Geology multi-scale data integration analysis system and method

Abstract

The invention relates to the technical field of geographic information analysis, in particular to a geoscience multi-scale data integration and analysis system and method.The system comprises a geographic position correlation adjustment module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and the geographic position correlation adjustment module obtains measuring point coordinates, elevation values and observation time, matches topographic measuring point data and screens measuring points exceeding a change interval; and comparing the maximum elevation change range and adjusting the matching parameters to generate a geographic measurement data matching result. According to the method, the precision and the stability of geoscience data are improved by constructing a multi-level data matching, monitoring, analyzing and integrating framework. Accurate matching of measurement point coordinates, elevation values and observation time is combined with terrain boundary displacement calculation, so that the integrity and space consistency of geographic measurement data are ensured. Based on the observation time interval and the variation amplitude of the remote sensing image sequence, the abnormal data offset condition is accurately identified, and the accuracy of abnormal measurement point screening is improved through data statistical distribution analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information analysis, and in particular, to a geoscience multi-scale data integration and analysis system and method. Background Art

[0002] The technical field of geographic information analysis includes the collection, processing, analysis, and display of geographic data, mainly used to analyze the spatial distribution characteristics of the earth's surface and related phenomena. This technical field covers from basic map making and satellite remote sensing data interpretation to complex geographic information systems (GIS) and location data analysis. Geographic information analysis relies on multiple data sources, including but not limited to satellite images, topographic survey data, and geotagged data, through which applications such as environmental monitoring, urban planning, disaster management, and natural resource management can be achieved.

[0003] Among them, a geoscience multi-scale data integration and analysis system refers to a technical solution for integrating and analyzing geoscience data from different scales and sources. By using specific data integration techniques, observational data at the microscale is combined with environmental data at the macroscale, thereby achieving data compatibility and analysis in a single system. This system realizes the effective integration of different data sources through data matching, assimilation techniques, and a unified data framework.

[0004] In the prior art during the process of geoscience data integration, there is a lack of fine matching of multi-scale data, resulting in low data compatibility. Due to the lack of matching calculation between the measurement point coordinates and terrain changes, it is difficult to guarantee the accuracy of terrain boundary information, affecting the data consistency of measurement points at different time scales. The detection of abnormal data only relies on data assimilation methods and does not fully consider the correlation between the observation time interval and the changes in remote sensing images, resulting in low screening accuracy of abnormal data. During the terrain classification process, there is a lack of optimization of the attribution based on the elevation distribution of measurement points, resulting in deviations in the classification boundaries of measurement points and affecting subsequent geoscience data analysis. The screening method of influencing factors is single, and only static data is used to evaluate the intensity of factor action, without considering the interaction between influencing factors, making it difficult for the factor screening results to reflect complex geoscience influence relationships. When integrating multi-scale data, a unified trend change comparison and classification matching mechanism is not established, the feature attribution of measurement points is unstable, and the analysis results after data integration are easily interfered by individual abnormal data, affecting the final accuracy of geoscience data analysis. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a geoscience multi-scale data integration and analysis system and method.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A geoscience multi-scale data integration and analysis system includes:

[0007] The geographical location association adjustment module obtains the coordinates, elevation values, and observation times of the measurement points, matches the terrain measurement point data, filters out the measurement points outside the change interval, compares the maximum elevation change range, and adjusts the matching parameters to generate the geographical measurement data matching result;

[0008] The abnormal data detection module monitors the observation time intervals of the measurement points and filters out the abnormal measurement points based on the geographical measurement data matching result to generate the abnormal measurement point offset analysis result;

[0009] The classification feature construction module obtains the elevation value distributions of all the measurement points based on the abnormal measurement point offset analysis result, identifies the terrain classification boundaries, and adjusts the corresponding attribution intervals to generate the terrain classification boundary intervals;

[0010] The composite influencing factor analysis module detects the elevation change ranges of the measurement points based on the terrain classification boundary intervals, filters out the influencing factors, and calculates the interaction intensity to generate the influencing factor action intensity analysis result;

[0011] The geoscience multi-scale data integration module adjusts the terrain classification boundaries according to the trend changes of the measurement points based on the influencing factor action intensity analysis result to obtain the geoscience multi-scale data integration analysis result.

[0012] As a further solution of the present invention, the geographical measurement data matching result specifically includes the measurement point coordinate matching degree, elevation change range, and terrain boundary displacement speed. The abnormal measurement point offset analysis result includes the remote sensing image change amplitude, measurement point data offset degree, and abnormal measurement point statistical distribution. The terrain classification boundary interval specifically refers to the measurement point elevation distribution, terrain classification attribution, and classification boundary deviation value. The influencing factor action intensity analysis result includes the elevation change influencing factor, influencing factor interaction intensity, and influencing factor fluctuation interval. The geoscience multi-scale data integration analysis result specifically includes the multi-scale data matching degree, elevation change trend, and measurement point feature attribution.

[0013] As a further solution of the present invention, the geographical location association adjustment module includes:

[0014] The terrain coordinate matching sub-module obtains the coordinates and observation times of the measurement points, calls the measurement point coordinates and observation times for matching, calculates the displacement vector of the measurement points during the observation time, filters out the measurement points with abnormal displacement vectors, calls the spatial relative positions of the measurement points for comparison, calculates the relative deviation of the spatial coordinates, and obtains the terrain coordinate matching deviation analysis result;

[0015] The terrain boundary displacement calculation sub-module, based on the terrain coordinate matching deviation analysis result, calls the coordinates of the terrain feature points and uses the formula:

[0016]

[0017] Calculate the terrain boundary change rate V t , and based on the change rate V t Analyze the spatial change trend of the measurement points during the observation time to obtain the terrain boundary displacement trend analysis result;

[0018] Among them, X i , Y i are the coordinate values of the i-th measurement point on the X and Y axes, X i-1 , Y i-1 are the coordinate values of the i-th measurement point on the X and Y axes in the previous time interval, and n is the number of data points in the time series;

[0019] Based on the terrain boundary displacement trend analysis result, the elevation change range comparison sub-module calls the elevation values of the measurement points and the time series data, calculates the maximum change amplitude of the elevation values within the time series, calls the time span for comparison with the maximum change amplitude, screens the measurement points with abnormal elevation changes, obtains the elevation change matching range, adjusts the matching parameters of the observation time span and the elevation change range, and generates the geographical measurement data matching result.

[0020] As a further solution of the present invention, the abnormal data detection module includes:

[0021] The time interval monitoring sub-module obtains the observation time interval of the measurement point data in the geographical measurement data matching result, sets the time interval reference value, sets the reference range according to the regional observation frequency, calls the observation time interval for comparison with the reference value, screens the time intervals exceeding the reference range, and obtains the time interval deviation analysis result;

[0022] Based on the time interval deviation analysis result, the data offset calculation sub-module obtains the change amplitude of the remote sensing image sequence, calls the time series of the measurement point data, and uses the formula:

[0023]

[0024] Calculate the data change amplitude D m according to the difference in remote sensing reflectance between adjacent moments, obtain the data change offset, call the data change offset for comparison with the time interval, screen the data change offsets exceeding the reference value, and obtain the data offset abnormal information;

[0025] Among them, R mj , R m(j-1) are the remote sensing reflectance values at the j-th moment and the j-1-th moment, T mj , T m(j-1) : the timestamps at the j-th moment and the j-1-th moment, and n is the number of data points in the time series;

[0026] Based on the data offset anomaly information, the abnormal measurement point screening sub-module calls the abnormal offset interval of the measurement point data, calculates the statistical distribution of the data change amplitude, sets the statistical range of the data change, screens the abnormal points beyond the statistical range, obtains the data distribution of the abnormal measurement points, and determines the clustering situation of the abnormal measurement points according to the abnormal point distribution density, so as to obtain the analysis result of the abnormal measurement point offset.

[0027] As a further solution of the present invention, the classification feature construction module includes:

[0028] Based on the analysis result of the abnormal measurement point offset, the elevation value calculation sub-module obtains the elevation data of all measurement points, extracts the corresponding relationship between the measurement point coordinate information and the elevation value, calculates the distribution interval of the measurement point elevation, sets the upper limit value and the lower limit value of the statistical interval of the elevation data, and screens the elevation abnormal measurement points beyond the upper and lower limit intervals according to the geographical distribution of the measurement points, so as to obtain the elevation value distribution interval;

[0029] The terrain classification boundary recognition sub-module calls the elevation value distribution interval, combines the measurement point coordinate information for data comparison, and uses the formula:

[0030]

[0031] Calculate the belonging probability P of the measurement point i' within the elevation value interval i′ , and according to the belonging probability P i′ Set the classification boundary threshold, screen the measurement points that match the classification boundary threshold, and construct the terrain classification boundary;

[0032] Among them, H i′ represents the elevation value of the measurement point i', and H mean represents the average elevation of the measurement point group,

[0033] H max and H min respectively represent the maximum elevation and the minimum elevation in the measurement point group;

[0034] Based on the terrain classification boundary, the terrain classification attribution adjustment sub-module extracts the spatial distribution characteristics of the terrain classification boundary, calculates the distribution of the measurement points in each region on the terrain classification boundary, sets the comparison standard for classification attribution adjustment, determines the attribution category of the measurement points, and adjusts the attribution interval of the terrain classification boundary according to the attribution category to generate the terrain classification boundary interval.

[0035] As a further solution of the present invention, the composite influence factor analysis module includes:

[0036] The elevation change detection sub-module calls the data of the terrain classification boundary interval, extracts the elevation change range of each measurement point from it, records the elevation change range according to the elevation change value at the corresponding time point, and obtains the elevation change information;

[0037] Based on the elevation change information, the influencing factor screening sub-module extracts the factors that affect the elevation change in the measurement point environment, including meteorological, geological, and hydrological factors, calls the extracted influencing factor data, and uses the formula:

[0038]

[0039] Calculate the action intensity F of the f-th influencing factor f From this, screen the factor with the greatest influence degree to obtain the key influencing factor;

[0040] Among them, C f represents the direct contribution value of the f-th influencing factor to the terrain feature, A f represents the activity intensity or influence weight of the f-th influencing factor, H Δ represents the span of the elevation change range;

[0041] The influencing factor interaction calculation sub-module calls the key influencing factors, extracts the time series change values of the influencing factors, screens the factor combinations with interactive influences, and generates the analysis result of the action intensity of the influencing factors.

[0042] As a further solution of the present invention, the geoscience multi-scale data integration module includes:

[0043] Based on the analysis result of the action intensity of the influencing factors, the interaction area screening sub-module screens the areas in the multi-scale data where the interaction intensity of the influencing factors is higher than the interaction threshold, calls the data of multiple time scales, obtains the interaction situation of the influencing factors in each area, classifies and marks the interaction intensity of the influencing factors, calculates the area proportion of the areas where the interaction intensity value exceeds the interaction threshold, and obtains the selected interaction influence areas;

[0044] Based on the selected interaction influence areas, the trend change comparison sub-module extracts the elevation change trend data during the observation time, and uses the formula:

[0045]

[0046] Calculate the elevation change of the measurement point within multiple time spans, screen the measurement point areas where the trend change exceeds the preset change range, and obtain the abnormal trend measurement point areas;

[0047] Among them, C represents the elevation change trend of the measurement point, t l ′ is the l-th time point, H l$h_l$ is the elevation value at the $l$-th time point, $t'$ and $H$ are the mean time and mean elevation of all time points respectively, and $g$ is the total number of time points;

[0048] The feature attribution matching sub-module calls the abnormal trend measurement point area, matches the terrain classification boundary interval, determines the classification result of each measurement point data within each elevation range, analyzes the corresponding relationship between the elevation range and the trend change of the measurement point, sorts out the feature attribution of the measurement point within each elevation range, and obtains the integrated analysis result of geoscience multi-scale data.

[0049] A method for integrated analysis of geoscience multi-scale data, which is executed based on the above-mentioned geoscience multi-scale data integration analysis system, and includes the following steps:

[0050] S1: Based on the measurement point coordinates, elevation values, and observation time, match the terrain measurement point data, screen out the measurement points that exceed the change interval, adjust the matching parameters, and generate the matching result of the geographical measurement data;

[0051] S2: Based on the matching result of the geographical measurement data, monitor the observation time interval, screen out the abnormal measurement points, calculate the spatial offset and elevation change, and generate the offset analysis result of the abnormal measurement points;

[0052] S3: Based on the offset analysis result of the abnormal measurement points, extract the elevation distribution of the measurement points, identify the classification boundary, adjust the attribution interval, establish the classification rule, and generate the terrain classification boundary interval;

[0053] S4: Based on the terrain classification boundary interval, detect the elevation change of the measurement points, screen out the influencing factors, calculate the interaction intensity, evaluate the contribution degree of the elevation change, and generate the analysis result of the influencing factor action intensity;

[0054] S5: Based on the analysis result of the influencing factor action intensity, conduct trend analysis of the measurement points, adjust the classification boundary in combination with the dynamic characteristics, optimize the data matching parameters, and obtain the integrated analysis result of geoscience multi-scale data.

[0055] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0056] In the present invention, by constructing a multi-level data matching, monitoring, analysis, and integration framework, the accuracy and stability of geoscience data are improved. The precise matching of measurement point coordinates, elevation values, and observation times, combined with terrain boundary displacement calculations, ensures the integrity and spatial consistency of geodetic measurement data. Based on the observation time interval and the change amplitude of remote sensing image sequences, abnormal data offsets are accurately identified, and the accuracy of screening abnormal measurement points is improved through data statistical distribution analysis. During the terrain classification process, the elevation value distribution range of measurement points is compared with the terrain classification boundary, and the boundary attribution is optimized through classification attribution adjustment to reduce classification errors. The analysis of influencing factors not only screens out the factors that have the greatest impact on elevation changes but also calculates the interaction intensity between different factors, providing a basis for multi-factor analysis of geoscience data. Based on the interaction intensity of influencing factors, high-impact areas are screened, the elevation change trend of measurement points is extracted, and the measurement point attribution is adjusted in combination with terrain classification information, making the data integration result more consistent with the real changes in the geographical environment. This solution improves the accuracy of geoscience data matching, enhances the detection ability of abnormal data, improves the accuracy of terrain classification and influencing factor analysis, and optimizes the feature attribution of measurement points through the integration of multi-scale data, making the analysis result after data integration more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is the system flow chart of the present invention;

[0058] Figure 2 is the flow chart of the geographical location association adjustment module of the present invention;

[0059] Figure 3 is the flow chart of the abnormal data detection module of the present invention;

[0060] Figure 4 is the flow chart of the classification feature construction module of the present invention;

[0061] Figure 5 is the flow chart of the composite influencing factor analysis module of the present invention;

[0062] Figure 6 is the flow chart of the geoscience multi-scale data integration module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to 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 used to limit the present invention.

[0064] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 should not be construed as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0065] Please refer to Figure 1 , the present invention provides a technical solution: a geoscience multi-scale data integration and analysis system includes:

[0066] The geographical location association adjustment module obtains the coordinates, elevation values, and observation times of the measurement points, matches the topographic measurement point data, filters out the measurement points outside the change interval, compares the maximum elevation change range, and adjusts the matching parameters to generate the geographical measurement data matching result;

[0067] The abnormal data detection module monitors the observation time interval of the measurement points and filters out the abnormal measurement points based on the geographical measurement data matching result to generate the abnormal measurement point offset analysis result;

[0068] The classification feature construction module obtains the elevation value distribution of all measurement points based on the abnormal measurement point offset analysis result, identifies the topographic classification boundary, and adjusts the corresponding attribution interval to generate the topographic classification boundary interval;

[0069] The composite influencing factor analysis module detects the elevation change range of the measurement points based on the topographic classification boundary interval, filters out the influencing factors, and calculates the interaction intensity to generate the influencing factor action intensity analysis result;

[0070] The geoscience multi-scale data integration module adjusts the topographic classification boundary according to the trend change of the measurement points based on the influencing factor action intensity analysis result to obtain the geoscience multi-scale data integration and analysis result;

[0071] The geographical measurement data matching result specifically includes the measurement point coordinate matching degree, elevation change range, and topographic boundary displacement speed. The abnormal measurement point offset analysis result includes the remote sensing image change amplitude, measurement point data offset degree, and abnormal measurement point statistical distribution. The topographic classification boundary interval specifically refers to the measurement point elevation distribution, topographic classification attribution, and classification boundary deviation value. The influencing factor action intensity analysis result includes the elevation change influencing factor, influencing factor interaction intensity, and influencing factor fluctuation interval. The geoscience multi-scale data integration and analysis result specifically includes the multi-scale data matching degree, elevation change trend, and measurement point feature attribution.

[0072] Please refer toFigure 2 , the geographical location association adjustment module includes:

[0073] The terrain coordinate matching sub-module obtains the coordinates of the measurement points and the observation time, calls the coordinates of the measurement points and the observation time for matching, calculates the displacement vector of the measurement points within the observation time, filters out the measurement points with abnormal displacement vectors, calls the spatial relative positions of the measurement points for comparison, calculates the relative deviation of the spatial coordinates, and obtains the terrain coordinate matching deviation analysis result;

[0074] Obtain the coordinates of the measurement points and the observation time, select multiple measurement points in a certain terrain area, such as the measurement points on different slopes of a mountainous area, use the observation timestamp to correspond to the spatial coordinate values of each measurement point, calculate the displacement vector of the measurement points within adjacent time intervals, and the calculation of the displacement vector is based on the change amount of the measurement coordinates at adjacent time points. For example, the coordinates of measurement point A at time t1 are (35.682, 139.753), and at time t2 are (35.684, 139.756), then the calculated displacement vector is: V =

[0075] (X t2 - X t1 ) 2 +(Y t2 - Y t1 ) 2 , V: the displacement vector of the measurement point, obtained by calculating the change amount of the spatial coordinates of the measurement point within two adjacent time intervals. X t1 , X t2 : the abscissa values of the measurement point at times t1 and t2, obtained by the geographical coordinate data recorded by the measurement device. Y t1 , Y t2 : the ordinate values of the measurement point at times t1 and t2, obtained in the same way as above. This calculation method is applicable to the displacement calculation of all measurement points. Filter out the measurement points with abnormal displacement vectors. For example, if the displacement vector exceeds the set terrain stability threshold, and the basis for setting the threshold is the terrain change characteristics of this area. For example, the displacement threshold for the stable area in the mountainous area is set to 0.001°, while the active area is set to 0.005°. Call the spatial relative positions of the measurement points for comparison, identify the relative offsets between the measurement points, calculate the mean square error of the offsets, set the offset error threshold, and points exceeding 0.003° will be identified as abnormal points, and obtain the terrain coordinate matching deviation value.

[0076] Based on the terrain coordinate matching deviation analysis result, the terrain boundary displacement calculation sub-module calls the coordinates of the terrain feature points and uses the formula:

[0077]

[0078] Calculate the terrain boundary change rate V t , according to the change rate Vt Analyze the spatial change trend of the measurement points within the observation time to obtain the analysis result of the terrain boundary displacement trend;

[0079] Among them, X i and Y i are the coordinate values of the i-th measurement point on the X and Y axes, obtained from the geolocation data of the measurement device. X i-1 and Y i-1 are the coordinate values of the i-th measurement point in the previous time interval on the X and Y axes, as above. n is the number of data points in the time series, calculated as the total number of data sampling points within the set time window, such as the total number of measurement point data within 10 hours. ∑ represents summing over all data points to calculate the overall average offset.

[0080] Invoke the coordinates of the terrain feature points, extract the key terrain feature points within the region, including mountain peaks, valley bottoms, and riverbeds, calculate the spatial change trend of the measurement points within the observation time based on the displacement of the feature points, set the time series data window, such as selecting the data every 10 hours for analysis, calculate the change rate of the feature points within adjacent time intervals. For example, if the change amount of the terrain boundary point within 10 hours is 0.002°, the change rate is calculated as 0.002° / 10h, using the formula:

[0081]

[0082] Calculate the terrain boundary change rate V t , and compare it with the reference rate of boundary displacement set for the region. The reference rate is set based on the geological activity characteristics of the region. For example, the threshold for a stable terrain region is set to 0.0005° / h, and the threshold for an active region is set to 0.002° / h. If the calculated value exceeds the reference rate, further calculate the boundary displacement trend.

[0083] Parameter assignment and calculation:

[0084] Assume the measurement point data is: (X1, Y1) = (35.682, 139.753), (X2, Y2) = (35.684, 139.756), (X3, Y3) = (35.685, 139.758)

[0085] Calculate:

[0086]

[0087] The finally calculated terrain boundary displacement rate is 0.003. Compared with the threshold of 0.0005 for the stable terrain region, it indicates that the boundary displacement rate of this region exceeds the stable range. Therefore, there may be a geological activity risk in this region.

[0088] Based on the analysis results of the terrain boundary displacement trend, the elevation change range comparison sub-module calls the elevation values and time series data of the measurement points, calculates the maximum change range of the elevation values within the time series, calls the time span and compares it with the maximum change range, screens the measurement points with abnormal elevation changes, obtains the elevation change matching range, adjusts the matching parameters of the observation time span and the elevation change range, and generates the geographical measurement data matching result;

[0089] Based on the terrain boundary displacement trend value, call the elevation value and time series data of the measurement point, and extract the elevation values at different times, such as the data series at 10 hours, 20 hours, and 30 hours. For example, the elevations of a certain measurement point at time points t1, t2, and t3 are 500m, 502m, and 505m respectively. Calculate the change range of the elevation value, ΔH = H t3 -H t1 = 505 - 500 = 5m. Set the elevation change reference range. For example, the elevation change range in the stable area of the mountainous area is 0 - 2m. If the change range exceeds this reference range, it is identified as an abnormal point. Call the time span and compare it with the maximum change range, and calculate the elevation change rate If the elevation change rate is higher than the set threshold (such as 0.05m / h), further analyze the elevation change pattern of this point to obtain the elevation change matching range. Among them, H t : The elevation value of the measurement point at time t, obtained from the topographic surveying equipment. ΔH: Elevation change amount, obtained by calculating the elevation difference between adjacent time points. Δt: Time span, calculated based on the time stamp of the observed data. V H : Elevation change rate, obtained by dividing the elevation change amount by the time span. Call the elevation change matching range, compare the elevation change trends under different observation time spans, and set the matching parameters for different time spans. For example, for short-period changes (such as within 1 hour), set the change upper limit to 0.5m, and for long-period changes (such as within 30 hours), set the change upper limit to 5m. Call the change trends within the short period and the long period for comparison. If the short-period change trend does not match the long-period trend, for example, the elevation change range within the short period is higher than 50% of the long-period change, then adjust the matching parameters, expand the observation time window, set a new observation time span, such as adjusting the originally set 30-hour data window to 40 hours, recalculate the elevation change rate and match the change trend. If it still does not meet the set matching rules, then screen the abnormal points and readjust the matching parameters, and finally generate the geographical measurement data matching result.

[0090] Please refer to Figure 3 , the abnormal data detection module includes:

[0091] The time interval monitoring sub-module obtains the observation time interval of the measurement point data in the geographical measurement data matching result, sets the time interval reference value, sets the reference range according to the regional observation frequency, calls the observation time interval to compare with the reference value, screens out the time intervals that exceed the reference range, and obtains the time interval deviation analysis result;

[0092] Obtain the observation time interval of the measurement point data, call the geographical measurement data matching result, parse the timestamp information, set the time series of the measurement points, extract the coordinate points corresponding to the observation timestamps according to the distribution of the measurement points in different regions, calculate the observation time difference between adjacent measurement points. For example, in a certain terrain area, measurement point A collects data at T1 = 10:00, and measurement point B collects data at T2 = 10:15, then the time interval is calculated as ΔT = T2 - T1 = 15 minutes. Apply this calculation process to all measurement points to obtain a complete list of time series intervals. Set the time interval reference value, determine the reference range for different regions according to the terrain characteristics. For example, set the reference interval to 10 minutes in the urban terrain area and 30 minutes in the forest area. Call the observation time interval to compare with the reference value, screen out the time interval data points that exceed the reference range. If the time interval of a certain measurement point data exceeds the set upper limit of 30 minutes, then mark this measurement point as an abnormal time point. Call the distribution of the abnormal time points, count the density of the abnormal time points in different terrain types. If the abnormal point density exceeds the set abnormal time point threshold, then further calculate the abnormal trend change rate of this region to obtain the time interval deviation value.

[0093] Based on the time interval deviation analysis result, the data offset calculation sub-module obtains the change amplitude of the remote sensing image sequence, calls the time series of the measurement point data, and uses the formula:

[0094]

[0095] Calculate the data change amplitude D according to the difference in remote sensing reflectance between adjacent moments m , obtain the data change offset, call the data change offset to compare with the time interval, screen out the data change offsets that exceed the reference value, and obtain the data offset abnormal information;

[0096] Among them, R mj , R m(j-1) are the remote sensing reflectance values at the j-th moment and the j - 1-th moment, which are parsed from the remote sensing image data, T mj , T m(j-1) are the timestamps at the j-th moment and the j - 1-th moment, which are obtained from the time records of the measurement data, and n is the total number of observation data points in the time series, which is calculated according to the data sampling rate.

[0097] Suppose the measurement point data is as follows: R m1= 0.25, R m2 = 0.27, R m3 = 0.35, T m1 = 10, T m2 = 20, T m3 = 30 (unit: minutes)

[0098] Calculate:

[0099] The finally calculated data change offset is 0.01. Compared with the offset reference value of 0.05 in the vegetation-covered area, the change offset of this data point is lower than the reference range, so it is not regarded as an abnormal point. If it exceeds the reference value, it will enter the next screening step. Calculate the offset of the data through the change amount of remote sensing reflectance in the time series, and normalize this offset with the time interval, so that the calculation result of the data offset can be applied to different time scales, improving the applicability of data anomaly detection.

[0100] The abnormal measurement point screening sub-module, based on the data offset anomaly information, calls the abnormal offset interval of the measurement point data, calculates the statistical distribution of the data change amplitude, sets the statistical range of the data change, screens the abnormal points beyond the statistical range, obtains the data distribution of the abnormal measurement points, and judges the aggregation situation of the abnormal measurement points according to the distribution density of the abnormal points, and obtains the offset analysis result of the abnormal measurement points;

[0101] Based on the data offset anomaly value, call the abnormal offset interval of the measurement point data, calculate the statistical distribution of the data change amplitude, extract the change trend of the measurement point data in different time periods, set the statistical range of the data change, analyze the change characteristics of different regions according to multi-year observation data. For example, the statistical range for urban areas is set to 0.01 - 0.03, and the statistical range for nature reserves is set to 0.02 - 0.05. Call the data change offset and compare it with the statistical range to screen the abnormal points beyond the statistical range. For example, if the calculated value of the data change offset of a certain measurement point is 0.06, exceeding the maximum threshold of 0.05 set for the nature reserve, then mark this measurement point as an abnormal point, calculate the spatial distribution of the abnormal points in this area, and count the distribution density of the abnormal points. If the set area is 1 km 2 , if the number of abnormal points exceeds the set abnormal measurement point density threshold (such as 0.1 per km 2 ), it means that the number of abnormal measurement points detected per unit area of 1 square kilometer (km 2 ) is 0.1, that is, it is expected to have 1 abnormal measurement point per 10 square kilometers, then mark this area as a high-density abnormal area, further calculate the change trend of the abnormal points in this area, and obtain the offset analysis result of the abnormal measurement points.

[0102] Please refer to Figure 4 , the classification feature construction module includes:

[0103] Based on the analysis results of the offset of abnormal measurement points, the elevation value calculation sub-module obtains the elevation data of all measurement points, extracts the corresponding relationship between the measurement point coordinate information and the elevation value, calculates the distribution interval of the elevation of the measurement points, sets the upper and lower limit values of the statistical interval of the elevation data, and filters out the elevation abnormal measurement points beyond the upper and lower limit intervals according to the geographical distribution of the measurement points, so as to obtain the elevation value distribution interval.

[0104] Based on the analysis results of the offset of abnormal measurement points, obtain the elevation data of the measurement points, call the coordinate information of the abnormal measurement points, analyze the distribution of the measurement points in the geographical space, obtain the altitude value data of the measurement points, classify the elevation data of all measurement points according to the coordinate positions, and calculate the elevation distribution interval of the measurement points. The specific calculation process is as follows. First, extract the elevation values of all measurement points, and calculate the range of the elevation data using the maximum and minimum values. For example, the elevation values of the measurement points in a certain area are H1 = 150m, H2 = 180m, H3 = 210m, H4 = 230m respectively, then the elevation range is H max -H min

[0105] = 230m - 150m = 80m. Secondly, divide this elevation range into fixed intervals. For example, if the width of each interval is set to 20m, then the elevation intervals can be divided into 150 - 170m, 170 - 190m, 190 - 210m, 210 - 230m. Then call the altitude values of the measurement points, compare them with the set intervals, determine the elevation intervals to which the measurement points belong, and calculate the number of measurement points in each interval. Filter out the elevation points with abnormal data. If the number of measurement points in a certain elevation interval is much larger or much lower than other intervals, there may be abnormalities in this interval. Set the abnormal threshold of the elevation value to 5. If the number of measurement points in an interval is less than 5 or exceeds the set upper limit, it is determined as an abnormal interval. Statistically analyze the density of the measurement points in this abnormal interval, calculate the spatial distribution characteristics of the abnormal measurement points, and obtain the elevation value distribution interval.

[0106] The terrain classification boundary recognition sub-module calls the elevation value distribution interval, combines the measurement point coordinate information for data comparison, and uses the formula:

[0107]

[0108] Calculate the attribution probability P of the measurement point i' within the elevation value interval i′ , and set the classification boundary threshold according to the attribution probability P i′ , filter out the measurement points that match the classification boundary threshold, and construct the terrain classification boundary;

[0109] Among them, H i′ represents the elevation value of the measurement point i', H meanrepresents the average elevation of the group of measurement points,

[0110] H max and H min respectively represent the maximum elevation and the minimum elevation in the group of measurement points.

[0111] Suppose the elevation data of the measurement points in a certain area are as follows: H1 = 150m, H2 = 180m, H3 = 210m, H4 = 230m. The calculated average elevation is:

[0112]

[0113] If the measurement point H2 = 180m, the attribution probability is calculated as follows:

[0114]

[0115] According to the attribution probability value, set the classification boundary threshold T P ′. Suppose T P ′ = 0.2. If the attribution probability of a certain measurement point is lower than the threshold, it may not belong to this elevation classification area. Filter the measurement points that meet the classification boundary threshold and construct the terrain classification boundary. Through the normalization calculation of the maximum value, minimum value and mean value of the measurement point elevation, the attribution probability of the measurement point can be applied to different terrain areas, avoiding the problem of traditional attribution judgment based on absolute values and improving the applicability of classification. The calculated attribution probability of the measurement point is 0.156. Comparing with the set classification boundary threshold 0.2, it can be determined that this measurement point may belong to this classification area. If the calculated value exceeds the threshold range, it is determined that this measurement point does not belong to this classification area, and the terrain classification boundary is adjusted in the subsequent steps.

[0116] The terrain classification attribution adjustment sub-module extracts the spatial distribution characteristics of the terrain classification boundary based on the terrain classification boundary, calculates the distribution of the measurement points in each area on the terrain classification boundary, sets the comparison criteria for classification attribution adjustment, judges the attribution category of the measurement points, adjusts the attribution interval of the terrain classification boundary according to the attribution category, and generates the terrain classification boundary interval;

[0117] Invoke the terrain classification boundary, extract the spatial distribution characteristics of the terrain classification boundary, calculate the distribution of measurement points in different regions on the terrain classification boundary, set the comparison criteria for classification attribution adjustment, calculate the classification offset of the measurement points, compare the current attribution category of the measurement points with the terrain classification boundary, and determine whether its attribution category needs to be adjusted. Use the elevation attribution probability of the measurement points for determination. If the attribution probability value is lower than the set attribution adjustment threshold, the classification attribution interval needs to be adjusted. For example, if the calculated attribution probability value of a certain measurement point is 0.25 and the attribution adjustment threshold is set to 0.3, since 0.25 < 0.3, the attribution category of this measurement point needs to be adjusted. The adjustment method is to move the position of the terrain classification boundary and assign the measurement point to a region that more conforms to its elevation distribution, and finally generate the terrain classification boundary interval.

[0118] Please refer to Figure 5 , the composite influencing factor analysis module includes:

[0119] The elevation change detection sub-module invokes the data of the terrain classification boundary interval, extracts the elevation change range of each measurement point from it, records the elevation change range according to the elevation change value at the corresponding time point, and obtains the elevation change information;

[0120] Based on the terrain classification boundary interval data, first monitor multiple elevation change points of the measurement point position, extract the elevation values of these points at different time stages, and organize the elevation data of these time points into a time series table. For example, the elevation records of measurement point 1 within a certain time period (such as 6 months) are 125.4 meters, 126.8 meters, 127.3 meters, and 125.9 meters in sequence. Determine the change trend through this time series record, and further obtain the elevation range through the difference between time points. The maximum and minimum values of this difference correspond to the maximum elevation and the minimum elevation values respectively. While calculating the difference, obtain the terrain type boundary of the measurement point through on-site investigation. For example, the maximum elevation at boundary point A is 130.2 meters, the minimum elevation is 120.5 meters, and the elevation difference is 9.7 meters. After completing these measurements, calibrate the elevation interval data of all measurement points within the entire range to complete the detection of the elevation change range and obtain the elevation change range data.

[0121] The influencing factor screening sub-module extracts various factors that may affect the elevation change in the measurement point environment based on the elevation change information, including meteorological, geological, and hydrological factors, invokes the extracted influence factor data, and uses the formula:

[0122]

[0123] Calculate the action intensity F of the f-th influencing factor f , screen out the factor with the greatest influence degree from it to obtain the key influencing factor;

[0124] Among them, Cf It represents the direct contribution value of the f-th impact factor to the terrain features, which is obtained by combining environmental monitoring data with geological, meteorological, and hydrological surveys. For example, by recording the soil properties or rock layer change data at the measurement points through geological surveys, the contribution value is set as the influence degree of different environmental factors on the measurement points, and the unit depends on the factor category. A f It represents the activity intensity or influence weight of the f-th impact factor, which is determined by the time activity degree of the factor within the elevation change range. The specific methods include recording the change frequency of the factor at different time points and performing time weighting on it. For example, the statistical frequency of the active factor is obtained through the change records of rain gauges and groundwater monitoring equipment every month to determine the influence weight. H Δ It represents the span of the elevation change range, that is, the difference between the maximum value and the minimum value in the elevation change. This value is directly obtained by monitoring the elevation data of the measurement points at different time points.

[0125] Substitute the parameters. For example, the contribution value C of the geological factor f is 0.75, the activity intensity A f is 1.2, and the elevation difference H within the interval Δ is 10.5 meters. Substituting into the formula, we can get: This value represents the actual influence degree of the geological factor on the elevation change. Calculate the influence values of each environmental factor in this way, and screen out the factor with the largest influence value, that is, the key influence factor.

[0126] The impact factor interaction calculation sub-module calls the key impact factor, extracts the time series change values of the impact factor, screens out the factor combinations with interactive effects, and generates the analysis result of the impact factor action intensity;

[0127] Based on the key impact factor, extract the change values of these factors in different time series, and judge the mutual influence intensity between the factors by comparing between multiple time points. For example, assume that the action values of the key factor geological factor at measurement point 1 are 0.09, 0.11, 0.08, and 0.10 at different time points respectively, and the action values of the meteorological factor at the corresponding time points are 0.04, 0.07, 0.05, and 0.06 respectively. Calculate the action difference between the two at each time point as follows: at the first time point, 0.09 - 0.04 = 0.05; at the second time point, 0.11 - 0.07 = 0.04, and so on. Record the mutual change differences between different factors. After calculating and screening these differences, judge which factors have a significant interactive relationship. For example, if the difference exceeds a certain reference value (such as 0.03), it is determined that there is a significant interaction, and calculate the average intensity of the interactive relationship. Finally, obtain the analysis result of the impact factor action intensity.

[0128] Please refer to Figure 6 , and the geoscience multi-scale data integration module includes:

[0129] Based on the analysis results of the influence factor action intensity, the interaction area screening sub-module screens the areas in the multi-scale data where the interaction intensity of the influence factors is higher than the interaction threshold, calls the data of multiple time scales, obtains the interaction situation of the influence factors in each area, classifies and marks the interaction intensity of the influence factors, calculates the area proportion of the areas where the interaction intensity value exceeds the interaction threshold, and obtains the selected interaction influence areas;

[0130] Based on the analysis results of the influence factor action intensity, first call the multi-scale data, including terrain data, remote sensing data, historical observation data of measurement points, etc., extract the interaction intensity of the influence factors in the areas where all measurement points are located, respectively mark the interaction intensity levels of different areas, and numericalize the interaction intensity. For example, in a certain area, the measured interaction intensity of influence factors A and B is 0.65, and the interaction intensity of influence factors C and D is 0.72. According to the set interaction threshold (such as 0.7), screen the areas where the interaction intensity is higher than this threshold. For the judgment of the interaction intensity critical value, by calculating the interaction intensity distribution of different measurement points, use statistical analysis methods to obtain the mean and standard deviation of the interaction intensity, and set the benchmark threshold as the mean plus a certain multiple of the standard deviation to ensure the rationality of the threshold. For example, in the measurement data of all areas, the mean of the interaction intensity of the influence factors is 0.58, and the standard deviation is 0.12. Set the threshold as 0.58 + 1×0.12 = 0.70, screen the areas where the interaction intensity exceeds 0.70, and calculate the area proportion of these areas. For example, the total area of the observation area is 500 square kilometers, and the area of the area where the interaction intensity exceeds 0.70 is 120 square kilometers. Then calculate that the proportion of the high-interaction area is 120 / 500 = 24%, and finally obtain the high-interaction influence area.

[0131] Based on the selected interaction influence areas, the trend change comparison sub-module extracts the elevation change trend data within the observation time, and uses the formula:

[0132]

[0133] Calculate the elevation change of the measurement points within multiple time spans, screen the areas of the measurement points where the trend change exceeds the preset change range, and obtain the abnormal trend measurement point areas;

[0134] Among them, C represents the elevation change trend of the measurement point, t l ′ is the l-th time point, H l is the elevation value at the l-th time point, t′ and H are the time mean and elevation mean of all time points respectively, and g is the total number of time points.

[0135] The parameter t l ′ is obtained from the measurement time record. For example, January 2021, July 2021, January 2022, and is converted into time series values 1, 2, 3, Hl Obtained from measured elevation data. For example, the elevations corresponding to a certain measurement point are 320.4m, 321.1m, and 322.0m respectively, and calculate their mean value. It is:

[0136] = 321.17m;

[0137] The time mean value is calculated as follows:

[0138] Calculate the numerator part:

[0139] Calculate the denominator part:

[0140] Finally, calculate the trend C value:

[0141] If the preset change range is |C| > 0.8m / year, then this measurement point is at the boundary value, and it can be further verified whether it belongs to the abnormal trend measurement point area. If the trend values calculated from multiple measurement points exceed 0.8m / year, then the overall area is marked as the abnormal trend measurement point area.

[0142] The feature attribution matching sub-module calls the abnormal trend measurement point area, matches the terrain classification boundary interval, determines the classification results of each measurement point data within each elevation range, analyzes the corresponding relationship between the elevation range and the trend change of the measurement point, sorts out the feature attribution of the measurement point within each elevation range, and obtains the integrated analysis result of geoscience multi-scale data.

[0143] Based on the abnormal trend measurement point area, call the terrain classification boundary interval, match the elevation range of each measurement point, obtain the attribution of the measurement point in different terrain classifications, adopt the zoning statistics method, calculate the proportion of the number of measurement points within different elevation ranges. For example, if the elevation interval corresponding to a certain terrain type is 300m - 320m, and it is found that there are 150 measurement points among them, and the total number of all measurement points is 600, then the proportion of the measurement points of this terrain classification is 150 / 600 = 25%. Calculate the attribution proportion of different elevation ranges in this way, and finally form the feature attribution information of the measurement points under each elevation classification, and obtain the integrated analysis result of geoscience multi-scale data.

[0144] A method for integrated analysis of geoscience multi-scale data. The method for integrated analysis of geoscience multi-scale data is executed based on the above-mentioned geoscience multi-scale data integration analysis system, and includes the following steps:

[0145] S1: Based on the measurement point coordinates, elevation values, and observation time, match the terrain measurement point data, screen out the measurement points exceeding the change interval, adjust the matching parameters, and generate the matching result of the geographical measurement data.

[0146] S2: Based on the matching results of geographical measurement data, monitor the observation time interval, screen abnormal measurement points, calculate the spatial offset and elevation change, and generate the offset analysis results of abnormal measurement points;

[0147] S3: Based on the offset analysis results of abnormal measurement points, extract the elevation distribution of measurement points, identify the classification boundaries, adjust the attribution intervals, establish classification rules, and generate the terrain classification boundary intervals;

[0148] S4: Based on the terrain classification boundary intervals, detect the elevation change of measurement points, screen the influencing factors, calculate the interaction intensity, evaluate the contribution degree of elevation change, and generate the analysis results of the acting intensity of influencing factors;

[0149] S5: Based on the analysis results of the acting intensity of influencing factors, conduct trend analysis of measurement points, adjust the classification boundaries in combination with dynamic characteristics, optimize the data matching parameters, and obtain the integrated analysis results of multi-scale geoscience data.

[0150] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A geoscience multi-scale data integration and analysis system, characterized in that, The system includes: The geographic location association adjustment module obtains the coordinates, elevation values, and observation times of the measurement points, matches the topographic measurement point data, filters the measurement points outside the change range, compares the maximum elevation change range, and adjusts the matching parameters to generate the matching result of the geographic measurement data; The abnormal data detection module monitors the observation time intervals of the measurement points based on the matching result of the geographic measurement data, filters the abnormal measurement points, and generates the offset analysis result of the abnormal measurement points; The classification feature construction module obtains the elevation value distribution of all the measurement points based on the offset analysis result of the abnormal measurement points, identifies the topographic classification boundaries, and adjusts the corresponding attribution intervals to generate the topographic classification boundary intervals; The composite influencing factor analysis module detects the elevation change range of the measurement points based on the topographic classification boundary intervals, filters the influencing factors, calculates the interaction intensity, and generates the analysis result of the influencing factor action intensity; The geoscience multi-scale data integration module adjusts the topographic classification boundaries according to the trend changes of the measurement points based on the analysis result of the influencing factor action intensity to obtain the analysis result of the geoscience multi-scale data integration.

2. The geoscience multi-scale data integration and analysis system according to claim 1, wherein The matching result of the geographic measurement data specifically refers to the coordinate matching degree of the measurement points, the elevation change range, and the topographic boundary displacement speed. The offset analysis result of the abnormal measurement points includes the change amplitude of the remote sensing image, the offset degree of the measurement point data, and the statistical distribution of the abnormal measurement points. The topographic classification boundary interval specifically refers to the elevation distribution of the measurement points, the topographic classification attribution, and the classification boundary deviation value. The analysis result of the influencing factor action intensity includes the elevation change influencing factor, the influencing factor interaction intensity, and the influencing factor fluctuation interval. The analysis result of the geoscience multi-scale data integration specifically refers to the multi-scale data matching degree, the elevation change trend, and the measurement point feature attribution.

3. The geoscience multi-scale data integration and analysis system according to claim 2, wherein The geographic location association adjustment module includes: The topographic coordinate matching sub-module obtains the coordinates and observation times of the measurement points, calls the measurement point coordinates and observation times for matching, calculates the displacement vector of the measurement points during the observation time, filters the measurement points with abnormal displacement vectors, calls the spatial relative positions of the measurement points for comparison, calculates the relative deviation of the spatial coordinates, and obtains the analysis result of the topographic coordinate matching deviation; The topographic boundary displacement calculation sub-module, based on the analysis result of the topographic coordinate matching deviation, calls the coordinates of the topographic feature points and uses the formula: Calculate the terrain boundary change rate V t , according to the change rate V t Analyze the spatial change trend of the measurement points during the observation time to obtain the terrain boundary displacement trend analysis result; Among them, X i , Y i are the coordinate values of the i-th measurement point on the X and Y axes, X i-1 , Y i-1 are the coordinate values of the previous time interval of the i-th measurement point on the X and Y axes, and n is the number of data points in the time series; The elevation change range comparison sub-module, based on the analysis result of the topographic boundary displacement trend, calls the elevation values and time series data of the measurement points, calculates the maximum change amplitude of the elevation values within the time series, calls the time span and the maximum change amplitude for comparison, filters the measurement points with abnormal elevation changes, obtains the elevation change matching range, adjusts the matching parameters of the observation time span and the elevation change range, and generates the matching result of the geographic measurement data.

4. The geoscience multi-scale data integration and analysis system according to claim 3, wherein The abnormal data detection module includes: The time interval monitoring sub-module obtains the observation time intervals of the measurement point data in the matching result of the geographic measurement data, sets the time interval reference value, sets the reference range according to the regional observation frequency, calls the observation time interval for comparison with the reference value, filters the time intervals outside the reference range, and obtains the analysis result of the time interval deviation; Based on the time interval deviation analysis result, the data offset calculation sub-module obtains the change amplitude of the remote sensing image sequence, calls the time series of the measurement point data, and uses the formula: Calculate the data change amplitude D based on the difference in remote sensing reflectance at adjacent times m , obtain the data change offset, call the data change offset for comparison with the time interval, filter out the data change offsets that exceed the reference value, and obtain the data offset anomaly information; where R mj , R m(j-1) are the remote sensing reflectance values at the j-th and (j - 1)-th moments, T mj , T m(j-1) : are the timestamps at the j-th and (j - 1)-th moments, and n is the number of data points in the time series; Based on the data offset anomaly information, the abnormal measurement point screening sub-module calls the abnormal offset interval of the measurement point data, calculates the statistical distribution of the data change amplitude, sets the statistical range of the data change, screens the abnormal points beyond the statistical range, obtains the data distribution of the abnormal measurement points, and judges the clustering situation of the abnormal measurement points according to the abnormal point distribution density, so as to obtain the analysis result of the abnormal measurement point offset.

5. The geoscience multi-scale data integration and analysis system according to claim 4, wherein The classification feature construction module includes: Based on the analysis result of the abnormal measurement point offset, the elevation value calculation sub-module obtains the elevation data of all measurement points, extracts the corresponding relationship between the measurement point coordinate information and the elevation value, calculates the distribution interval of the measurement point elevation, sets the upper and lower limit values of the statistical interval of the elevation data, and screens the elevation abnormal measurement points beyond the upper and lower limit intervals according to the geographical distribution of the measurement points, so as to obtain the elevation value distribution interval; The terrain classification boundary recognition sub-module calls the elevation value distribution interval and conducts data comparison in combination with the measurement point coordinate information, using the formula: Calculate the attribution probability P of the measurement point i' within the elevation value range i′ , according to the attribution probability P i′ Set the classification boundary threshold, screen the measurement points that match the classification boundary threshold, and construct the terrain classification boundary; Among them, H i′ represents the elevation value of the measurement point i′, H mean represents the average elevation of the group of measurement points, H max and H min respectively represent the maximum elevation and the minimum elevation in the group of measurement points; Based on the terrain classification boundary, the terrain classification attribution adjustment sub-module extracts the spatial distribution characteristics of the terrain classification boundary, calculates the distribution of the measurement points in each area on the terrain classification boundary, sets the comparison standard for the classification attribution adjustment, judges the attribution category of the measurement points, and adjusts the attribution interval of the terrain classification boundary according to the attribution category to generate the terrain classification boundary interval.

6. The geoscience multi-scale data integration and analysis system according to claim 5, characterized in that The composite influence factor analysis module includes: The elevation change detection sub-module calls the data in the terrain classification boundary interval, extracts the elevation change range of each measurement point from it, records the elevation change range according to the elevation change value at the corresponding time point, and obtains the elevation change information; Based on the elevation change information, the influence factor screening sub-module extracts the factors affecting the elevation change in the measurement point environment, including meteorological, geological, and hydrological factors, and calls the extracted influence factor data, using the formula: Calculate the action intensity F of the f-th influence factor f , screen out the factor with the greatest influence degree from it to obtain the key influence factor; Among them, C f represents the direct contribution value of the f-th influencing factor to the terrain feature, A f represents the activity intensity or influence weight of the f-th influencing factor, H Δ represents the span of the elevation change range; The influence factor interaction calculation sub-module calls the key influence factors, extracts the time series change values of the influence factors, screens the factor combinations with interactive influences, and generates the analysis result of the influence factor action intensity.

7. The geoscience multi-scale data integration and analysis system according to claim 6, wherein The geoscience multi-scale data integration module includes: Based on the analysis result of the influence factor action intensity, the interaction area screening sub-module screens the areas in the multi-scale data where the influence factor interaction intensity is higher than the interaction threshold, calls the data of multiple time scales, obtains the influence factor interaction situation in each area, classifies and marks the influence factor interaction intensity, and calculates the area proportion of the areas where the interaction intensity value exceeds the interaction threshold to obtain the selected interaction influence area; Based on the selected interaction influence area, the trend change comparison sub-module extracts the elevation change trend data during the observation time, using the formula: Calculate the elevation change of the measurement points within multiple time spans, and screen the measurement point areas where the trend change exceeds the preset change range to obtain the abnormal trend measurement point areas; Among them, C represents the elevation change trend of the measurement point, t l ′ is the l-th time point, H l is the elevation value at the l-th time point, and are respectively the time mean value and the elevation mean value of all time points, and g is the total number of time points; The feature attribution matching sub-module calls the abnormal trend measurement point area, matches the terrain classification boundary interval, determines the classification results of each measurement point data within each elevation range, analyzes the corresponding relationship between the elevation range and the trend change of the measurement points, sorts out the feature attribution of the measurement points within each elevation range, and obtains the integrated analysis result of geoscience multi-scale data.

8. A method for integrated analysis of multi-scale geoscience data, characterized in that, Execute according to the geoscience multi-scale data integration analysis system described in any one of claims 1-7, including the following steps: S1: Based on the measurement point coordinates, elevation values, and observation time, match the terrain measurement point data, screen the measurement points that exceed the change interval, adjust the matching parameters, and generate the matching result of the geographical measurement data. S2: Based on the matching result of the geographical measurement data, monitor the observation time interval, screen the abnormal measurement points, calculate the spatial offset and elevation change, and generate the offset analysis result of the abnormal measurement points. S3: Based on the offset analysis result of the abnormal measurement points, extract the elevation distribution of the measurement points, identify the classification boundary, adjust the attribution interval, establish the classification rule, and generate the terrain classification boundary interval. S4: Based on the terrain classification boundary interval, detect the elevation change of the measurement points, screen the influencing factors, calculate the interaction intensity, evaluate the contribution degree of the elevation change, and generate the analysis result of the influencing factor action intensity. S5: Based on the analysis result of the influencing factor action intensity, conduct the trend analysis of the measurement points, adjust the classification boundary in combination with the dynamic characteristics, optimize the data matching parameters, and obtain the integrated analysis result of geoscience multi-scale data.

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