Tailing pond space-time abnormal deformation area identification method, device and equipment and storage medium

Abstract

The invention provides a tailings pond space-time abnormal deformation area identification method, device and equipment and a storage medium, and the method comprises the steps: obtaining the time sequence deformation monitoring data of each measurement point in a plurality of measurement points in a tailings pond monitoring area in a preset time period; obtaining a first deformation rate of each measurement point based on the time sequence deformation monitoring data, and obtaining a deformation rate difference value of each measurement point between at least two adjacent sub-time periods in the preset section; obtaining at least one first target measurement point set based on the first deformation rate and the spatial distance between the different measurement points; obtaining at least one second target measurement point set based on the deformation rate difference value and the spatial distance between different measurement points; and determining at least one rapid deformation area based on the at least one first target measurement point set, and determining at least one accelerated deformation area based on the at least one second target measurement point set. Through the technical scheme of the invention, accurate identification of the space-time abnormal deformation area of the tailing pond can be realized.

Description

Technical Field

[0001] This application relates to the field of geological monitoring technology, and in particular to a method, device, equipment and storage medium for identifying spatiotemporal anomaly deformation zones in tailings ponds. Background Technology

[0002] Tailings dams, as special sites for storing tailings or industrial waste, pose a serious threat to the local ecological environment and the safety of residents' lives and property should a dam collapse occur. Therefore, long-term dynamic monitoring of the tailings dam body and its surrounding surface is crucial. Monitoring spatiotemporal anomalies is a key aspect of tailings dam area monitoring.

[0003] In related technologies, deformation of tailings dam areas is mainly monitored through leveling instruments and GNSS measurements, which is costly and difficult to meet the needs of long-term monitoring. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] In a first aspect, this application proposes a method for identifying spatiotemporal anomaly deformation zones in tailings ponds. The method includes: acquiring temporal deformation monitoring data of each of multiple measurement points within a tailings pond monitoring area over a preset time period; acquiring a first deformation rate for each measurement point based on the temporal deformation monitoring data, and acquiring the deformation rate difference between at least two adjacent sub-time periods for each measurement point within the preset period; wherein the end time of the latter sub-time period among the at least two adjacent sub-time periods is the end time of the preset period; acquiring at least one first target measurement point set based on the first deformation rate and the spatial distance between different measurement points; acquiring at least one second target measurement point set based on the deformation rate difference and the spatial distance between different measurement points; determining at least one rapid deformation zone based on the at least one first target measurement point set, and determining at least one accelerated deformation zone based on the at least one second target measurement point set.

[0006] In one implementation, obtaining at least one first target measurement point set based on the first deformation rate and the spatial distance between different measurement points includes: obtaining points among the measurement points whose first deformation rate is greater than a first deformation rate threshold as first candidate measurement points; obtaining the standard deviation of the first deformation rate of the first candidate measurement points as a deformation rate standard value; obtaining a first difference between the deformation rate standard value and the first deformation rate threshold; and filtering and aggregating the first candidate measurement points based on the first difference and the spatial distance to obtain at least one first target measurement point set; wherein, the absolute value of the first deformation rate of the measurement points in each first target measurement point set is greater than the first difference, and the spatial distance between measurement points in the same first target measurement point set is less than or equal to a first spatial distance threshold.

[0007] In one implementation, obtaining at least one second target measurement point set based on the deformation rate difference and the spatial distance between different measurement points includes: obtaining points among the measurement points whose deformation rate difference is greater than a preset threshold as second candidate measurement points; filtering and aggregating the second candidate measurement points based on the spatial distance between the second candidate measurement points to obtain at least one second target measurement point set; wherein the spatial distance value between measurement points in the same second target measurement point set is less than or equal to a second spatial distance threshold.

[0008] In one implementation, determining at least one rapid deformation region based on the at least one first target measurement point set includes: determining at least one first candidate deformation region based on the at least one first target measurement point set, and determining at least one second candidate deformation region based on the at least one second target measurement point set; for each first candidate deformation region, obtaining a first measurement point count of all the measurement points in the first candidate deformation region; for each first candidate deformation region, obtaining a second measurement point count of the first target measurement point set within the first candidate deformation region; for each first candidate deformation region, in response to a ratio of the second measurement point count to the first measurement point count being greater than a preset ratio threshold, determining the first candidate deformation region as the rapid deformation region.

[0009] In one implementation, the time-series deformation monitoring data is time-series InSAR data.

[0010] Secondly, this application proposes a device for identifying spatiotemporal anomaly deformation zones in tailings ponds. The device includes: an acquisition module for acquiring temporal deformation monitoring data of each of multiple measurement points within a tailings pond monitoring area over a preset time period; a first processing module for acquiring a first deformation rate of each measurement point based on the temporal deformation monitoring data, and acquiring the deformation rate difference between at least two adjacent sub-time periods of each measurement point within the preset period; wherein the end time of the latter sub-time period among the at least two adjacent sub-time periods is the end time of the preset period; a second processing module for acquiring at least one first target measurement point set based on the first deformation rate and the spatial distance between different measurement points; a third processing module for acquiring at least one second target measurement point set based on the deformation rate difference and the spatial distance between different measurement points; and a fourth processing module for determining at least one rapid deformation zone based on the at least one first target measurement point set, and determining at least one accelerated deformation zone based on the at least one second target measurement point set.

[0011] In one implementation, the second processing module can be used to: obtain points among the measurement points whose first deformation rate is greater than a first deformation rate threshold as first candidate measurement points; obtain the standard deviation of the first deformation rate of the first candidate measurement points as a deformation rate standard value; obtain a first difference between the deformation rate standard value and the first deformation rate threshold; filter and aggregate the first candidate measurement points based on the first difference and the spatial distance to obtain at least one set of first target measurement points; wherein, the absolute value of the first deformation rate of the measurement points in each set of first target measurement points is greater than the first difference, and the spatial distance between the measurement points in the same set of first target measurement points is less than or equal to a first spatial distance threshold.

[0012] In one implementation, the third processing module can be used to: obtain points among the measurement points whose deformation rate difference is greater than a preset threshold as second candidate measurement points; filter and aggregate the second candidate measurement points based on the spatial distance between the second candidate measurement points to obtain at least one set of second target measurement points; wherein the spatial distance value between measurement points in the same set of second target measurement points is less than or equal to a second spatial distance threshold.

[0013] In one implementation, the fourth processing module can be used to: determine at least one first candidate deformation region based on the at least one first target measurement point set, and determine at least one second candidate deformation region based on the at least one second target measurement point set; for each first candidate deformation region, obtain a first measurement point count of all the measurement points in the first candidate deformation region; for each first candidate deformation region, obtain a second measurement point count of the first target measurement point set within the first candidate deformation region; for each first candidate deformation region, in response to the ratio of the second measurement point count to the first measurement point count being greater than a preset ratio threshold, determine the first candidate deformation region as the rapid deformation region.

[0014] In one implementation, the time-series deformation monitoring data is time-series InSAR data.

[0015] Thirdly, this application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the tailings dam spatiotemporal anomaly deformation zone identification method as described in the first aspect.

[0016] Fourthly, this application proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect.

[0017] Fifthly, this application proposes a program product comprising at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method described in the first aspect.

[0018] The method, apparatus, equipment, and storage medium for identifying spatiotemporal anomaly deformation zones in tailings ponds provided in this application can determine the first deformation rate of each measurement point and the deformation rate difference between at least two adjacent sub-time periods of each measurement point in a preset segment based on the temporal deformation monitoring data of the monitoring area. The obtained first deformation rate, deformation rate difference, and spatial distance between different measurement points are used to acquire at least one first target measurement point set and at least one second target measurement point set, and the deformation zone is determined based on the obtained measurement point sets. This enables accurate identification of spatiotemporal anomaly deformation zones in tailings ponds.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for identifying spatiotemporal anomaly deformation zones in tailings ponds, as provided in an embodiment of this application. Figure 2 This is a flowchart illustrating another method for identifying spatiotemporal anomaly deformation zones in tailings ponds provided in an embodiment of this application. Figure 3 This is a flowchart illustrating another method for identifying spatiotemporal anomaly deformation zones in tailings ponds provided in this application embodiment; Figure 4 This is a schematic diagram of a deformation rate monitoring result provided in an embodiment of this application; Figure 5 This is a schematic diagram of another deformation rate monitoring result provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of a tailings dam spatiotemporal anomaly deformation zone identification device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following describes a method and apparatus for identifying spatiotemporal anomaly deformation zones in tailings ponds according to embodiments of this application, with reference to the accompanying drawings.

[0023] Figure 1 This is a flowchart illustrating a method for identifying spatiotemporal anomaly deformation zones in tailings ponds, as provided in an embodiment of this application. Figure 1 As shown, the method may include, but is not limited to, the following steps: S101: Acquire the time-series deformation monitoring data of each of the multiple measurement points in the tailings dam monitoring area within a preset time period.

[0024] In some embodiments, the aforementioned time-series deformation data may be time-series InSAR (Interferometric Synthetic Aperture Radar) data.

[0025] For example, time-series InSAR data of the tailings dam body and surrounding area within a preset time period is acquired, and the time-series InSAR data is processed to locate measurement points in the area. Phase information of the measurement points at multiple times within the preset time period is extracted and inverted to obtain deformation data of each measurement point at multiple times within the preset time period.

[0026] S102: Based on the time-series deformation monitoring data, obtain the first deformation rate of each measurement point, and obtain the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset segment.

[0027] In the embodiments of this application, the end time of the latter sub-time period among the above-mentioned at least two adjacent sub-time periods is the end time of the preset time period.

[0028] As an example, the deformation rate can be calculated by modeling historical deformation data of measurement points as a linear motion model. The functional relationship can be expressed as:

[0029] In the above formula, Compared to the deformation value at the initial moment, This is the initial deformation value. For deformation rate, It is expressed as the time of the observation relative to the initial observation time. This represents the error between the modeled deformation and the measured deformation value. Based on N periods of deformation data within the monitoring period, a deformation model of the measurement point at each time point can be established, and its matrix expression is as follows:

[0030] Among them, matrix , , , It can be represented as follows:

[0031] Therefore, the deformation rate values ​​at each measurement point can be obtained based on the least squares theory. .

[0032] For example, a preset time period can be divided into at least two sub-time periods, and the deformation rate of each sub-time period can be obtained, thereby obtaining the deformation rate difference between the last sub-time period and the adjacent sub-time periods.

[0033] As an example, taking the division of a preset time period into four sub-time periods as an example, it can be modeled into four piecewise linear motion models based on deformation monitoring data, and its functional relationship can be expressed as:

[0034] In the above formula, , , and These represent the deformation rate values ​​for four consecutive time periods. Indicates the first The time interval is relative to the initial time interval, and ε represents the error between the modeled deformation and the deformation measurement. , and This represents the inflection point between the preceding and following time periods. It should be noted that, to ensure the continuity of deformation, at the inflection point, the deformation rate of each segment must satisfy the following relationship:

[0035]

[0036]

[0037] Based on the deformation data from N periods within the monitoring cycle, a deformation model of the measurement point at each moment can be established, and its matrix expression is as follows:

[0038] Among them, matrix , , , It can be represented as follows:

[0039]

[0040]

[0041]

[0042] The deformation rate of the measurement point in the four time periods can then be obtained using the least squares method. , , and .

[0043] S103: Based on the first deformation rate and the spatial distance between different measurement points, obtain at least one set of first target measurement points.

[0044] For example, measurement points with a first phase change rate greater than a preset deformation rate threshold and a spatial distance less than or equal to a preset spatial distance threshold are aggregated into a measurement point set, thereby obtaining at least one first target measurement point set.

[0045] S104: Based on the deformation rate difference and the spatial distance between different measurement points, obtain at least one set of second target measurement points.

[0046] For example, measurement points whose deformation rate difference is greater than a preset deformation rate difference threshold and whose spatial distance is less than or equal to a preset spatial distance threshold are aggregated into a measurement point set, thereby obtaining at least one second target measurement point set.

[0047] In some embodiments, obtaining at least one second target measurement point set based on the deformation rate difference and the spatial distance between different measurement points includes: obtaining points among the measurement points whose deformation rate difference is greater than a preset threshold as second candidate measurement points; filtering and aggregating the second candidate measurement points based on the spatial distance between the second candidate measurement points to obtain at least one second target measurement point set; wherein the spatial distance value between measurement points in the same second target measurement point set is less than or equal to a second spatial distance threshold.

[0048] For example, measurement points that are spatially adjacent and whose deformation rate changes exceed a preset deformation rate change threshold are aggregated into a set of measurement points, thereby obtaining at least one second target measurement point set.

[0049] S105: Determine at least one rapid deformation zone based on at least one first target measurement point set, and determine at least one accelerated deformation zone based on at least one second target measurement point set.

[0050] For example, for each first set of measurement points, the circumscribed polygon of the measurement points in the first set of measurement points is labeled as the fast deformation zone.

[0051] For example, for each second set of measurement points, the circumscribed polygon of the measurement points in the second set of measurement points is labeled as the accelerated deformation zone.

[0052] By implementing the embodiments of this application, the first deformation rate of each measurement point and the deformation rate difference between at least two adjacent sub-time periods of each measurement point can be determined based on the temporal deformation monitoring data of the monitoring area. The obtained first deformation rate, deformation rate difference, and spatial distance between different measurement points enable the acquisition of at least one first target measurement point set and at least one second target measurement point set, which are then used to determine the deformation zone. This enables accurate identification of spatiotemporal abnormal deformation zones in tailings ponds.

[0053] As an example, please see Figure 2 , Figure 2 This is a flowchart illustrating another method for identifying spatiotemporal anomaly deformation zones in tailings ponds provided in this application. Figure 2 As shown, the method may include, but is not limited to, the following steps: S201: Obtain time-series deformation monitoring data for each of multiple measurement points within the tailings dam monitoring area over a preset time period.

[0054] In the embodiments of this application, step S201 can be implemented in any of the ways described in the embodiments of this application. The embodiments of this application do not limit this, nor will they be described in detail.

[0055] S202: Based on the time-series deformation monitoring data, obtain the first deformation rate of each measurement point, and obtain the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset segment.

[0056] In the embodiments of this application, step S202 can be implemented in any of the ways described in the embodiments of this application. The embodiments of this application do not limit this, nor will they be described in detail.

[0057] S203: Select the first candidate measurement point whose first deformation rate is greater than the deformation rate threshold.

[0058] For example, based on the deformation rate threshold determined according to different deformation levels, the measurement points with the corresponding first phase change rate within the threshold are regarded as relatively stable measurement points, and the measurement points with the corresponding first phase change rate greater than the deformation rate threshold are regarded as first candidate measurement points.

[0059] S204: Obtain the standard deviation of the first deformation rate of the first candidate measurement point as the standard value of deformation rate.

[0060] For example, the standard deviation of the deformation rate corresponding to the first candidate measurement point is calculated, and the calculation process can be expressed as follows:

[0061] in, The standard deviation of the deformation rate, For the i-th candidate measurement point, The mean of the first deformation rate corresponding to all first candidate measurement points.

[0062] S205: Obtain the first difference between the standard value of deformation rate and the deformation rate threshold.

[0063] S206: Based on the first difference and spatial distance, filter and aggregate the first candidate measurement points to obtain at least one set of first target measurement points.

[0064] In each first target measurement point set, the absolute value of the first deformation rate of the measurement point is greater than the first difference, and the spatial distance between the measurement points in the same first target measurement point set is less than or equal to the first spatial distance threshold.

[0065] For example, the spatial distance between the candidate point and its neighboring candidate points is less than or equal to a preset distance threshold, and the absolute value of the candidate point's deformation rate is... Exceeding the rate threshold with standard deviation The difference between the measurement points is taken as a target measurement point, thereby aggregating adjacent target measurement points into a set to obtain at least one first target measurement point set.

[0066] S207: Based on the difference in deformation rate and the spatial distance between different measurement points, obtain at least one set of second target measurement points.

[0067] In the embodiments of this application, step S207 can be implemented in any of the embodiments of this application. The embodiments of this application do not limit this and will not be described in detail.

[0068] S208: Determine at least one rapid deformation zone based on at least one first target measurement point set, and determine at least one accelerated deformation zone based on at least one second target measurement point set.

[0069] In the embodiments of this application, step S208 can be implemented in any of the embodiments of this application. The embodiments of this application do not limit this and will not be described in detail.

[0070] By implementing the embodiments of this application, measurement points within the target measurement point set can be screened based on the average deformation rate corresponding to each measurement point, thereby improving the effectiveness and accuracy of deformation monitoring.

[0071] As an example, please participate Figure 3 , Figure 3 This is a flowchart illustrating another method for identifying spatiotemporal anomaly deformation zones in tailings ponds provided in this application. Figure 3 As shown, the method may include, but is not limited to, the following steps: S301: Acquire the time-series deformation monitoring data of each of the multiple measurement points in the tailings dam monitoring area within a preset time period.

[0072] In the embodiments of this application, step S301 can be implemented in any of the ways described in the embodiments of this application. The embodiments of this application do not limit this, nor will they be described in detail.

[0073] S302: Based on the time-series deformation monitoring data, obtain the first deformation rate of each measurement point, and obtain the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset segment.

[0074] In the embodiments of this application, step S302 can be implemented in any of the ways described in the embodiments of this application. The embodiments of this application do not limit this, nor will they be described in detail.

[0075] S303: Based on the first deformation rate and the spatial distance between different measurement points, obtain at least one set of first target measurement points.

[0076] In the embodiments of this application, step S303 can be implemented in any of the embodiments of this application. The embodiments of this application do not limit this and will not be described in detail.

[0077] S304: Based on the difference in deformation rate and the spatial distance between different measurement points, obtain at least one set of second target measurement points.

[0078] In the embodiments of this application, step S304 can be implemented in any of the embodiments of this application. The embodiments of this application do not limit this and will not be described in detail.

[0079] S305: Determine at least one first candidate deformation zone based on at least one first target measurement point set, and determine at least one second candidate deformation zone based on at least one second target measurement point set.

[0080] In the embodiments of this application, step S305 can be implemented in any of the embodiments of this application. The embodiments of this application do not limit this and will not be described in detail.

[0081] S306: For each first candidate deformation zone, obtain the first measurement point count of all measurement points in the first candidate deformation zone.

[0082] For example, for each first candidate deformation region, the number of all measurement points within the first candidate deformation region is obtained as the first measurement point number.

[0083] S307: For each first candidate deformation zone, obtain the number of second measurement points in the first target measurement point set within the first candidate deformation zone.

[0084] For example, for each first candidate deformation region, the number of all first target measurement points within that first candidate deformation region is obtained as the number of second measurement points.

[0085] S308: For each first candidate deformation zone, in response to the ratio of the number of second measurement points to the number of first measurement points being greater than a preset ratio threshold, the first candidate deformation zone is determined as a rapid deformation zone.

[0086] It should be noted that the proportional thresholds corresponding to different first candidate deformation regions can be different. For example, the corresponding proportional threshold can be set by the area of ​​the first candidate deformation region or the total number of measurement points within the first candidate deformation region.

[0087] S309: Determine at least one accelerated deformation zone based on at least one set of second target measurement points.

[0088] In the embodiments of this application, the specific implementation method for determining at least one accelerated deformation zone based on at least one second target measurement point set can be the same as the specific implementation method for determining at least one rapid deformation zone based on at least one first target measurement point set, and will not be described again here.

[0089] By implementing the embodiments of this application, a target deformation region can be selected from candidate deformation regions based on the proportion of the target measurement point within all measurement points in the candidate deformation region. This improves the accuracy of deformation region detection.

[0090] The following will further describe the identification of spatiotemporal anomaly deformation zones in tailings ponds provided in this application embodiment, with reference to specific embodiments.

[0091] Table 1 is an example table of basic information of SAR images provided in an embodiment of this application.

[0092] Table 1 Basic Information of SAR Imagery

[0093] Please see Figure 4 and Figure 5 , Figure 4 This is a schematic diagram of a deformation rate monitoring result provided in an embodiment of this application. Figure 5 This is a schematic diagram illustrating another deformation rate monitoring result provided in an embodiment of this application. First, 98 Sentinel-1A SAR data frames were selected for the monitoring area from January 2, 2021 to November 6, 2024. Based on time-series InSAR technology, the annual average deformation rate of the tailings dam from January 2, 2021 to November 6, 2024 was extracted, as shown below. Figure 4As shown. Then, based on the set time and rate threshold parameters, the deformation data of the measurement points from January 2, 2021 to November 6, 2024 were divided into four time periods. The first time period spans from January 2, 2021 to April 15, 2022, with a total of 35 images; the second time period spans from April 15, 2022 to April 22, 2023, with a total of 29 images; the third time period spans from April 22, 2023 to April 28, 2024, with a total of 26 images; and the fourth time period spans from April 28, 2024 to November 6, 2024, with a total of 11 images. The division principle is based on the last monitoring time, dividing the monitoring results sequentially backwards until the beginning of the monitoring period. This experiment designated the last six months of the monitoring period as a separate monitoring period, while the remaining periods were grouped into groups with a one-year monitoring cycle. If the monitoring cycle of the last group was less than six months, the data from that group was combined with data from the adjacent monitoring cycle. Finally, based on the least squares principle, the deformation rate of each measurement point in different time periods was calculated. Figure 5 The deformation rate results are for the last two time periods during the monitoring period.

[0094] A rate threshold is set to identify rapid deformation measurement points, and spatially neighboring points are aggregated into a candidate point set. The circumscribed polygon of this set is then labeled as a rapid deformation candidate region. The rate difference between the third and fourth time periods is calculated, and based on the set rate change threshold, recently accelerated deformation measurement points are identified. Subsequently, spatially neighboring points are aggregated into a candidate set, and the circumscribed polygon of this set is labeled as a recently accelerated deformation candidate region.

[0095] Finally, the percentage of measurement points with rates exceeding the threshold in the rapid deformation candidate area is counted, and areas with a percentage exceeding 20% ​​are identified as the final rapid deformation areas. Similarly, the percentage of measurement points with rate changes exceeding the threshold in the recent accelerated deformation candidate area is counted, and areas with a percentage exceeding 20% ​​are identified as the final accelerated deformation areas. The union of the rapid deformation areas and the recent accelerated deformation areas within the monitoring period is determined as the spatiotemporal abnormal deformation areas of the tailings dam.

[0096] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a tailings dam spatiotemporal anomaly deformation zone identification device provided in an embodiment of this application. Figure 6As shown, the device 600 includes: an acquisition module 601, used to acquire time-series deformation monitoring data of each of multiple measurement points in the tailings dam monitoring area within a preset time period; a first processing module 602, used to acquire a first deformation rate of each measurement point based on the time-series deformation monitoring data, and acquire the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset period; wherein the end time of the latter sub-time period in the at least two adjacent sub-time periods is the end time of the preset period; a second processing module 603, used to acquire at least one first target measurement point set based on the first deformation rate and the spatial distance between different measurement points; a third processing module 604, used to acquire at least one second target measurement point set based on the deformation rate difference and the spatial distance between different measurement points; and a fourth processing module 605, used to determine at least one rapid deformation zone based on at least one first target measurement point set, and to determine at least one accelerated deformation zone based on at least one second target measurement point set.

[0097] In one implementation, the second processing module 603 can be used to: obtain points among the measurement points whose first deformation rate is greater than a first deformation rate threshold as first candidate measurement points; obtain the standard deviation of the first deformation rate of the first candidate measurement points as a deformation rate standard value; obtain the first difference between the deformation rate standard value and the first deformation rate threshold; filter and aggregate the first candidate measurement points based on the first difference and spatial distance to obtain at least one first target measurement point set; wherein, the absolute value of the first deformation rate of the measurement points in each first target measurement point set is greater than the first difference, and the spatial distance between the measurement points in the same first target measurement point set is less than or equal to the first spatial distance threshold.

[0098] In one implementation, the third processing module 604 can be used to: obtain points among the measurement points whose deformation rate difference is greater than a preset threshold as second candidate measurement points; filter and aggregate the second candidate measurement points based on the spatial distance between the second candidate measurement points to obtain at least one set of second target measurement points; wherein the spatial distance value between measurement points in the same set of second target measurement points is less than or equal to the second spatial distance threshold.

[0099] In one implementation, the fourth processing module 605 can be used to: determine at least one first candidate deformation region based on at least one first target measurement point set, and determine at least one second candidate deformation region based on at least one second target measurement point set; for each first candidate deformation region, obtain the first measurement point count of all measurement points in the first candidate deformation region; for each first candidate deformation region, obtain the second measurement point count of the first target measurement point set within the first candidate deformation region; for each first candidate deformation region, in response to the ratio of the second measurement point count to the first measurement point count being greater than a preset ratio threshold, determine the first candidate deformation region as a rapid deformation region.

[0100] In one implementation, the time-series deformation monitoring data is time-series InSAR data.

[0101] The apparatus of this application embodiment can determine the first deformation rate of each measurement point and the deformation rate difference between at least two adjacent sub-time periods of each measurement point based on the temporal deformation monitoring data of the monitoring area. The obtained first deformation rate, deformation rate difference, and spatial distance between different measurement points enable the acquisition of at least one first target measurement point set and at least one second target measurement point set, which are then used to determine the deformation zone. This enables accurate identification of spatiotemporal abnormal deformation zones in tailings ponds.

[0102] It should be noted that the foregoing explanation of the embodiment of the method for identifying spatiotemporal anomaly deformation zones in tailings ponds also applies to the tailings pond spatiotemporal anomaly deformation zone identification device of this embodiment, and will not be repeated here.

[0103] To implement the above embodiments, this application also proposes an electronic device. Please see [link to relevant documentation]. Figure 7 , Figure 7 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 7 As shown, the electronic device 700 includes: a processor 701 and a memory 702 communicatively connected to the processor 701; the memory 702 stores computer execution instructions; the processor 701 executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0104] To implement the above embodiments, this application also proposes a storage medium storing instructions that, when executed on an electronic device, cause the electronic device to perform the methods provided in the foregoing embodiments.

[0105] To implement the above embodiments, this application also proposes a program product, including at least one of a program and instructions, wherein when the program and instructions are executed by an electronic device, they implement the steps of the method provided in the foregoing embodiments.

[0106] It should be noted that the acquisition, transmission, storage, use, and processing of data in this application comply with the relevant provisions of national laws and regulations and do not violate public order and good morals.

[0107] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0108] It is worth noting that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0109] In the description of this application, unless otherwise stated, " / " means "or", for example, A / B can mean A or B; "and / or" in this document is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.

[0110] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0111] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0112] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0113] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0114] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0115] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0117] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying spatiotemporal anomaly deformation zones in tailings ponds, characterized in that, include: Acquire temporal deformation monitoring data of each of the multiple measurement points within the tailings dam monitoring area during a preset time period; Based on the time-series deformation monitoring data, the first deformation rate of each measurement point is obtained, and the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset segment is obtained; wherein, the end time of the latter sub-time period in the at least two adjacent sub-time periods is the end time of the preset segment. Based on the first deformation rate and the spatial distance between different measurement points, at least one set of first target measurement points is obtained; Based on the deformation rate difference and the spatial distance between different measurement points, at least one set of second target measurement points is obtained; At least one rapid deformation zone is determined based on the at least one first target measurement point set, and at least one accelerated deformation zone is determined based on the at least one second target measurement point set.

2. The method according to claim 1, characterized in that, The step of obtaining at least one set of first target measurement points based on the first deformation rate and the spatial distance between different measurement points includes: The points in the measurement points whose first deformation rate is greater than the first deformation rate threshold are selected as the first candidate measurement points; The standard deviation of the first deformation rate at the first candidate measurement point is obtained as the standard value of the deformation rate. Obtain the first difference between the standard value of the deformation rate and the first deformation rate threshold; Based on the first difference and the spatial distance, the first candidate measurement points are filtered and aggregated to obtain at least one set of the first target measurement points; wherein, the absolute value of the first deformation rate of the measurement points in each set of the first target measurement points is greater than the first difference, and the spatial distance between the measurement points in the same set of the first target measurement points is less than or equal to a first spatial distance threshold.

3. The method according to claim 1, characterized in that, The step of obtaining at least one set of second target measurement points based on the deformation rate difference and the spatial distance between different measurement points includes: The points in the measurement points whose deformation rate difference is greater than a preset threshold are selected as the second candidate measurement points; The second candidate measurement points are filtered and aggregated based on the spatial distance between the second candidate measurement points to obtain at least one set of the second target measurement points; wherein the spatial distance value between measurement points in the same set of the second target measurement points is less than or equal to a second spatial distance threshold.

4. The method according to claim 1, characterized in that, Determining at least one rapid deformation zone based on the at least one set of first target measurement points includes: At least one first candidate deformation region is determined based on the at least one first target measurement point set, and at least one second candidate deformation region is determined based on the at least one second target measurement point set. For each first candidate deformation region, obtain the first number of measurement points for all the measurement points in the first candidate deformation region; For each first candidate deformation region, obtain the second number of measurement points in the first target measurement point set within the first candidate deformation region; For each of the first candidate deformation regions, in response to the ratio of the number of the second measurement points to the number of the first measurement points being greater than a preset ratio threshold, the first candidate deformation region is determined as the rapid deformation region.

5. The method according to claim 1, characterized in that, The time-series deformation monitoring data is time-series InSAR data.

6. A device for identifying spatiotemporal anomaly deformation zones in tailings ponds, characterized in that, include: The acquisition module is used to acquire the time-series deformation monitoring data of each of the multiple measurement points in the tailings dam monitoring area within a preset time period; The first processing module is used to obtain the first deformation rate of each measurement point based on the time-series deformation monitoring data, and to obtain the deformation rate difference between at least two adjacent sub-time periods of each measurement point in the preset segment; wherein, the end time of the latter sub-time period in the at least two adjacent sub-time periods is the end time of the preset segment. The second processing module is used to obtain at least one set of first target measurement points based on the first deformation rate and the spatial distance between different measurement points; The third processing module is used to obtain at least one set of second target measurement points based on the deformation rate difference and the spatial distance between different measurement points; The fourth processing module is used to determine at least one rapid deformation zone based on the at least one first target measurement point set, and to determine at least one accelerated deformation zone based on the at least one second target measurement point set.

7. The apparatus according to claim 6, characterized in that, The second processing module is specifically used for: The points in the measurement points whose first deformation rate is greater than the first deformation rate threshold are selected as the first candidate measurement points; The standard deviation of the first deformation rate at the first candidate measurement point is obtained as the standard value of the deformation rate. Obtain the first difference between the standard value of the deformation rate and the first deformation rate threshold; Based on the first difference and the spatial distance, the first candidate measurement points are filtered and aggregated to obtain at least one set of the first target measurement points; wherein, the absolute value of the first deformation rate of the measurement points in each set of the first target measurement points is greater than the first difference, and the spatial distance between the measurement points in the same set of the first target measurement points is less than or equal to a first spatial distance threshold.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 5.

9. A storage medium storing instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method of any one of claims 1 to 5.

10. A program product comprising at least one of a program and instructions, characterized in that, When at least one of the program or instructions is executed by an electronic device, it implements the steps of the method according to any one of claims 1 to 5.

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