A fusion method and device based on heterogeneous time series settlement data

Abstract

The application belongs to the technical field of time series precipitation data, and discloses a fusion method and device based on heterogeneous time series precipitation data. The method can realize heterogeneous data integration, benchmark unification, spatial matching precision improvement, and acquisition of long-time series high-quality precipitation monitoring data, and provides reliable data support for related applications.

Description

Technical Field

[0001] This application relates to the field of time-series settlement data technology, and in particular to a fusion method and apparatus based on heterogeneous time-series settlement data. Background Technology

[0002] In the field of land subsidence monitoring, with the continuous development of technology, various monitoring methods have emerged, such as SBAS-InSAR technology. SBAS-InSAR (Smal1 Baseline Subset Interferometric Synthetic Aperture Radar) is a radar interferometry technique that utilizes multi-period short-baseline radar imagery to effectively monitor minute surface deformations through differential interferometry. However, in practical applications, several problems urgently need to be addressed. Firstly, the time-series subsidence data obtained using SBAS-InSAR technology with different software have different storage formats, such as SHP and TIF data formats. These two formats are heterogeneous and difficult to directly fuse. Secondly, even for the same area, data from different sources have inconsistent resolutions, and the monitoring point locations vary from result to result. This leads to temporal and spatial discontinuity in the data, making it difficult to obtain complete and continuous cumulative subsidence data. In addition, there may be systematic biases between different data sources, which affect the accuracy and reliability of settlement monitoring data, thus bringing inconvenience and limitations to related applications such as geological disaster risk assessment and urban ground settlement monitoring. Summary of the Invention

[0003] This application provides a fusion method and apparatus based on heterogeneous time-series settlement data, which can realize the integration of heterogeneous data, standardization of benchmarks, improvement of spatial matching accuracy, and acquisition of high-quality long-term settlement monitoring data, providing reliable data support for related applications.

[0004] In a first aspect, this application provides a fusion method based on heterogeneous time-series settlement data, the method comprising:

[0005] Acquire first time-series settlement data and multiple second time-series settlement data; wherein, the first time-series settlement data is in SHP data format, and the second time-series settlement data is in tag image file format, and each second time-series settlement data corresponds to a different time period; the first time-series settlement data includes multiple monitoring points and the monitoring results corresponding to each monitoring point, wherein the monitoring results include monitoring time, time-series settlement amount at the monitoring time, and location information of the monitoring point; each pixel in the second time-series settlement data corresponds to a location information, and the pixel value of the pixel is the time-series settlement amount of the location information corresponding to the pixel;

[0006] Based on the first time series settlement data, the starting reference of the second time series settlement data is adjusted to obtain the adjusted second time series settlement data; wherein, the starting time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data.

[0007] Based on the first time series settlement data, extract the supplementary time series settlement amount and the time period corresponding to each monitoring point in the first time series settlement data from the adjusted second time series settlement data;

[0008] By using the supplementary time-series settlement data and the time periods corresponding to the extracted monitoring points, the monitoring results corresponding to each monitoring point in the first time-series settlement data are supplemented and extended to obtain the adjusted first time-series settlement data.

[0009] Secondly, this application provides a fusion device based on heterogeneous time-series settlement data, the device comprising:

[0010] The first unit is used to acquire first time-series settlement data and multiple second time-series settlement data. The first time-series settlement data is in SHP format, and the second time-series settlement data is in tag image file format. Each second time-series settlement data corresponds to a different time period. The first time-series settlement data includes multiple monitoring points and monitoring results corresponding to each monitoring point. The monitoring results include the monitoring time, the time-series settlement amount at the monitoring time, and the location information of the monitoring point. Each pixel in the second time-series settlement data corresponds to a location information point, and the pixel value of the pixel is the time-series settlement amount corresponding to the location information of the pixel.

[0011] The second unit is used to adjust the starting reference of the second time series settlement data according to the first time series settlement data to obtain the adjusted second time series settlement data; wherein, the starting time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data.

[0012] The third unit is used to extract, from the adjusted second time series settlement data, the supplementary time series settlement amount corresponding to each monitoring point in the first time series settlement data and the time period corresponding to the supplementary time series settlement amount, based on the first time series settlement data.

[0013] The fourth unit is used to supplement and extend the monitoring results corresponding to each monitoring point in the first time series settlement data by using the supplementary time series settlement amount and the time period corresponding to the supplementary time series settlement amount extracted for each monitoring point, so as to obtain the adjusted first time series settlement data.

[0014] Thirdly, this application provides a readable medium including executable instructions, which, when executed by a processor of an electronic device, cause the electronic device to perform any of the methods described in the first aspect.

[0015] Fourthly, this application provides an electronic device including a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method as described in any of the first aspects.

[0016] As can be seen from the above technical solution, this application has the following beneficial effects compared with the prior art:

[0017] 1. The method proposed in this application can effectively solve the problem of difficulty in fusing heterogeneous data formats. By fusing first time-series settlement data (SHP format) and multiple second time-series settlement data (TIF format), the limitation of data formats is broken, enabling settlement data from different sources to be processed and analyzed within the same framework, thus providing a foundation for obtaining complete settlement monitoring results.

[0018] 2. To address the systemic bias issue between different data sources, this application adjusts the starting benchmark of the second time series settlement data so that the starting time of its corresponding time period is consistent with the latest monitoring time in the first time series settlement data. This eliminates the systemic bias between different data sources, ensures the continuity of time series, and improves the quality of data fusion.

[0019] 3. This application addresses the issues of inconsistent data resolution and different monitoring point locations. During the fusion process, supplementary time-series settlement amounts and time periods corresponding to each monitoring point are extracted from the adjusted second-time-series settlement data based on the first-time-series settlement data. This supplements and extends the monitoring results of each monitoring point in the first-time-series settlement data, achieving an effective temporal connection of settlement data and obtaining longer-series settlement monitoring results. This provides richer and more complete time-dimensional data for applications such as geological hazard risk assessment and urban ground subsidence monitoring, helping to more accurately analyze the changing trends and patterns of surface subsidence, and more timely detect potential geological hazard risks, thereby improving the scientific nature and effectiveness of related decision-making.

[0020] The further effects of the aforementioned non-conventional preferred method will be explained below in conjunction with specific embodiments. Attached Figure Description

[0021] To more clearly illustrate the embodiments of this application or the existing technical solutions, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a fusion method based on heterogeneous time-series settlement data provided in this application;

[0023] Figure 2 A schematic diagram of the structure of a fusion device based on heterogeneous time-series sedimentation data provided in this application;

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] The various non-limiting embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0027] See Figure 1This paper illustrates a fusion method based on heterogeneous time-series settlement data in an embodiment of this application. In this embodiment, the method may include, for example, the following steps:

[0028] S101: Obtain first time series settlement data and multiple second time series settlement data.

[0029] The first time-series settlement data is in SHP data format. SHP data format is a vector data storage format that stores multiple monitoring points obtained from SBAS-InSAR technology. Each point contains values ​​for multiple monitoring dates and latitude and longitude, all of which are called time-series settlement values. It includes many auxiliary files, such as cpg, prj, and shx files. When opened with ArcGIS, it contains many rows of data, each row representing a point. Each point has multiple attributes, such as lon (longitude), lat (latitude), ssqy (region), sslb (data category), 20180110xb (initial settlement value for 20180110), 20180122xb (settlement value for 20180122), and so on. Users can add or modify these attributes themselves.

[0030] The second time-series settlement data is in TIF (Tagged Image File) format, and each second time-series settlement data corresponds to a different time period. TIF data format is a raster data storage format, similar to an image, where each pixel has latitude and longitude location information, and the pixel value represents the settlement amount at that location. In the second time-series settlement data, each pixel value represents the settlement amount at that location. For example, if the currently opened TIF file (i.e., the second time-series settlement data) is TsRaster_20240713, and one pixel has a value of 0.1, it means that the settlement amount at that location from the initial settlement amount (e.g., 20240127) to 20240713 is 0.1.

[0031] The first time-series settlement data includes multiple monitoring points and the corresponding monitoring results for each monitoring point. The monitoring results include the monitoring time, the time-series settlement amount during the monitoring time, and the location information of the monitoring point. In the second time-series settlement data, each pixel corresponds to a location information point, and the pixel value of the pixel is the time-series settlement amount corresponding to the location information of that pixel.

[0032] S102: Based on the first time series settlement data, the starting reference of the second time series settlement data is adjusted to obtain the adjusted second time series settlement data.

[0033] The start time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data.

[0034] As an example, target second-time-series settlement data can be selected from the plurality of second-time-series settlement data; wherein the end time of the time period corresponding to the target second-time-series settlement data is the same as or later than the latest monitoring time of the first-time-series settlement data.

[0035] Then, reference second-time-series settlement data can be determined from the target second-time-series settlement data. The end time of the time period corresponding to the reference second-time-series settlement data is the same as the latest monitoring time in the first-time-series settlement data.

[0036] Finally, the pixel values ​​of the reference second time-series settlement data can be subtracted from the pixel values ​​of the target second time-series settlement data to obtain the adjusted second time-series settlement data.

[0037] For example, first, change the starting reference for the time-series settlement in the TIF format (i.e., the second time-series settlement data). If an algorithm software obtains time-series results in multiple TIF format files (i.e., the second time-series settlement data), TIF_1 represents the initial settlement from 20220101 (all values ​​are 0), TIF_2 represents the settlement from 20220101 to 20220113, TIF_3 represents the settlement from 20220101 to 20220125, ..., TIF_25 represents the settlement from 20220101 to 20231230, ..., TIF_n represents the settlement from 20220101 to 20241212; to change the starting reference, subtract TIF_25 from TIF_25 to TIF_n. This yields the cumulative settlement starting from 20231230. At this point, a series of TIF files (i.e., adjusted second-series settlement data) are obtained, with the last period in the SHP file (i.e., the latest monitoring time in the first-series settlement data) as the starting point.

[0038] In one implementation, after the step of adjusting the starting reference of the second time-series settlement data based on the first time-series settlement data to obtain the adjusted second time-series settlement data, the method may further include:

[0039] The adjusted second time-series settlement data is interpolated to obtain the latest adjusted second time-series settlement data; wherein the interpolation processing method includes one of the following: Kriging interpolation or inverse distance interpolation.

[0040] Understandably, TIF-formatted settlement data (i.e., second-time series settlement data) can be interpolated using methods such as Kriging interpolation and inverse distance interpolation. Since Kriging interpolation considers spatial autocorrelation, its results more accurately reflect reality, making it a suitable choice. The goal is to calculate the settlement amount for points with empty pixel values ​​in the second-time series settlement data, ensuring that each pixel in the data contains settlement information. It's important to note that Kriging interpolation is a geostatistical interpolation method used to predict the values ​​of unknown points based on known values. It's based on the concept of spatial autocorrelation, meaning that points that are closer together are more likely to have similar values. It effectively considers spatial autocorrelation and provides prediction error estimates. It is applicable to various fields requiring spatial data analysis and prediction.

[0041] S103: Based on the first time series settlement data, extract the supplementary time series settlement amount and the time period corresponding to the supplementary time series settlement amount from the adjusted second time series settlement data.

[0042] In this embodiment, supplementary time-series settlement and the corresponding time period for each monitoring point in the first time-series settlement data can be extracted from the adjusted second time-series settlement data based on the first time-series settlement data. In other words, for each monitoring point in the first time-series settlement data, the supplementary time-series settlement and the corresponding time period for each monitoring point can be calculated separately. The time period corresponding to the supplementary time-series settlement can be understood as the time period of the adjusted second time-series settlement data corresponding to the supplementary time-series settlement.

[0043] Specifically, for the i-th monitoring point in the adjusted second time-series settlement data, based on the location information of the i-th monitoring point and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data, the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is determined; the time-series settlement amount corresponding to the target location information in the adjusted second time-series settlement data is extracted; and the extracted time-series settlement amount corresponding to the target location information and the time period corresponding to the adjusted second time-series settlement data are used as the supplementary time-series settlement amount corresponding to the i-th monitoring point and the time period corresponding to the supplementary time-series settlement amount; where i is a positive integer greater than or equal to 1.

[0044] As an example, the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data can be calculated using the following formula:

[0045] col_i_j = int(abs(lon_i - lon_tif_j) / pixel_lon_j);

[0046] row_i_j = int(abs(lat_i - lat_tif_j) / pixel_lat_j);

[0047] Wherein, col_i_j is the row number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; row_i_j is the column number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; int() represents the floor function; abs() represents the absolute value function; lon_i represents the longitude in the location information of the ith monitoring point; lat_i represents the latitude in the location information of the ith monitoring point; lon_tif_j represents the longitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; lat_tif_j represents the latitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; pixel_lon_j represents the length in the size information of the pixel in the j-th adjusted second time-series settlement data; and pixel_lat_j represents the width in the size information of the pixel in the j-th adjusted second time-series settlement data.

[0048] S104: Using the supplementary time-series settlement amount and the time period corresponding to the supplementary time-series settlement amount extracted for each monitoring point, the monitoring results corresponding to each monitoring point in the first time-series settlement data are supplemented and extended to obtain the adjusted first time-series settlement data.

[0049] In this embodiment, the supplementary time-series settlement amounts and corresponding time periods for each extracted monitoring point can be used to supplement and extend the monitoring results for each monitoring point in the first time-series settlement data, resulting in adjusted first time-series settlement data. As an example, the supplementary time-series settlement amounts for each extracted monitoring point can be filled into the monitoring results for each monitoring point in the first time-series settlement data according to the time periods of the supplementary time-series settlement amounts for each monitoring point, thus obtaining adjusted first time-series settlement data. Specifically, the supplementary time-series settlement amounts for each extracted monitoring point can be filled into the monitoring results for each time period of the first time-series settlement data according to the time periods of the supplementary time-series settlement amounts for each monitoring point. In other words, the supplementary time-series settlement values ​​corresponding to each extracted monitoring point are concatenated with the monitoring results corresponding to each monitoring point in the first time-series settlement data. Specifically, the time-series settlement value of the last period (i.e., the latest monitoring time) in the first time-series settlement data can be used as the starting reference for the supplementary time-series settlement values ​​corresponding to each extracted monitoring point, and then added together. That is, the supplementary time-series settlement values ​​corresponding to each extracted monitoring point are directly added to the time-series settlement value of the last period (i.e., the latest monitoring time) in the first time-series settlement data. In this way, the cumulative settlement value (i.e., the time-series settlement value) of all monitoring points in the SHP (i.e., the first time-series settlement data) can be extended from n periods to n+m periods, thus achieving the extension.

[0050] For example, you can iterate through the SHP format settlement data (i.e., the first time-series settlement data) to find the settlement value of each point in each TIF (i.e., the adjusted second time-series settlement data) at each monitoring point in the first time-series settlement data. The specific method is as follows:

[0051] (1) First, obtain the latitude and longitude information of the first point in SHP (lon_1,lat_1) (i.e. the first monitoring point in the first time series settlement data), and the adjusted second time series settlement data, where the adjusted second time series settlement data is the time series settlement information (date_1_lon_1_lat_1,date_2_lon_1_lat_1,date_3_lon_1_lat_1,...,date_n_lon_1_lat_1).

[0052] (2) Calculate the settlement value in the first TIF corresponding to the location of the first monitoring point (i.e., the time series settlement corresponding to it in the first adjusted second time series settlement data) using the location information lon_1,lat_1. The calculation method is as follows:

[0053] Extract the header information from the first TIF file (i.e., the first adjusted second-time-series settlement data). This includes the latitude and longitude information (lon_tif_1, lat_tif_1) in the first row and first column of the TIF file (i.e., the length and width of each pixel (pixel_lon_1, pixel_lat_1), which represents the position and size information of the pixels in the first adjusted second-time-series settlement data. It should be noted that the pixel size information is identical across all pixels in the adjusted second-time-series settlement data; pixel size information can be understood as resolution.

[0054] First, calculate the row and column numbers (col_1, row_1) of (lon_1, lat_1) in the TIF file. The calculation method is as follows:

[0055] col_1_1 = int(abs(lon_1-lon_tif_1) / pixel_lon_1)

[0056] row_1_1 = int(abs(lat_1-lat_tif_1) / pixel_lat_1)

[0057] Among them, col_1_1 `row_1_1` is the row number in the target location information corresponding to the location information of the first monitoring point in the first adjusted second time-series settlement data; `row_1_1` is the column number in the target location information corresponding to the location information of the first monitoring point in the first adjusted second time-series settlement data; `1nt()` represents the integer function; `abs()` represents the absolute value function; `lon_1` represents the longitude in the location information of the first monitoring point; `lat_1` represents the latitude in the location information of the first monitoring point; `lon_t1f_1` represents the longitude in the location information of the first row and first column of the pixel in the first adjusted second time-series settlement data; `lat_t1f_1` represents the latitude in the location information of the first row and first column of the pixel in the first adjusted second time-series settlement data; `pixel_lon_1` represents the length in the size information of the pixel in the first adjusted second time-series settlement data; `pixel_lat_1` represents the width in the size information of the pixel in the first adjusted second time-series settlement data. Int represents integer division, int(2.5) = 2, int(2.9) = 2; Abs represents absolute value, abs(-1) = 1, abs(3) = 3.

[0058] Then read the settlement value tif_1_lon_1_lat_1 at position (col_1, row_1) in the TIF, which is the supplementary time series settlement corresponding to the first monitoring point.

[0059] (3) Calculate the settlement value in the first TIF corresponding to the location of the first monitoring point (i.e., the time series settlement corresponding to it in the first adjusted second time series settlement data) using the location information lon_1,lat_1 of the first monitoring point. The method is as shown in (2). Get tif_1_lon_1_lat_1, which is the supplementary time series settlement corresponding to the first monitoring point, until all the TIF settlement values ​​corresponding to the location information lon_1,lat_1 of the first monitoring point are extracted, until tif_m_lon_1_lat_1, which is the time series settlement corresponding to it in the last (mth) adjusted second time series settlement data.

[0060] (4) Then, perform time-series connection, taking the last period's settlement amount date_n_lon_1_lat_1 at the lon_1,lat_1 position in the SHP (i.e., the first time-series settlement data) as the starting reference for TIF (i.e., the supplementary time-series settlement amount corresponding to each monitoring point), and add them together to obtain the adjusted first time-series settlement data, specifically:

[0061] date_n+1_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_1_lon_1_lat_1;

[0062] date_n+2_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_2_lon_1_lat_1; ...

[0063] date_n+m_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_m_lon_1_lat_1.

[0064] (5) Continue to extract the latitude and longitude information (lon_2, lat_2) of the second point in the SHP, as well as the time series settlement information (date_1_lon_2_lat_2, date_2_lon_2_lat_2, date_3_lon_2_lat_2, ..., date_n_lon_2_lat_2). Use the same methods as (2), (3), and (4) to obtain the adjusted first time series settlement data, specifically:

[0065] date_n+1_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_1_lon_2_lat_2

[0066] date_n+2_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_2_lon_2_lat_2 ...

[0067] date_n+m_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_m_lon_2_lat_2.

[0068] The process continues until all points in the SHP have been extracted and processed, that is, until the supplementary time-series settlement corresponding to all monitoring points has been extracted, and the supplementary time-series settlement corresponding to all the extracted monitoring points has been used to supplement and extend the monitoring results corresponding to each monitoring point in the first time-series settlement data, so as to obtain the adjusted first time-series settlement data.

[0069] In one implementation of this embodiment, the method may further include: smoothing the adjusted first time-series settlement data to obtain smoothed first time-series settlement data.

[0070] The purpose of smoothing the adjusted first time-series settlement data is to reduce noise, make the data trend clearer, and more easily identify the actual settlement changes. The smoothing process can be one of the following: moving average, weighted moving average, wavelet transform, or Gaussian smoothing. In one embodiment, Gaussian smoothing filtering can be used to smooth the adjusted first time-series settlement data to obtain smoothed first time-series settlement data.

[0071] It should be noted that in this embodiment, the first time series settlement data and multiple second time series settlement data must use the same coordinate system. If the coordinate systems are different, they need to be converted to the same coordinate system.

[0072] If the calculated target location information col and row exceed the boundary of the second time series settlement data, then the corresponding point in the first time series settlement data will be deleted. For example, if the size of a tif (i.e., the second time series settlement data) is (300, 500), and the col calculated from the latitude and longitude of a point in the shapefile (i.e., the first time series settlement data) is 305 and row=502, then the point in the shapefile will be deleted.

[0073] The term "heterogeneous" as used in this application specifically refers to two different data formats: SHP and TIF (first time series settlement data and multiple second time series settlement data).

[0074] As can be seen from the above technical solution, this application has the following beneficial effects compared with the prior art:

[0075] 1. The method proposed in this application can effectively solve the problem of difficulty in fusing heterogeneous data formats. By fusing first time-series settlement data (SHP format) and multiple second time-series settlement data (TIF format), the limitation of data formats is broken, enabling settlement data from different sources to be processed and analyzed within the same framework, thus providing a foundation for obtaining complete settlement monitoring results.

[0076] 2. To address the systemic bias issue between different data sources, this application adjusts the starting benchmark of the second time series settlement data so that the starting time of its corresponding time period is consistent with the latest monitoring time in the first time series settlement data. This eliminates the systemic bias between different data sources, ensures the continuity of time series, and improves the quality of data fusion.

[0077] 3. This application addresses the issues of inconsistent data resolution and different monitoring point locations. During the fusion process, supplementary time-series settlement amounts and time periods corresponding to each monitoring point are extracted from the adjusted second-time-series settlement data based on the first-time-series settlement data. This supplements and extends the monitoring results of each monitoring point in the first-time-series settlement data, achieving an effective temporal connection of settlement data and obtaining longer-series settlement monitoring results. This provides richer and more complete time-dimensional data for applications such as geological hazard risk assessment and urban ground subsidence monitoring, helping to more accurately analyze the changing trends and patterns of surface subsidence, and more timely detect potential geological hazard risks, thereby improving the scientific nature and effectiveness of related decision-making.

[0078] In other words, the beneficial effects of this application are as follows:

[0079] (1) Fusion method for SBAS-InSAR data: This method fully considers the characteristics of SBAS-InSAR data (heterogeneous data format, inconsistent resolution, inconsistent benchmark, etc.) and proposes a complete data fusion process.

[0080] (2) Using SHP data (i.e., first time series settlement data) as the benchmark: Taking advantage of the precise location of SHP data points, it is used as the benchmark for time series connection, which ensures the spatial accuracy of the fused data.

[0081] (3) TIF (second time series settlement data) benchmark adjustment method: A time series-based TIF benchmark adjustment method is proposed to eliminate the systematic bias between different data sources.

[0082] (4) Data smoothing: Gaussian smoothing filter improves the quality of the fused data.

[0083] Thus, the method proposed in this application can effectively fuse multi-source SBAS-InSAR time-series settlement data to obtain longer-term and higher-quality settlement monitoring results, providing more reliable data support for applications such as geological disaster risk assessment and urban ground settlement monitoring.

[0084] like Figure 2 The image shows a specific embodiment of a fusion device based on heterogeneous time-series settlement data provided in this application. The device described in this embodiment is the physical device used to perform the method described in the above embodiments. Its technical solution is essentially the same as that of the above embodiments, and the corresponding descriptions in the above embodiments are also applicable to this embodiment. The device includes:

[0085] The first unit 201 is used to acquire first time-series settlement data and multiple second time-series settlement data. The first time-series settlement data is in SHP format, and the second time-series settlement data is in tag image file format. Each second time-series settlement data corresponds to a different time period. The first time-series settlement data includes multiple monitoring points and monitoring results corresponding to each monitoring point. The monitoring results include the monitoring time, the time-series settlement amount at the monitoring time, and the location information of the monitoring point. Each pixel in the second time-series settlement data corresponds to a location information point, and the pixel value of the pixel is the time-series settlement amount corresponding to the location information of the pixel.

[0086] The second unit 202 is used to adjust the starting reference of the second time series settlement data according to the first time series settlement data to obtain the adjusted second time series settlement data; wherein, the starting time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data.

[0087] The third unit 203 is used to extract, from the adjusted second time series settlement data, the supplementary time series settlement amount corresponding to each monitoring point in the first time series settlement data and the time period corresponding to the supplementary time series settlement amount, based on the first time series settlement data.

[0088] The fourth unit 204 is used to supplement and extend the monitoring results corresponding to each monitoring point in the first time series settlement data by using the supplementary time series settlement amount and the time period corresponding to the supplementary time series settlement amount extracted for each monitoring point, so as to obtain the adjusted first time series settlement data.

[0089] Optionally, the second unit 202 is used for:

[0090] From the plurality of second time-series settlement data, target second time-series settlement data is selected; wherein, the end time of the time period corresponding to the target second time-series settlement data is the same as or later than the latest monitoring time of the first time-series settlement data.

[0091] From the target second time series settlement data, a reference second time series settlement data is determined; wherein, the end time of the time period corresponding to the reference second time series settlement data is the same as the latest monitoring time in the first time series settlement data;

[0092] The adjusted second time-series settlement data is obtained by subtracting the pixel values ​​of the reference second time-series settlement data from the pixel values ​​of the target second time-series settlement data.

[0093] Optionally, the device further includes a fifth unit, used to perform interpolation processing on the adjusted second time-series settlement data after the step of adjusting the starting reference of the second time-series settlement data according to the first time-series settlement data to obtain the adjusted second time-series settlement data, so as to obtain the latest adjusted second time-series settlement data.

[0094] The interpolation processing method includes one of the following: Kriging interpolation or inverse distance interpolation.

[0095] Optionally, the third unit 203 is used for:

[0096] For the i-th monitoring point in the adjusted second time-series settlement data, based on the location information of the i-th monitoring point and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data, the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is determined; the time-series settlement amount corresponding to the target location information in the adjusted second time-series settlement data is extracted, and the extracted time-series settlement amount corresponding to the target location information and the time period corresponding to the adjusted second time-series settlement data are used as the supplementary time-series settlement amount corresponding to the i-th monitoring point and the time period corresponding to the supplementary time-series settlement amount; where i is a positive integer greater than or equal to 1.

[0097] Optionally, the third unit 203 is used for:

[0098] The target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is calculated using the following formula:

[0099] col_i_j = int(abs(lon_i - lon_tif_j) / pixel_lon_j);

[0100] row_i_j = int(abs(lat_i - lat_tif_j) / pixel_lat_j);

[0101] Wherein, col_i_j is the row number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; row_i_j is the column number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; int() represents the floor function; abs() represents the absolute value function; lon_i represents the longitude in the location information of the ith monitoring point; lat_i represents the latitude in the location information of the ith monitoring point; lon_tif_j represents the longitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; lat_tif_j represents the latitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; pixel_lon_j represents the length in the size information of the pixel in the j-th adjusted second time-series settlement data; and pixel_lat_j represents the width in the size information of the pixel in the j-th adjusted second time-series settlement data.

[0102] Optionally, the fourth unit 204 is used for:

[0103] The extracted time-series settlement data corresponding to each monitoring point is filled into the monitoring results corresponding to each monitoring point in the first time-series settlement data according to the time period of the supplementary time-series settlement data corresponding to each monitoring point, so as to obtain the adjusted first time-series settlement data.

[0104] Optionally, the device further includes a sixth unit for:

[0105] The adjusted first time-series settlement data is smoothed to obtain smoothed first time-series settlement data.

[0106] In this way, the device can achieve heterogeneous data integration, benchmark unification, improved spatial matching accuracy, and acquisition of high-quality long-term settlement monitoring data, providing reliable data support for related applications.

[0107] Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0108] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0109] Memory is used to store instructions for execution. Specifically, instructions for execution are computer programs that can be executed. Memory can include main memory and non-volatile memory, and it provides the processor with execution instructions and data.

[0110] In one possible implementation, the processor reads the corresponding execution instructions from non-volatile memory into memory and then executes them. Alternatively, it may obtain the corresponding execution instructions from other devices to form a fusion device based on heterogeneous time-series settling data at the logical level. The processor executes the execution instructions stored in memory to implement the fusion method based on heterogeneous time-series settling data provided in any embodiment of this application.

[0111] The above is as stated in this application. Figure 1The method executed by the fusion device based on heterogeneous time-series settling data provided in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor.

[0112] The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0113] This application also proposes a readable medium that stores execution instructions. When the stored execution instructions are executed by the processor of an electronic device, the electronic device can execute the fusion method based on heterogeneous time-series sedimentation data provided in any embodiment of this application, and specifically perform the above-mentioned evaluation method.

[0114] The electronic devices described in the foregoing embodiments may be computers.

[0115] Those skilled in the art will understand that the embodiments of this application can be provided as methods or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0116] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0117] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A fusion method based on heterogeneous time-series settlement data, characterized in that, The method includes: Acquire first time-series settlement data and multiple second time-series settlement data; wherein, the first time-series settlement data is in SHP data format, and the second time-series settlement data is in tag image file format, and each second time-series settlement data corresponds to a different time period; the first time-series settlement data includes multiple monitoring points and the monitoring results corresponding to each monitoring point, wherein the monitoring results include monitoring time, time-series settlement amount at the monitoring time, and location information of the monitoring point; each pixel in the second time-series settlement data corresponds to a location information, and the pixel value of the pixel is the time-series settlement amount of the location information corresponding to the pixel; Based on the first time series settlement data, the starting reference of the second time series settlement data is adjusted to obtain the adjusted second time series settlement data; wherein, the starting time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data. Based on the first time series settlement data, extract the supplementary time series settlement amount and the time period corresponding to each monitoring point in the first time series settlement data from the adjusted second time series settlement data; Using the supplementary time-series settlement data and the time period corresponding to each of the extracted monitoring points, the monitoring results corresponding to each monitoring point in the first time-series settlement data are supplemented and extended to obtain the adjusted first time-series settlement data. The step of extracting the supplementary time-series settlement amount and the time period corresponding to each monitoring point in the first time-series settlement data from the adjusted second time-series settlement data based on the first time-series settlement data includes: For the i-th monitoring point in the adjusted second time-series settlement data, based on the location information of the i-th monitoring point, and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data, the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is determined; the time-series settlement amount corresponding to the target location information in the adjusted second time-series settlement data is extracted, and the extracted time-series settlement amount corresponding to the target location information and the time period corresponding to the adjusted second time-series settlement data are used as the supplementary time-series settlement amount corresponding to the i-th monitoring point and the time period corresponding to the supplementary time-series settlement amount; where i is a positive integer greater than or equal to 1; The step of determining the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data based on the location information of the i-th monitoring point and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data includes: The target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is calculated using the following formula: col_i_j = int(abs(lon_i - lon_tif_j) / pixel_lon_j); row_i_j = int(abs(lat_i - lat_tif_j) / pixel_lat_j); Wherein, col_i_j is the row number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; row_i_j is the column number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; int() represents the floor function; abs() represents the absolute value function; lon_i represents the longitude in the location information of the ith monitoring point; lat_i represents the latitude in the location information of the ith monitoring point; lon_tif_j represents the longitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; lat_tif_j represents the latitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; pixel_lon_j represents the length in the size information of the pixel in the j-th adjusted second time-series settlement data; and pixel_lat_j represents the width in the size information of the pixel in the j-th adjusted second time-series settlement data.

2. The method according to claim 1, characterized in that, The step of adjusting the starting reference of the second time-series settlement data based on the first time-series settlement data to obtain the adjusted second time-series settlement data includes: From the plurality of second time-series settlement data, target second time-series settlement data is selected; wherein, the end time of the time period corresponding to the target second time-series settlement data is the same as or later than the latest monitoring time of the first time-series settlement data. From the target second time series settlement data, a reference second time series settlement data is determined; wherein, the end time of the time period corresponding to the reference second time series settlement data is the same as the latest monitoring time in the first time series settlement data; The adjusted second time-series settlement data is obtained by subtracting the pixel values ​​of the reference second time-series settlement data from the pixel values ​​of the target second time-series settlement data.

3. The method according to claim 1, characterized in that, After the step of adjusting the starting reference of the second time-series settlement data based on the first time-series settlement data to obtain the adjusted second time-series settlement data, the method further includes: The adjusted second time-series settlement data are interpolated to obtain the latest adjusted second time-series settlement data; The interpolation processing method includes one of the following: Kriging interpolation or inverse distance interpolation.

4. The method according to claim 1, characterized in that, The process involves supplementing and extending the monitoring results for each monitoring point in the first time-series settlement data using the extracted supplementary time-series settlement data and the corresponding time periods, to obtain adjusted first time-series settlement data, including: The extracted time-series settlement data corresponding to each monitoring point is filled into the monitoring results corresponding to each monitoring point in the first time-series settlement data according to the time period of the supplementary time-series settlement data corresponding to each monitoring point, so as to obtain the adjusted first time-series settlement data.

5. The method according to claim 1, characterized in that, The method further includes: The adjusted first time-series settlement data is smoothed to obtain smoothed first time-series settlement data.

6. A fusion device based on heterogeneous time-series settlement data, characterized in that, The device includes: The first unit is used to acquire first time-series settlement data and multiple second time-series settlement data. The first time-series settlement data is in SHP format, and the second time-series settlement data is in tag image file format. Each second time-series settlement data corresponds to a different time period. The first time-series settlement data includes multiple monitoring points and monitoring results corresponding to each monitoring point. The monitoring results include the monitoring time, the time-series settlement amount at the monitoring time, and the location information of the monitoring point. Each pixel in the second time-series settlement data corresponds to a location information point, and the pixel value of the pixel is the time-series settlement amount corresponding to the location information of the pixel. The second unit is used to adjust the starting reference of the second time series settlement data according to the first time series settlement data to obtain the adjusted second time series settlement data; wherein, the starting time of the time period corresponding to the adjusted second time series settlement data is the latest monitoring time in the first time series settlement data. The third unit is used to extract, from the adjusted second time series settlement data, the supplementary time series settlement amount corresponding to each monitoring point in the first time series settlement data and the time period corresponding to the supplementary time series settlement amount, based on the first time series settlement data. The fourth unit is used to supplement and extend the monitoring results of each monitoring point in the first time series settlement data by using the supplementary time series settlement amount and the time period corresponding to the supplementary time series settlement amount extracted for each monitoring point, so as to obtain the adjusted first time series settlement data. The step of extracting the supplementary time-series settlement amount and the time period corresponding to each monitoring point in the first time-series settlement data from the adjusted second time-series settlement data based on the first time-series settlement data includes: For the i-th monitoring point in the adjusted second time-series settlement data, based on the location information of the i-th monitoring point, and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data, the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is determined; the time-series settlement amount corresponding to the target location information in the adjusted second time-series settlement data is extracted, and the extracted time-series settlement amount corresponding to the target location information and the time period corresponding to the adjusted second time-series settlement data are used as the supplementary time-series settlement amount corresponding to the i-th monitoring point and the time period corresponding to the supplementary time-series settlement amount; where i is a positive integer greater than or equal to 1; The step of determining the target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data based on the location information of the i-th monitoring point and the location and size information of the pixels in the i-th row and i-th column of the adjusted second time-series settlement data includes: The target location information corresponding to the location information of the i-th monitoring point in the adjusted second time-series settlement data is calculated using the following formula: col_i_j = int(abs(lon_i - lon_tif_j) / pixel_lon_j); row_i_j = int(abs(lat_i - lat_tif_j) / pixel_lat_j); Wherein, col_i_j is the row number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; row_i_j is the column number of the target location information corresponding to the location information of the ith monitoring point in the j-th adjusted second time-series settlement data; int() represents the floor function; abs() represents the absolute value function; lon_i represents the longitude in the location information of the ith monitoring point; lat_i represents the latitude in the location information of the ith monitoring point; lon_tif_j represents the longitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; lat_tif_j represents the latitude in the location information of the first row and first column of the j-th adjusted second time-series settlement data; pixel_lon_j represents the length in the size information of the pixel in the j-th adjusted second time-series settlement data; and pixel_lat_j represents the width in the size information of the pixel in the j-th adjusted second time-series settlement data.

7. A readable medium, characterized in that, The readable medium includes execution instructions that, when executed by the processor of the electronic device, cause the electronic device to perform the method as described in any one of claims 1-5.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory storing execution instructions. When the processor executes the execution instructions stored in the memory, the processor performs the method as described in any one of claims 1-5.

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