Fusion method and device based on heterogeneous time sequence settlement data

Abstract

The invention belongs to the technical field of time sequence settlement data, and discloses a fusion method and device based on heterogeneous time sequence settlement data. According to the method, heterogeneous data integration, standard unification, improvement of space matching precision and acquisition of long-time-sequence high-quality settlement monitoring data can be realized, and reliable data support is provided for related applications.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of time series subsidence data, and particularly relates to a fusion method and device based on heterogeneous time series subsidence data. BACKGROUND

[0002] In the field of surface subsidence monitoring, with the continuous development of technology, various monitoring means have appeared, such as SBAS-InSAR technology. SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) is a radar interferometric measurement technology. The technology uses multi-period short baseline radar images to effectively monitor the micro deformation of the ground surface through differential interferometric measurement. The technology can use multi-period short baseline radar images to monitor the micro deformation of the ground surface through differential interferometric measurement. However, in the actual application process, there are some problems to be solved. On the one hand, the storage formats of the time series subsidence obtained by using different software of SBAS-InSAR technology are different, for example, there are SHP data format and TIF data format. The two formats belong to heterogeneous data formats, and it is difficult to directly perform fusion processing. On the other hand, even for the same region, the resolution of data from different sources is inconsistent, and the monitoring point position of each result is not the same, which leads to the incoherence of data in time and space, and it is difficult to obtain complete and continuous cumulative subsidence data. In addition, there may be system bias between different data sources, which affects the accuracy and reliability of the subsidence monitoring data, and further brings inconvenience and limitation to the related applications such as geological disaster risk assessment and urban ground subsidence monitoring. SUMMARY

[0003] The present application provides a fusion method and device based on heterogeneous time series subsidence data, which can realize heterogeneous data integration, reference unification, spatial matching precision improvement and acquisition of long-time series high-quality subsidence monitoring data, and provides reliable data support for related applications.

[0004] In a first aspect, the present application provides a fusion method based on heterogeneous time series subsidence data, the method comprising: The first time sequence subsidence data and the plurality of second time sequence subsidence data are obtained; wherein, the first time sequence subsidence data is in SHP data format, and the second time sequence subsidence data is in label image file format; each second time sequence subsidence data corresponds to a different time period; the first time sequence subsidence data comprises a plurality of monitoring points and monitoring results corresponding to each monitoring point, wherein the monitoring results comprise monitoring time, time sequence subsidence amount at the monitoring time, and position information of the monitoring point; each pixel point in the second time sequence subsidence data corresponds to position information, and a pixel value of the pixel point is a time sequence subsidence amount of the position information corresponding to the pixel point. The starting reference of the second time sequence subsidence data is adjusted according to the first time sequence subsidence data, to obtain adjusted second time sequence subsidence data; wherein, a starting time of the time period corresponding to the adjusted second time sequence subsidence data is the latest monitoring time in the first time sequence subsidence data. The first time sequence subsidence data, from the adjusted second time sequence subsidence data, extracts the supplementary time sequence subsidence amount corresponding to each monitoring point in the first time sequence subsidence data and the time period corresponding to the supplementary time sequence subsidence amount. The monitoring results corresponding to each monitoring point in the first time sequence subsidence data are supplemented and lengthened by using the extracted supplementary time sequence subsidence amount corresponding to each monitoring point and the time period corresponding to the supplementary time sequence subsidence amount, to obtain adjusted first time sequence subsidence data.

[0005] In a second aspect, the application provides a fusion device based on heterogeneous time sequence subsidence data, the device comprising: A first unit is configured to obtain first time sequence subsidence data and a plurality of second time sequence subsidence data; wherein, the first time sequence subsidence data is in SHP data format, and the second time sequence subsidence data is in label image file format; each second time sequence subsidence data corresponds to a different time period; the first time sequence subsidence data comprises a plurality of monitoring points and monitoring results corresponding to each monitoring point, wherein the monitoring results comprise monitoring time, time sequence subsidence amount at the monitoring time, and position information of the monitoring point; each pixel point in the second time sequence subsidence data corresponds to position information, and a pixel value of the pixel point is a time sequence subsidence amount of the position information corresponding to the pixel point. A second unit is configured to adjust the starting reference of the second time sequence subsidence data according to the first time sequence subsidence data, to obtain adjusted second time sequence subsidence data; wherein, a starting time of the time period corresponding to the adjusted second time sequence subsidence data is the latest monitoring time in the first time sequence subsidence data. A third unit is configured to extract, from the adjusted second time sequence subsidence data, the supplementary time sequence subsidence amount corresponding to each monitoring point in the first time sequence subsidence data and the time period corresponding to the supplementary time sequence subsidence amount, according to the first time sequence subsidence data. The fourth unit is configured to supplementally extend the monitoring result of each monitoring point in the first time-series subsidence data by using the extracted supplementary time-series subsidence amount corresponding to each monitoring point and the time period corresponding to the supplementary time-series subsidence amount, to obtain adjusted first time-series subsidence data.

[0006] In a third aspect, the present application provides a readable medium comprising execution instructions, when a processor of an electronic device executes the execution instructions, the electronic device executes the method according to any one of the first aspect.

[0007] In a fourth aspect, the present application provides an electronic device comprising a processor and a memory storing execution instructions, when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of the first aspect.

[0008] From the above technical solutions, the present application has the following beneficial effects compared with the prior art: 1. The method provided by the present application can effectively solve the problem of difficult fusion of heterogeneous data formats. By fusing the first time-series subsidence data (SHP format) and the plurality of second time-series subsidence data (TIF format), the limitation of data format is broken, so that subsidence data of different sources can be processed and analyzed under the same framework, providing a basis for obtaining complete subsidence monitoring results.

[0009] 2. For the system deviation problem between different data sources, the present application adjusts the starting reference of the second time-series subsidence data, so that the starting time of the corresponding time period is consistent with the latest monitoring time in the first time-series subsidence data, thereby eliminating the system deviation between different data sources, ensuring the time series continuity, and improving the data fusion quality.

[0010] 3. The present application solves the problem of inconsistent data resolution and different monitoring point positions. In the fusion process, the supplementary time-series subsidence amount and the time period corresponding to each monitoring point are extracted from the adjusted second time-series subsidence data according to the first time-series subsidence data, and then the monitoring result of each monitoring point in the first time-series subsidence data is supplemented and extended, realizing effective connection of subsidence data in time, obtaining longer time-series subsidence monitoring results, providing richer and more complete time dimension data for geological disaster risk assessment, urban ground subsidence monitoring and other applications, and helping to more accurately analyze the change trend and law of surface subsidence, and more timely discover potential geological disaster risks, and improve the scientificity and effectiveness of related decisions.

[0011] The further effects of the above-mentioned non-conventional preferred modes will be described in the following with reference to the specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0013] Figure 1 A flowchart of a fusion method based on heterogeneous time series subsidence data provided by the present application; Figure 2 A structural diagram of a fusion device based on heterogeneous time series subsidence data provided by the present application; Figure 3 A structural diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and advantages of the present application more clear, the technical scheme of the present application will be described clearly and completely in the following with specific embodiments and corresponding drawings. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0015] The various non-limiting embodiments of the present application will be described in detail below with reference to the drawings.

[0016] Referring to Figure 1 , a fusion method based on heterogeneous time series subsidence data in the embodiments of the present application is shown. In the present embodiment, the method may, for example, include the following steps: S101: obtaining first time series subsidence data and a plurality of second time series subsidence data.

[0017] The format of the first time series subsidence data is SHP data format. SHP data format is a kind of vector data storage format, which stores a plurality of monitoring points obtained by SBAS-InSAR technology, each point contains values of multiple monitoring dates and longitude and latitude, all values are called time series subsidence. Including many auxiliary files, such as cpg, prj, shx and multiple files, after opening by arcgis, including many rows of data, each row is a point, each point has multiple attributes, such as lon (longitude), lat (latitude), ssqy (belonging area), sslb (data category), 20180110xb (20180110 initial subsidence), 20180122xb (20180122 subsidence), and so on, the attributes can be added or modified by the user.

[0018] The second time-series deposition data is in a Tagged Image File Format (TIF data format), and each second time-series deposition data corresponds to a different time period. The TIF data format is a storage format of raster data, similar to an image, each pixel has latitude and longitude position information, and the pixel value is the deposition amount at the position. Each pixel value in the second time-series deposition data represents the deposition amount at the position. Assuming that the currently opened tif file (i.e., the second time-series deposition data) is TsRaster_20240713, and the pixel value of one pixel point is 0.1, it means that the deposition amount at this position from the starting deposition amount (assuming 20240127) to 20240713 is 0.1.

[0019] The first time-series deposition data includes a plurality of monitoring points and monitoring results corresponding to each monitoring point, wherein the monitoring results include monitoring time, time-series deposition amount at the monitoring time, and position information of the monitoring point. Each pixel point in the second time-series deposition data corresponds to a position information, and the pixel value of the pixel point is the time-series deposition amount of the position information corresponding to the pixel point.

[0020] S102: Adjusting the starting reference of the second time-series deposition data according to the first time-series deposition data to obtain adjusted second time-series deposition data.

[0021] The starting time of the time period corresponding to the adjusted second time-series deposition data is the latest monitoring time in the first time-series deposition data.

[0022] As an example, the target second time-series deposition data can be selected from the plurality of second time-series deposition data; wherein the termination time of the time period corresponding to the target second time-series deposition data is the same as or later than the latest monitoring time in the first time-series deposition data.

[0023] Then, the reference second time-series deposition data can be determined from the target second time-series deposition data; wherein the termination time of the time period corresponding to the reference second time-series deposition data is the same as the latest monitoring time in the first time-series deposition data.

[0024] Finally, the pixel value in the target second time-series deposition data can be subtracted by the pixel value of the reference second time-series deposition data to obtain the adjusted second time-series deposition data.

[0025] For example, first change the starting reference of the timing subsidence amount of the TIF format (i.e. the second timing subsidence data). As the timing result obtained by some algorithm software is a plurality of TIF format files (i.e. the second timing subsidence data), TIF_1 represents the starting subsidence amount of 20220101, all values are 0, TIF_2 represents the subsidence amount from 20220101 to 20220113, TIF_3 represents the subsidence amount from 20220101 to 20220125,... TIF_25 represents the subsidence amount from 20220101 to 20231230,..., TIF_n represents the subsidence amount from 20220101 to 20241212; to change the starting reference, subtract TIF_25 from TIF_25 to TIF_n, at this time, the cumulative subsidence amount with 20231230 as the starting reference is obtained. At this time, a series of TIF files (i.e. the adjusted second timing subsidence data) with the last period (i.e. the latest monitoring time in the first timing subsidence data) in the SHP file (i.e. the first timing subsidence data) as the starting reference are obtained.

[0026] In an implementation manner, after the step of adjusting the starting reference of the second timing subsidence data according to the first timing subsidence data to obtain the adjusted second timing subsidence data, the method can further include: performing interpolation processing on the adjusted second timing subsidence data to obtain the latest adjusted second timing subsidence data; wherein the processing manner of the interpolation processing includes one of the following manners: Kriging interpolation, inverse distance interpolation.

[0027] It can be understood that the TIF format subsidence data (i.e. the second timing subsidence data) can be interpolated, such as Kriging interpolation, inverse distance interpolation and other interpolation methods. Since Kriging interpolation can consider spatial autocorrelation, the interpolation result is more in line with the actual situation, so Kriging interpolation can be used. The purpose is to calculate the subsidence amount of the point where the pixel value is empty in the second timing subsidence data. So that each pixel value in the second timing subsidence data has subsidence amount information. It should be noted that Kriging interpolation method (Kriging Interpolation) is a geostatistical interpolation method, which is used to predict the value of an unknown point according to the value of a known point. It is based on the concept of spatial autocorrelation, that is, points with closer distances are more likely to have similar values. It can effectively consider spatial autocorrelation and provide prediction error estimation. It is suitable for various fields that need to analyze and predict spatial data.

[0028] S103: According to the first timing subsidence data, from the adjusted second timing subsidence data, extract the supplementary timing subsidence amount corresponding to each monitoring point in the first timing subsidence data and the time period corresponding to the supplementary timing subsidence amount.

[0029] In the embodiment, the supplementary time-dependent settlement amount corresponding to each monitoring point in the first time-dependent settlement data and the time period corresponding to the supplementary time-dependent settlement amount can be extracted from the adjusted second time-dependent settlement data according to the first time-dependent settlement data. That is, the supplementary time-dependent settlement amount corresponding to each monitoring point and the time period corresponding to the supplementary time-dependent settlement amount can be calculated for each monitoring point in the first time-dependent settlement data, respectively. The time period corresponding to the supplementary time-dependent settlement amount can be understood as the time period of the adjusted second time-dependent settlement data corresponding to the supplementary time-dependent settlement amount.

[0030] Specifically, for the i th monitoring point in the adjusted second time-dependent settlement data, the target position information corresponding to the position information of the i th monitoring point in the adjusted second time-dependent settlement data is determined according to the position information of the i th monitoring point, and the position information and size information of the pixel point in the i th row and i th column in the adjusted second time-dependent settlement data. The time-dependent settlement amount corresponding to the target position information in the adjusted second time-dependent settlement data is extracted, and the extracted time-dependent settlement amount corresponding to the target position information and the time period corresponding to the adjusted second time-dependent settlement data are taken as the supplementary time-dependent settlement amount corresponding to the i th monitoring point and the time period corresponding to the supplementary time-dependent settlement amount. Wherein, i is a positive integer greater than or equal to 1.

[0031] As an example, the target position information corresponding to the position information of the i th monitoring point in the adjusted second time-dependent settlement data can be calculated by 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 position information corresponding to the position information of the i th monitoring point in the j th adjusted second time series subsidence data; row_i_j is the column number of the target position information corresponding to the position information of the i th monitoring point in the j th adjusted second time series subsidence data; int() represents the integer function; abs() represents the absolute value function; lon_i represents the longitude in the position information of the i th monitoring point; lat_i represents the latitude in the position information of the i th monitoring point; lon_tif_j represents the longitude of the position information of the pixel point in the first row and the first column in the j th adjusted second time series subsidence data; lat_tif_j represents the latitude of the position information of the pixel point in the first row and the first column in the j th adjusted second time series subsidence data; pixel_lon_j represents the length in the size information of the pixel point in the j th adjusted second time series subsidence data; pixel_lat_j represents the width in the size information of the pixel point in the j th adjusted second time series subsidence data.

[0032] S104: The monitoring results corresponding to each monitoring point in the first time series subsidence data are supplemented and extended by using the extracted supplementary time series subsidence corresponding to each monitoring point and the time period corresponding to the supplementary time series subsidence, to obtain adjusted first time series subsidence data.

[0033] In the embodiment, the monitoring result corresponding to each monitoring point in the first time-series subsidence data can be supplemented and extended by using the extracted supplementary time-series subsidence amount corresponding to each monitoring point and the time period corresponding to the supplementary time-series subsidence amount. As an example, the extracted supplementary time-series subsidence amount corresponding to each monitoring point can be filled into the monitoring result corresponding to each monitoring point in the first time-series subsidence data according to the time period of the supplementary time-series subsidence amount corresponding to each monitoring point, to obtain the adjusted first time-series subsidence data. Specifically, the extracted supplementary time-series subsidence amount corresponding to each monitoring point can be filled into the monitoring result corresponding to the time period of each monitoring point in the first time-series subsidence data according to the time period of the supplementary time-series subsidence amount corresponding to each monitoring point. That is, the extracted supplementary time-series subsidence amount corresponding to each monitoring point and the monitoring result corresponding to each monitoring point in the first time-series subsidence data are connected, and specifically, the time-series subsidence amount of the last period (i.e., the latest monitoring time) in the first time-series subsidence data can be taken as the starting reference of the extracted supplementary time-series subsidence amount corresponding to each monitoring point, and the supplementary time-series subsidence amount corresponding to each monitoring point can be added to the time-series subsidence amount of the last period (i.e., the latest monitoring time) in the first time-series subsidence data, that is, the supplementary time-series subsidence amount corresponding to each monitoring point can be directly added behind the time-series subsidence amount of the last period (i.e., the latest monitoring time) in the first time-series subsidence data. In this way, the cumulative subsidence amount (i.e., the time-series subsidence amount) of all monitoring points in the SHP (i.e., the first time-series subsidence data) can be extended from n periods to n+m periods, thereby achieving the extension.

[0034] For example, the SHP format subsidence data (i.e., the first time-series subsidence data) can be traversed to find the subsidence value of each point at the corresponding position in each TIF (i.e., the adjusted second time-series subsidence data). The specific method is as follows: (1) First, the longitude and latitude information (lon_1, lat_1) of the first point in the SHP (i.e., the first monitoring point in the first time-series subsidence data) is obtained, and the adjusted second time-series subsidence data is obtained, wherein the adjusted second time-series subsidence data is the time-series subsidence amount 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).

[0035] (2) The subsidence value in the first TIF corresponding to the position of the first monitoring point (i.e., the time-series subsidence amount corresponding to the first adjusted second time-series subsidence data) is calculated by using the position information lon_1, lat_1 of the first monitoring point, and the calculation method is as follows: Extract the header file information of the first TIF file (i.e. the first adjusted second time series subsidence data), including the longitude and latitude information (lon_tif_1, lat_tif_1) of the first row and first column of the TIF file (i.e. the first adjusted second time series subsidence data), and the length and width (pixel_lon_1, pixel_lat_1) of each pixel, i.e. the position information and size information of the pixel in the first adjusted second time series subsidence data. It should be noted that the size information of the pixel in the adjusted second time series subsidence data is the same, and the size information of the pixel can be understood as the resolution.

[0036] The row and column numbers (col_1, row_1) of (lon_1, lat_1) in TIF can be calculated first, and the calculation method is as follows: col_1_1 = int(abs(lon_1-lon_tif_1) / pixel_lon_1) row_1_1 = int(abs(lat_1-lat_tif_1) / pixel_lat_1) Where col_1_1 is the row number in the target position information in the first adjusted second time series subsidence data corresponding to the position information of the first monitoring point; row_1_1 is the column number in the target position information in the first adjusted second time series subsidence data corresponding to the position information of the first monitoring point; int() represents the integer function; abs() represents the absolute value function; lon_1 represents the longitude in the position information of the first monitoring point; lat_1 represents the latitude in the position information of the first monitoring point; lon_t1f_1 represents the longitude in the position information of the first pixel in the first row and first column of the first adjusted second time series subsidence data; lat_t1f_1 represents the latitude in the position information of the first pixel in the first row and first column of the first adjusted second time series subsidence data; pixel_lon_1 represents the length in the size information of the pixel in the first adjusted second time series subsidence data; pixel_lat_1 represents the width in the size information of the pixel in the first adjusted second time series subsidence data. Int represents rounding, int(2.5)=2, int(2.9)=2, Abs represents absolute value, abs(-1)=1, abs(3)=3.

[0037] Then read the subsidence value tif_1_lon_1_lat_1 at the position (col_1, row_1) in TIF, i.e. the supplementary time series subsidence corresponding to the first monitoring point.

[0038] (3) The first TIF settlement value corresponding to the position (i.e. the time series settlement amount corresponding to the first adjusted second time series settlement data) is calculated by the position information lon_1, lat_1 of the first monitoring point, and the method is shown in the corresponding content of (2). The tif_1_lon_1_lat_1, i.e. the first monitoring point corresponding to the supplementary time series settlement amount, is obtained. Until the TIF settlement value of the position corresponding to the position information lon_1, lat_1 of the first monitoring point is extracted, and until tif_m_lon_1_lat_1, i.e. the time series settlement amount corresponding to the last (mth) adjusted second time series settlement data.

[0039] (4) Then, the last period settlement amount date_n_lon_1_lat_1 at the position lon_1, lat_1 in SHP (i.e. the first time series settlement data) is taken as the starting reference of TIF (i.e. the supplementary time series settlement amount corresponding to each monitoring point), and the addition is performed to obtain the adjusted first time series settlement data, which is specifically: date_n+1_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_1_lon_1_lat_1; date_n+2_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_2_lon_1_lat_1; ... date_n+m_lon_1_lat_1 = date_n_lon_1_lat_1 + tif_m_lon_1_lat_1.

[0040] (5) Continue to extract the longitude and latitude information (lon_2, lat_2) of the second point in SHP, and the time series settlement amount 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), and use the same method as (2), (3), (4) to obtain the adjusted first time series settlement data, which is specifically: date_n+1_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_1_lon_2_lat_2 date_n+2_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_2_lon_2_lat_2 ... date_n+m_lon_2_lat_2 = date_n_lon_2_lat_2 + tif_m_lon_2_lat_2.

[0041] Until all points in the SHP are extracted and processed, that is, all the supplementary timing subsidence of the monitoring points is extracted, and the extracted supplementary timing subsidence of all monitoring points is supplemented to the monitoring results of each monitoring point in the first timing subsidence data to obtain the adjusted first timing subsidence data.

[0042] In an implementation manner of the embodiment, the method can further include: performing smoothing processing on the adjusted first timing subsidence data to obtain smoothed first timing subsidence data.

[0043] The purpose of performing smoothing processing on the adjusted first timing subsidence data is to reduce noise, make the data trend clearer, and more easily identify the real subsidence change. The processing manner of the smoothing processing can be one of the following manners: moving average, weighted moving average, wavelet transform, and Gaussian smoothing. In an implementation manner, the adjusted first timing subsidence data can be smoothed by using a Gaussian smoothing filtering manner to obtain smoothed first timing subsidence data.

[0044] It should be noted that in the embodiment, the first timing subsidence data and the plurality of second timing subsidence data must use the same coordinate system, and if the coordinate systems are different, the coordinate systems need to be converted to the same coordinate system.

[0045] If the target position information col and row calculated exceeds the boundary of the second timing subsidence data, the point in the corresponding first timing subsidence data is deleted. For example, if the size of a tif (that is, the second timing subsidence data) is (300, 500), and the col calculated by the longitude and latitude of a point in the shp (that is, the first timing subsidence data) is 305, and row = 502, the point in the shp is deleted.

[0046] The "heterogeneous" mentioned in the application specifically refers to two different data formats of SHP and TIF (the first timing subsidence data and the plurality of second timing subsidence data).

[0047] It can be seen from the above technical solution that the application has the following beneficial effects compared with the prior art: 1. The method proposed in the application can effectively solve the problem of difficulty in fusing heterogeneous data formats. By fusing the first time series subsidence data (SHP format) and the plurality of second time series subsidence data (TIF format), the limitation of data format is broken, so that subsidence data of different sources can be processed and analyzed under the same framework, providing a basis for obtaining complete subsidence monitoring results subsequently.

[0048] 2. For the system deviation problem between different data sources, the application adjusts the starting reference of the second time series subsidence data, so that the starting time of the corresponding time period is consistent with the latest monitoring time in the first time series subsidence data, thereby eliminating the system deviation between different data sources, ensuring the time series continuity, and improving the data fusion quality.

[0049] 3. The application solves the problem of inconsistent data resolution and different monitoring point positions. In the fusion process, the corresponding supplementary time series subsidence and time period of each monitoring point are extracted from the adjusted second time series subsidence data according to the first time series subsidence data, and then the monitoring results of each monitoring point in the first time series subsidence data are supplemented and extended, realizing effective connection of subsidence data in time, obtaining longer time series subsidence monitoring results, providing more rich and complete time dimension data for geological disaster risk assessment, urban ground subsidence monitoring and other applications, and helping to more accurately analyze the change trend and law of surface subsidence, and more timely discover potential geological disaster risks, improving the scientificity and effectiveness of related decisions.

[0050] That is, the beneficial effects of the application are as follows: (1) Fusion method for SBAS-InSAR data: This method fully considers the characteristics of SBAS-InSAR data (heterogeneous data format, inconsistent resolution, inconsistent reference, etc.), and proposes a complete data fusion process.

[0051] (2) SHP data (i.e. first time series subsidence data) as reference: Using the accurate point position characteristics of SHP data as the reference for time series connection, the spatial accuracy of the fused data is ensured.

[0052] (3) TIF (i.e. second time series subsidence data) reference adjustment method: A TIF reference adjustment method based on time series is proposed, which eliminates the system deviation between different data sources.

[0053] (4) Data smoothing: Through Gaussian smoothing filtering, the quality of the fused data is improved.

[0054] Thus, by the method provided in the present application, effective fusion of multi-source SBAS-InSAR time-series subsidence data can be achieved, and longer time-series and higher quality subsidence monitoring results can be obtained, thereby providing more reliable data support for geological disaster risk assessment, urban ground subsidence monitoring and other applications.

[0055] As shown in Figure 2 Fig. 1 is a specific embodiment of a fusion device based on heterogeneous time-series subsidence data provided by the present application. The device described in this embodiment, i.e., the entity device for executing the method described in the above embodiment, has the same essence as the above embodiment, and the corresponding description in the above embodiment is also applicable to this embodiment. The device comprises: A first unit 201 is configured to obtain first time-series subsidence data and a plurality of second time-series subsidence data. The format of the first time-series subsidence data is SHP data format, and the format of the second time-series subsidence data is label image file format. The time period corresponding to each second time-series subsidence data is different. The first time-series subsidence data comprises a plurality of monitoring points and monitoring results corresponding to each monitoring point. The monitoring results comprise monitoring time, time-series subsidence amount at the monitoring time, and position information of the monitoring point. Each pixel point in the second time-series subsidence data corresponds to a position information, and the pixel value of the pixel point is the time-series subsidence amount of the position information corresponding to the pixel point. A second unit 202 is configured to adjust the starting reference of the second time-series subsidence data according to the first time-series subsidence data, to obtain adjusted second time-series subsidence data. The starting time of the time period corresponding to the adjusted second time-series subsidence data is the latest monitoring time in the first time-series subsidence data. A third unit 203 is configured to extract, from the adjusted second time-series subsidence data, the supplementary time-series subsidence amount corresponding to each monitoring point in the first time-series subsidence data and the time period corresponding to the supplementary time-series subsidence amount, according to the first time-series subsidence data. A fourth unit 204 is configured to supplement and extend the monitoring results corresponding to each monitoring point in the first time-series subsidence data by using the extracted supplementary time-series subsidence amount corresponding to each monitoring point and the time period corresponding to the supplementary time-series subsidence amount, to obtain adjusted first time-series subsidence data.

[0056] Optionally, the second unit 202 is configured to: select target second time-series subsidence data from the plurality of second time-series subsidence data. The terminal time of the time period corresponding to the target second time-series subsidence data is the same as or later than the latest monitoring time in the first time-series subsidence data. determining reference second time-series subsidence data from the target second time-series subsidence data, wherein a terminal time of a time period corresponding to the reference second time-series subsidence data is the same as the latest monitoring time in the first time-series subsidence data; subtracting pixel values of the reference second time-series subsidence data from pixel values in the target second time-series subsidence data to obtain adjusted second time-series subsidence data.

[0057] Optionally, the apparatus further comprises a fifth unit configured to perform interpolation processing on the adjusted second time-series subsidence data to obtain latest adjusted second time-series subsidence data after the step of adjusting a starting reference of second time-series subsidence data according to the first time-series subsidence data to obtain adjusted second time-series subsidence data. The interpolation processing includes one of Kriging interpolation and inverse distance interpolation.

[0058] Optionally, the third unit 203 is configured to: For the i-th monitoring point in the adjusted second time-series subsidence data, determine target position information corresponding to the position information of the i-th monitoring point in the adjusted second time-series subsidence data according to the position information of the i-th monitoring point and position information and size information of pixel points in the i-th row and the i-th column in the adjusted second time-series subsidence data; extract a time-series subsidence amount corresponding to the target position information in the adjusted second time-series subsidence data, and take the extracted time-series subsidence amount corresponding to the target position information and a time period corresponding to the adjusted second time-series subsidence data as a supplementary time-series subsidence amount corresponding to the i-th monitoring point and a time period corresponding to the supplementary time-series subsidence amount; wherein i is a positive integer greater than or equal to 1.

[0059] Optionally, the third unit 203 is configured to: The target position information corresponding to the position information of the i-th monitoring point in the adjusted second time-series subsidence data is calculated by 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 position information corresponding to the position information of the i th monitoring point in the j th adjusted second time series subsidence data; row_i_j is the column number of the target position information corresponding to the position information of the i th monitoring point in the j th adjusted second time series subsidence data; int () represents the integer function; abs () represents the absolute value function; lon_i represents the longitude in the position information of the i th monitoring point; lat_i represents the latitude in the position information of the i th monitoring point; lon_tif_j represents the longitude in the position information of the pixel point at the 1st row and the 1st column in the j th adjusted second time series subsidence data; lat_tif_j represents the latitude in the position information of the pixel point at the 1st row and the 1st column in the j th adjusted second time series subsidence data; pixel_lon_j represents the length in the size information of the pixel point in the j th adjusted second time series subsidence data; pixel_lat_j represents the width in the size information of the pixel point in the j th adjusted second time series subsidence data.

[0060] Optionally, the fourth unit 204 is configured to: fill the extracted supplementary time series subsidence amount corresponding to each monitoring point into the monitoring result corresponding to each monitoring point in the first time series subsidence data according to the time period of the supplementary time series subsidence amount corresponding to each monitoring point, to obtain adjusted first time series subsidence data.

[0061] Optionally, the device further comprises a sixth unit configured to: smooth the adjusted first time series subsidence data to obtain smoothed first time series subsidence data.

[0062] In this way, the device can realize heterogeneous data integration, benchmark unification, spatial matching precision improvement, and acquisition of long time series high-quality subsidence monitoring data, and provide reliable data support for related applications.

[0063] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. At the hardware level, the electronic device comprises a processor, and optionally further comprises an internal bus, a network interface, and a memory. The memory can include a memory such as a random-access memory (RAM), and can also include a non-volatile memory such as at least one disk memory. Of course, the electronic device can also include other hardware required by a business.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] The above is as stated in this application. Figure 1 The 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.

[0068] The steps of the method disclosed in the embodiments of the present application can be directly embodied as hardware code processing executed by a processor, or a combination of hardware and software modules in the code processing. The software module can be located in a storage medium such as random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, or the like. The storage medium is located in the storage, and the processor reads information in the storage medium and combines hardware to complete the steps of the above method.

[0069] The embodiments of the present application further provide a readable storage medium, which stores execution instructions. When the execution instructions stored in the readable storage medium are executed by a processor of an electronic device, the electronic device can execute the fusion method based on heterogeneous time series settlement data provided in any of the embodiments of the present application, and is specifically used for executing the above evaluation method.

[0070] The electronic device described in each of the foregoing embodiments can be a computer.

[0071] Those skilled in the art should understand that the embodiments of the present application can be provided as a method or a computer program product. Therefore, the present application can be in the form of a completely hardware embodiment, a completely software embodiment, or a combination of software and hardware.

[0072] Each of the embodiments of the present application is described in a progressive manner, and the same or similar parts of each of the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the part of the method embodiments.

[0073] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles, or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or further include elements inherent in such processes, methods, articles, or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element.

[0074] The above only describes the embodiments of the present application and is not intended to limit the present application. Those skilled in the art can make various modifications and changes to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the scope of the claims of the present application.

Claims

1. A fusion method based on heterogeneous timing settlement data, characterized in that, The method comprises: obtaining first time sequence subsidence data and a plurality of second time sequence subsidence data; wherein the first time sequence subsidence data is in SHP data format, the second time sequence subsidence data is in label image file format, each second time sequence subsidence data corresponds to a different time period; the first time sequence subsidence data comprises a plurality of monitoring points and monitoring results corresponding to each monitoring point, wherein the monitoring results comprise monitoring time, time sequence subsidence at the monitoring time, and position information of the monitoring point; each pixel point in the second time sequence subsidence data corresponds to a position information, and the pixel value of the pixel point is the time sequence subsidence of the position information corresponding to the pixel point; adjusting a starting reference of the second time sequence subsidence data according to the first time sequence subsidence data to obtain adjusted second time sequence subsidence data; wherein the starting time of the time period corresponding to the adjusted second time sequence subsidence data is the latest monitoring time in the first time sequence subsidence data; extracting, from the adjusted second time sequence subsidence data, the supplementary time sequence subsidence corresponding to each monitoring point in the first time sequence subsidence data and the time period corresponding to the supplementary time sequence subsidence according to the first time sequence subsidence data; using the extracted supplementary time sequence subsidence corresponding to each monitoring point and the time period corresponding to the supplementary time sequence subsidence to supplement and extend the monitoring results corresponding to each monitoring point in the first time sequence subsidence data to obtain adjusted first time sequence subsidence data.

2. The method of claim 1, wherein, The method comprises: screening target second time sequence subsidence data from the plurality of second time sequence subsidence data; wherein the end time of the time period corresponding to the target second time sequence subsidence data is the same as or later than the latest monitoring time in the first time sequence subsidence data; determining reference second time sequence subsidence data from the target second time sequence subsidence data; wherein the end time of the time period corresponding to the reference second time sequence subsidence data is the same as the latest monitoring time in the first time sequence subsidence data; subtracting the pixel value of the reference second time sequence subsidence data from the pixel value in the target second time sequence subsidence data to obtain adjusted second time sequence subsidence data.

3. The method of claim 1, wherein, After the step of adjusting the starting reference of the second time sequence subsidence data according to the first time sequence subsidence data to obtain adjusted second time sequence subsidence data, the method further comprises: performing interpolation processing on the adjusted second time sequence subsidence data to obtain the latest adjusted second time sequence subsidence data; wherein the processing mode of the interpolation processing comprises one of the following modes: Kriging interpolation, inverse distance interpolation.

4. The method according to claim 1 or 3, characterized in that, The method comprises: screening target second time sequence subsidence data from the plurality of second time sequence subsidence data; wherein the end time of the time period corresponding to the target second time sequence subsidence data is the same as or later than the latest monitoring time in the first time sequence subsidence data; determining reference second time sequence subsidence data from the target second time sequence subsidence data; wherein the end time of the time period corresponding to the reference second time sequence subsidence data is the same as the latest monitoring time in the first time sequence subsidence data; subtracting the pixel value of the reference second time sequence subsidence data from the pixel value in the target second time sequence subsidence data to obtain adjusted second time sequence subsidence data. For the i-th monitoring point in the adjusted second time-series subsidence data, according to the position information of the i-th monitoring point, and the position information and size information of the pixel point in the i-th row and the i-th column of the adjusted second time-series subsidence data, the target position information corresponding to the position information of the i-th monitoring point in the adjusted second time-series subsidence data is determined; the time-series subsidence amount corresponding to the target position information is extracted, and the extracted time-series subsidence amount corresponding to the target position information and the time period corresponding to the adjusted second time-series subsidence data are taken as the supplementary time-series subsidence amount corresponding to the i-th monitoring point and the time period corresponding to the supplementary time-series subsidence amount; wherein i is a positive integer greater than or equal to 1.

5. The method of claim 4, wherein, The target position information corresponding to the position information of the i-th monitoring point in the adjusted second time-series subsidence data is determined according to the position information of the i-th monitoring point, and the position information and size information of the pixel point in the i-th row and the i-th column of the adjusted second time-series subsidence data, comprising: The target position information corresponding to the position information of the i-th monitoring point in the adjusted second time-series subsidence data is calculated by 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 in the target position information corresponding to the position information of the i-th monitoring point in the j-th adjusted second time-series subsidence data; row_i_j is the column number in the target position information corresponding to the position information of the i-th monitoring point in the j-th adjusted second time-series subsidence data; int() represents the integer function; abs() represents the absolute value function; lon_i represents the longitude in the position information of the i-th monitoring point; lat_i represents the latitude in the position information of the i-th monitoring point; lon_tif_j represents the longitude in the position information of the pixel point in the first row and the first column of the j-th adjusted second time-series subsidence data; lat_tif_j represents the latitude in the position information of the pixel point in the first row and the first column of the j-th adjusted second time-series subsidence data; pixel_lon_j represents the length in the size information of the pixel point in the j-th adjusted second time-series subsidence data; pixel_lat_j represents the width in the size information of the pixel point in the j-th adjusted second time-series subsidence data.

6. The method of claim 1, wherein, The monitoring results corresponding to each monitoring point in the first time-series subsidence data are supplemented and extended by using the extracted supplementary time-series subsidence amount corresponding to each monitoring point and the time period corresponding to the supplementary time-series subsidence amount, to obtain the adjusted first time-series subsidence data, comprising: The extracted supplementary time-series subsidence quantity corresponding to each monitoring point is filled into the monitoring result corresponding to each monitoring point in the first time-series subsidence data according to the time period of the supplementary time-series subsidence quantity corresponding to each monitoring point, to obtain adjusted first time-series subsidence data.

7. The method of claim 1, wherein, The method further comprises: The adjusted first time-series subsidence data is smoothed to obtain smoothed first time-series subsidence data.

8. A fusion device based on heterogeneous chronologically settled data, characterized by, The device comprises: A first unit is configured to acquire first time-series subsidence data and a plurality of second time-series subsidence data; the first time-series subsidence data is in SHP data format, and the second time-series subsidence data is in label image file format; each second time-series subsidence data corresponds to a different time period; the first time-series subsidence data comprises a plurality of monitoring points and monitoring results corresponding to each monitoring point; the monitoring result comprises monitoring time, time-series subsidence quantity at the monitoring time, and position information of the monitoring point; each pixel point in the second time-series subsidence data corresponds to a position information, and a pixel value of the pixel point is a time-series subsidence quantity of the position information corresponding to the pixel point; A second unit is configured to adjust a starting reference of the second time-series subsidence data according to the first time-series subsidence data, to obtain adjusted second time-series subsidence data; a starting time of a time period corresponding to the adjusted second time-series subsidence data is the latest monitoring time in the first time-series subsidence data; A third unit is configured to extract supplementary time-series subsidence quantity corresponding to each monitoring point and a time period corresponding to the supplementary time-series subsidence quantity from the adjusted second time-series subsidence data according to the first time-series subsidence data; A fourth unit is configured to supplement and extend the monitoring result corresponding to each monitoring point in the first time-series subsidence data by using the extracted supplementary time-series subsidence quantity corresponding to each monitoring point and the time period corresponding to the supplementary time-series subsidence quantity, to obtain adjusted first time-series subsidence data.

9. A readable medium characterized by The readable medium comprises execution instructions, and when a processor of an electronic device executes the execution instructions, the electronic device executes the method in any one of claims 1-7.

10. An electronic device, comprising: The electronic device comprises a processor and a memory storing execution instructions, and when the processor executes the execution instructions stored in the memory, the processor executes the method in any one of claims 1-7.

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