal components. The grid division module calculates the local density of monitoring points based on compressed monitoring data, analyzes the spatial distribution characteristics, divides the grid according to the local density, and re-divides the grid according to the division scale of the differentiated area to obtain gridded monitoring data; The data mapping expression module is based on grid monitoring data. It maps the data to the GIS platform according to spatial coordinates, calls the map hierarchical processing of the GIS platform, displays the monitoring point data according to spatial distribution and temporal changes, and generates visual expression information of geological disaster data.

[0039] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for expressing geological disaster monitoring point data based on GIS, characterized in that: The following steps are involved: S1: Obtain the detection data of the geological disaster monitoring point, call the spatial coordinates of the monitoring point and the corresponding monitoring data, calculate the spatial correlation according to the spatial distance between the monitoring points, calculate the monitoring data of the uncollected points according to the spatial correlation, and generate the interpolated monitoring value; S2: Based on the interpolated monitoring value, obtain the time series data of the monitoring point, align the data according to the time series, analyze the change trend of adjacent moments, call the state estimation value of the time series, adjust the interpolated monitoring value according to the change trend of the time series, and obtain the time series adjusted monitoring value; S3: Based on the time series, the monitoring value is adjusted, and the distance from each monitoring point to the cluster center is calculated according to the set number of clusters, and the monitoring points are classified into corresponding clusters according to the shortest distance, and the classified monitoring data is called to calculate the feature change range, extract the feature principal component, and generate compressed monitoring data based on the feature principal component dimensionality reduction; S4: Based on the compressed monitoring data, calculate the local density of the monitoring points, analyze the spatial distribution characteristics, divide the grid according to the local density, and re-divide the grid according to the division scale of the differentiated area to obtain gridded monitoring data.

2. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The interpolated monitoring values ​​include spatial prediction points, monitoring parameter interpolation and spatial correlation weights; the timing-adjusted monitoring values ​​include time-aligned data, state estimation values ​​and dynamically completed monitoring parameters; the compressed monitoring data include cluster center points, characteristic principal components and dimensionality reduction monitoring parameter sets; the gridded monitoring data include high-density regional grids, low-density regional grids and disaster risk distribution units.

3. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The steps for obtaining the interpolation monitoring value are specifically as follows: S101: Acquire detection data of geological disaster monitoring points, call the spatial coordinates of the monitoring points and corresponding monitoring data, including spatial coordinates, geological activity intensity, rainfall and soil moisture, establish a monitoring point set according to the distribution range of the spatial coordinates, filter outliers according to the parameter distribution in the monitoring point set, call the outliers to perform deviation calculation, adjust the parameter interval of the monitoring point data according to the calculated deviation, and obtain a monitoring data set; S102: Based on the monitoring data set, calculate the spatial distance between the monitoring points, call the spatial coordinates of the monitoring points, calculate the correlation weights between the monitoring points according to the distances between the coordinate points, compare the correlation weights of the monitoring points with the preset correlation threshold, screen the monitoring point pairs with strong spatial correlation, call the screened monitoring points, calculate the correlation contribution values, adjust the influence weights between the monitoring points according to the contribution values, and obtain the spatial correlation weights; S103: Based on the spatial correlation weight, the spatial coordinates of the uncollected points are called, and the monitoring parameters of the uncollected points are calculated according to the monitoring data of the collected monitoring points and the correlation weight, so as to generate interpolated monitoring values.

4. The method for expressing geological disaster monitoring point data based on GIS according to claim 3 is characterized in that: The formula for calculating the monitoring parameters of the uncollected points is: ； in, represents the interpolation of monitoring parameters of uncollected point j, represents the spatial correlation weight between monitoring point i and uncollected point j, represents the monitoring data value of the i-th collected monitoring point, and Respectively represent the spatial coordinate position values ​​of the i-th collected monitoring point and the uncollected monitoring point, represents a numerically stable constant, Represents the number of collected monitoring points.

5. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The steps for obtaining the timing adjustment monitoring value are specifically as follows: S201: Based on the interpolated monitoring value, combined with the monitoring point time series data index, call the monitoring point coordinates and monitoring data in the time series, sort the monitoring point data list according to the time sequence, calculate the time step according to the collection time interval of adjacent time points, call the time step to filter the time interval of data fluctuation, adjust the data sampling density according to the time interval, and obtain the time series data set; S202: Based on the time series data set, call the monitoring data in the time series, calculate the value change rate of the monitoring point at different time points, call the change rate to calculate the similarity of adjacent time points, filter the time period with stable data change trend in the time series according to the similarity, call the data in the stable time period to generate a smooth change curve, and obtain the time series trend feature; S203: Based on the time series trend characteristics and interpolated monitoring values, calculate the deviation of the monitoring data in the time series, call the deviation to adjust the state estimation value of the monitoring data, correct the monitoring data in the time series according to the state estimation value, call the corrected data to generate a time series interpolation result, and obtain the time series adjusted monitoring value.

6. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The steps for obtaining the compressed monitoring data are specifically as follows: S301: Based on the time series, the monitoring values ​​are adjusted to obtain the spatial coordinates, disaster intensity and rainfall monitoring indicators of the monitoring points, the spatial coordinates of the monitoring points are used to calculate the Euclidean distance between the monitoring points, a monitoring point distance matrix is ​​constructed based on the Euclidean distance, the distance matrix is ​​used to filter pairs of monitoring points with close spatial distances, and a spatial relationship mapping of the monitoring points is established based on the screening results to obtain the spatial distance distribution of the monitoring points; S302: Based on the spatial distance distribution of the monitoring points, the set number of clusters is called to calculate the distance from the monitoring point to each cluster center, the cluster center with the shortest distance is selected according to the calculation result, the screening result is called to classify the monitoring points, the distribution range of the monitoring points in each cluster is adjusted according to the classification result, the adjusted cluster information is called to establish the classification result of the monitoring points, and the spatial cluster distribution of the monitoring points is obtained; S303: Based on the spatial cluster distribution of the monitoring points, the classified monitoring data is called to calculate the feature change range, the key influencing factors of the monitoring points are extracted according to the feature change range, the key influencing factors are called to perform dimensionality reduction operations, the characteristic principal components are screened according to the dimensionality reduction operation results, the characteristic principal components are called to reconstruct the monitoring point data table, and compressed monitoring data are generated.

7. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The steps for obtaining the grid monitoring data are specifically as follows: S401: Based on the compressed monitoring data, the spatial coordinates and disaster intensity data of the monitoring points are obtained, the spatial coordinates of the monitoring points are used to calculate the distance between adjacent monitoring points, the number density of local monitoring points is calculated based on the distance, the number density of monitoring points is used to filter the areas where the concentration of monitoring points in the area exceeds a preset density threshold, and the local density distribution of the monitoring points is established based on the screening results to obtain the local density value of the monitoring points; S402: Based on the local density value of the monitoring point, call the spatial distribution data of the monitoring point in the area, divide the dense area and the sparse area according to the local density range, call the distribution boundaries of the dense area and the sparse area to calculate the area division threshold, adjust the spatial range of each area according to the division threshold, call the adjusted area division result to establish a differentiated density area, and obtain the area density division result; S403: Based on the regional density division result, grid division is performed according to the local density, the grid scale is calculated, a small grid division scale is set for the dense area, and a large grid division scale is set for the sparse area, grid division information is generated according to the grid scale of each area, and the grid division scheme is called to readjust the grid range to which the monitoring point belongs, and the spatial mapping relationship of the monitoring data is reconstructed according to the adjustment result to obtain gridded monitoring data; The formula for calculating the grid size is: ； in, Representative The grid size value of the region, represents the scaling constant, Representative The local density value of the region, represents a small constant that prevents the denominator from being zero, Representative The average disaster intensity value of the region, represents the average value of regional disaster intensity, Represents the standard deviation of regional disaster intensity.

8. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The method further comprises: S5: Based on the grid monitoring data, the data is mapped to the GIS platform according to the spatial coordinates, the map hierarchical processing of the GIS platform is called, the monitoring point data is displayed according to the spatial distribution and time change, and the geological disaster data visualization expression information is generated; The geological disaster data visualization expression information includes spatial mapping monitoring points, time evolution curves and disaster intensity distribution maps.

9. The method for expressing geological disaster monitoring point data based on GIS according to claim 1, characterized in that: The steps for obtaining the visual expression information of geological disaster data are specifically as follows: S501: Based on the gridded monitoring data, the spatial coordinates, timestamps and disaster intensity data of the monitoring points are obtained, the spatial coordinate data are called to match the geographic reference coordinate system of the GIS platform, the longitude and latitude conversion values ​​of the monitoring points are calculated according to the geographic reference coordinate system, the converted longitude and latitude data are called to be mapped to the spatial database of the GIS platform, and the GIS mapping monitoring data is obtained; S502: Based on the GIS mapping monitoring data, call the map level information of the GIS platform, calculate the map display range according to the spatial distribution of the monitoring points, call the map display range data to match the spatial coordinates of the monitoring points, adjust the map display ratio according to the distribution density of the spatial coordinates, divide the map level according to the adjusted ratio, and obtain the map level monitoring data; S503: Based on the hierarchical monitoring data of the map, call the timestamp and disaster intensity data of the monitoring point, calculate the time change trend of the monitoring point according to the timestamp, call the time change trend data to match the spatial coordinates, display the monitoring point data according to the spatial distribution and time change, and obtain the visual expression information of the geological disaster data.

10. A GIS-based geological disaster monitoring point data expression system, characterized in that: According to the method for expressing geological disaster monitoring point data based on GIS according to any one of claims 1 to 9, the system comprises: The data interpolation processing module obtains the detection data of the geological disaster monitoring points, calculates the monitoring data of the uncollected points based on the spatial correlation, and generates the interpolated monitoring values; The timing optimization processing module aligns the data according to the time series based on the interpolation monitoring value, analyzes the change trend of adjacent moments, calls the state estimation value of the time series, adjusts the interpolation monitoring value according to the change trend of the time series, and obtains the timing adjustment monitoring value; The data compression processing module adjusts the monitoring value based on the time series, classifies the monitoring points into corresponding clusters according to the shortest distance, calls the classified monitoring data to calculate the feature change range, extracts the feature principal component, reduces the dimension based on the feature principal component, and generates compressed monitoring data; The grid division module performs grid division according to the local density based on the compressed monitoring data, and re-divides the grid according to the division scale of the differentiated area to obtain gridded monitoring data; The data mapping expression module maps the data to the GIS platform based on the gridded monitoring data according to the spatial coordinates, displays the monitoring point data according to the spatial distribution and time changes, and generates visual expression information of geological disaster data.

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