Water conservancy infrastructure risk early warning method, system, device and storage medium

Abstract

The application discloses a water conservancy infrastructure risk early warning method, system, device and storage medium. The water conservancy infrastructure risk early warning method comprises the following steps: acquiring optical remote sensing data, single-view complex product data, ground distance product data, pixel distance and a labeled value of water conservancy infrastructure of a target region; determining a first time sequence of deformation sequence of the target region based on the single-view complex product data and the pixel distance; determining a first soil moisture content sequence of the target region based on the optical remote sensing data and the ground distance product data; calculating a correlation coefficient of the first time sequence of deformation sequence and the first soil moisture content sequence; inputting the labeled value of the water conservancy infrastructure, the first time sequence of deformation sequence, the first soil moisture content sequence and the correlation coefficient into a disaster monitoring model to obtain a disaster occurrence probability value output by the disaster monitoring model, and performing early warning based on the disaster occurrence probability value. The early warning accuracy of disasters of water conservancy infrastructure in rivers and lakes in the target region is improved.

Description

Technical Field

[0001] This application relates to the technical field of risk early warning for water conservancy infrastructure, and in particular to methods, systems, equipment and storage media for risk early warning of water conservancy infrastructure. Background Technology

[0002] Water conservancy infrastructure (such as dikes, levees, and embankments) is the core facility of flood control and tide prevention engineering systems, and its structural safety is directly related to the safety of coastal cities, farmland, and people's lives and property. With the increase of service time and the long-term effects of external environment (such as rainfall, water flow erosion, and groundwater changes), water conservancy infrastructure is prone to hidden dangers such as leakage, piping, settlement, and landslides. If these are not identified in time, they may cause major disasters such as dike breaches. Therefore, accurate and efficient detection of risks to water conservancy infrastructure is crucial.

[0003] Currently, risk detection methods for water conservancy infrastructure mostly rely on manual inspections, point-type sensor monitoring (such as piezometers and displacement gauges), or empirical assessments based on the inherent properties of dikes (such as soil type, construction grade, and service life). This results in a lack of comprehensiveness in risk assessment and makes it difficult to meet the needs of water conservancy infrastructure safety management in complex environments. Summary of the Invention

[0004] This application aims to at least address the technical problems existing in the prior art. To this end, this application proposes a method, system, equipment, and storage medium for early warning of risks to water conservancy infrastructure, which can integrate first time-series rainfall sequences, second time-series rainfall sequences, optical remote sensing data, single-view complex product data, and ground distance product data to improve the accuracy of early warning of risks to water conservancy infrastructure.

[0005] The first aspect of this application provides a method for early warning of risks in water conservancy infrastructure, comprising the following steps: Acquire optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure for the target area. The single-view complex product data and the ground distance product data are both data of the satellite revisit period to which the predicted time belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. A first temporal deformation sequence of the target region is determined based on the single-view complex product data and pixel distance; Based on the optical remote sensing data and the ground distance product data, a first soil moisture content sequence for the target area is determined; Calculate the correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence; The labeled values ​​of the water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and an early warning is issued based on the disaster occurrence probability value.

[0006] The water conservancy infrastructure risk early warning method according to the embodiments of this application has at least the following beneficial effects: This method acquires optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and ground distance product data are obtained from satellite revisit cycles of the predicted time obtained via double-orbit time-series synthetic aperture radar (SAPRA). The pixel distance is the pixel distance of the synthetic aperture radar data obtained via SAPRA. Based on the single-view complex product data and pixel distance, the first temporal deformation sequence of the target area is determined. Based on the optical remote sensing data and ground distance product data, the first soil moisture content sequence of the target area is determined. The correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence is calculated. The labeled values ​​of water conservancy infrastructure, the first temporal deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into a disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model. Early warning is then issued based on the disaster occurrence probability value. This application integrates optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure to construct a disaster monitoring model. This overcomes the data silos and institutional deficiencies of traditional technologies and improves the accuracy of early warning for disasters occurring in water conservancy infrastructure in rivers and lakes within the target area.

[0007] According to some embodiments of this application, determining the first temporal deformation sequence of the target region based on the single-view complex product data and pixel distance includes: The second temporal deformation sequence of the single-view complex product data is calculated using the small baseline set interferometric synthetic aperture radar method, wherein the temporal deformation sequence includes several temporal deformation values ​​and the latitude and longitude corresponding to each temporal deformation value; The third temporal deformation sequence of the single-view complex product data was calculated using the permanent scatterer synthetic aperture radar interferometry method. Calculate the distance between the first type of point and the second type of point in the preset coordinate system, and use it as the distance between the first points. The first type of point is any point in the second time-series deformation sequence, and the second type of point is any point in the third time-series deformation sequence. The points in the time-series deformation sequence are obtained by projecting the latitude and longitude corresponding to each time-series deformation value onto the preset coordinate system. The first temporal deformation sequence is determined based on the second temporal deformation sequence, the third temporal deformation sequence, the first point distance, and the pixel distance.

[0008] According to some embodiments of this application, determining the first temporal deformation sequence based on the second temporal deformation sequence, the third temporal deformation sequence, the first point distance, and the pixel distance includes: Traverse the points in the second temporal deformation sequence, and filter out all points in the second temporal deformation sequence whose distance from any point in the third temporal deformation sequence is less than a first preset distance threshold, as the corresponding points in the second temporal deformation sequence, wherein the first preset distance threshold is one-tenth of the pixel distance; Determine a fourth temporal deformation sequence, wherein the fourth temporal deformation sequence is a sequence consisting of all points in the second temporal deformation sequence except for the points with the same name in the second temporal deformation sequence; The first trend term of the third time-series deformation sequence is calculated using a time-series decomposition algorithm; the second trend term of the fourth time-series deformation sequence is calculated using a time-series decomposition algorithm, wherein the first trend term belongs to the third time-series deformation sequence and the second trend term belongs to the fourth time-series deformation sequence; Calculate the distance between the second type of point and the third type of point in the preset coordinate system, and use it as the distance between the second points, wherein the third type of point is any point in the fourth temporal deformation sequence; Traverse the points in the third temporal deformation sequence and the fourth temporal deformation sequence, and filter out point pairs whose distance to the second point is less than a second preset distance threshold. The point pair consists of the points in the third temporal deformation sequence and the points in the fourth temporal deformation sequence whose distance to the second point is less than the second preset distance threshold. Determine the third trend term corresponding to all points of the third temporal deformation sequence in the point pair; determine the fourth trend term corresponding to all points of the fourth temporal deformation sequence in the point pair, wherein the third trend term belongs to the first trend term and the fourth trend term belongs to the second trend term; The third trend term is replaced with the fourth trend term in the fourth time series deformation sequence to obtain the fifth time series deformation sequence; The first time-series deformation sequence is obtained by combining the third time-series deformation sequence and the fifth time-series deformation sequence.

[0009] According to some embodiments of this application, determining the first soil moisture content sequence of the target area based on the optical remote sensing data and the ground distance product data includes: Extract the homopolarization and crosspolarization data of the ground distance product data; The same-polarization data is filtered to obtain filtered same-polarization data; the cross-polarization data is filtered to obtain filtered cross-polarization data. Geocoding is performed on the filtered homopolarized data to obtain encoded homopolarized data; geocoding is performed on the filtered cross-polarized data to obtain encoded cross-polarized data. Radiometric calibration is performed on the encoded homopolarized data to obtain homopolarized backscattering coefficients; radiometric calibration is performed on the encoded crosspolarized data to obtain crosspolarized backscattering coefficients. The joint backscattering coefficient is determined based on the same polarization backscattering coefficient, the cross-polarization backscattering coefficient, and the preset backscattering coefficient parameter value; Calculate the normalized vegetation index and normalized water index of the optical remote sensing data; The joint backscattering coefficient, the normalized vegetation index, and the normalized water index are input into the soil moisture content prediction model to obtain the first soil moisture content sequence output by the soil moisture content measurement model.

[0010] According to some embodiments of this application, the preset backscattering coefficient parameter value includes a first parameter value and a second parameter value, and the determination of the joint backscattering coefficient based on the co-polarized backscattering coefficient, the cross-polarized backscattering coefficient, and the preset backscattering coefficient parameter value includes: Multiplying the same polarization backscattering coefficient by the first parameter value yields the first coefficient; Multiplying the cross-polarization backscattering coefficient by the second parameter value yields the second coefficient; The first coefficient and the second coefficient are added together to obtain the joint backscattering coefficient.

[0011] According to some embodiments of this application, calculating the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence includes: Extract the first temporal deformation sequence within a preset time window as the second temporal deformation sequence; extract the first soil moisture content sequence within a preset time window as the second soil moisture content sequence; Calculate the temporal deformation mean of the second temporal deformation sequence; calculate the soil moisture mean of the second soil moisture content sequence; The correlation coefficient is determined based on the second time-series deformation sequence, the second soil moisture content sequence, the mean time-series deformation, and the mean soil moisture content.

[0012] According to some embodiments of this application, before obtaining the labeled values ​​of the water conservancy infrastructure in the target area, the method further includes: Having obtained the soil type, process type, geological condition type, construction grade, and service life of the water conservancy infrastructure in the target area, the soil type, process type, geological condition type, and construction grade of the water conservancy infrastructure are encoded using the unique thermal coding method to obtain soil type coding value, process type coding value, geological condition type coding value, and construction grade coding value. Take the integer value of the number of years used, and use the integer value as the value of the number of years used. The acquisition of labeled values ​​for water conservancy infrastructure in the target area includes: The soil type code, process type code, geological condition type code, construction grade code, and service life mark of the water conservancy infrastructure shall be used as the mark value of the water conservancy infrastructure.

[0013] A second aspect of this application provides a water conservancy infrastructure risk early warning system, the water conservancy infrastructure risk early warning system comprising: The data acquisition module is used to acquire optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and the ground distance product data are both data of the satellite revisit period to which the time to be predicted belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. The first temporal deformation sequence determination module is used to determine the first temporal deformation sequence of the target region based on the single-view complex product data and pixel distance; The first soil moisture content sequence determination module is used to determine the first soil moisture content sequence of the target area based on the optical remote sensing data and the ground distance product data; The correlation coefficient calculation module is used to calculate the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence; The early warning module is used to input the labeled values ​​of the water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence and the correlation coefficient into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and to issue an early warning based on the disaster occurrence probability value.

[0014] This system acquires optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and ground distance product data are obtained from satellite revisit cycles of the predicted time obtained via double-orbit time-series synthetic aperture radar (SAPRA). The pixel distance is the pixel distance of the synthetic aperture radar data obtained via SAPRA. Based on the single-view complex product data and pixel distance, the system determines the first temporal deformation sequence of the target area. Based on the optical remote sensing data and ground distance product data, the system determines the first soil moisture content sequence of the target area. The system calculates the correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence. The labeled values ​​of water conservancy infrastructure, the first temporal deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into a disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model. Early warning is then issued based on this disaster occurrence probability value. This application integrates optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure to construct a disaster monitoring model. This overcomes the data silos and institutional deficiencies of traditional technologies and improves the accuracy of early warning for disasters occurring in water conservancy infrastructure in rivers and lakes within the target area.

[0015] A third aspect of this application provides a water infrastructure risk early warning electronic device, including at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, which are executed by the at least one control processor to enable the at least one control processor to perform the above-described water infrastructure risk early warning method.

[0016] In a fourth aspect, this application provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the aforementioned water infrastructure risk early warning method.

[0017] It should be noted that the beneficial effects of the second to fourth aspects of this application with respect to the prior art are the same as the beneficial effects of the aforementioned water conservancy infrastructure risk early warning system with respect to the prior art, and will not be described in detail here.

[0018] 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

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of the water conservancy infrastructure risk early warning method provided in this application; Figure 2 This is a schematic diagram of the structure of an embodiment of the water conservancy infrastructure risk early warning system provided in this application; Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device provided in this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of the 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 are only used to explain this application, and should not be construed as limiting this application.

[0021] In the description of this application, the use of terms such as "first," "second," etc., is for the purpose of distinguishing technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0022] In the description of this application, it should be understood that the orientation descriptions, such as up, down, etc., are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0023] In the description of this application, it should be noted that, unless otherwise explicitly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0024] Water conservancy infrastructure (such as dikes, levees, and embankments) is the core facility of flood control and tide prevention engineering systems, and its structural safety is directly related to the safety of coastal cities, farmland, and people's lives and property. With the increase of service time and the long-term effects of external environment (such as rainfall, water flow erosion, and groundwater changes), water conservancy infrastructure is prone to hidden dangers such as leakage, piping, settlement, and landslides. If these are not identified in time, they may cause major disasters such as dike breaches. Therefore, accurate and efficient detection of risks to water conservancy infrastructure is crucial.

[0025] Currently, risk detection methods for water conservancy infrastructure mostly rely on manual inspections, point-type sensor monitoring (such as piezometers and displacement gauges), or empirical assessments based on the inherent properties of dikes (such as soil type, construction grade, and service life). This results in a lack of comprehensiveness in risk assessment and makes it difficult to meet the needs of water conservancy infrastructure safety management in complex environments.

[0026] To address the aforementioned technical deficiencies, embodiments of this application provide a method, system, device, and storage medium for early warning of risks to water conservancy infrastructure.

[0027] Please see Figure 1 This is a flowchart illustrating a risk early warning method for water conservancy infrastructure provided in an embodiment of this application. The method is applied to electronic devices, such as servers. Figure 1 As shown, the risk early warning method for water conservancy infrastructure includes: Step S101: Obtain optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. Among them, the single-view complex product data and ground distance product data are data of the satellite revisit cycle to which the time to be predicted belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. Step S102: Determine the first temporal deformation sequence of the target region based on single-view complex product data and pixel distance; Step S103: Determine the first soil moisture content sequence of the target area based on optical remote sensing data and ground distance product data; Step S104: Calculate the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence; Step S105: Input the labeled values ​​of water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence and the correlation coefficient into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and issue an early warning based on the disaster occurrence probability value.

[0028] The aforementioned water conservancy infrastructure may include, but is not limited to, dikes, levees, and embankments.

[0029] The code values ​​for soil type, process type, geological condition type, construction grade, and years of use are indicated.

[0030] The aforementioned first temporal deformation sequence may include, but is not limited to, a first temporal deformation variable sequence and a first temporal deformation rate sequence. The aforementioned first temporal deformation sequence may be a temporal deformation sequence arranged in chronological order.

[0031] The first soil moisture content sequence mentioned above can be a soil moisture content sequence arranged in chronological order.

[0032] In step S104 above, early warning is given based on the probability value of disaster occurrence. This can be done by sending an SMS notification to the user when the probability value of disaster occurrence is greater than a preset early warning threshold. The preset early warning threshold can be a constant value that is set in advance according to actual needs.

[0033] This method acquires optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and ground distance product data are obtained from satellite revisit cycles of the predicted time obtained via double-orbit time-series synthetic aperture radar (SAPRA). The pixel distance is the pixel distance of the synthetic aperture radar data obtained via SAPRA. Based on the single-view complex product data and pixel distance, the first temporal deformation sequence of the target area is determined. Based on the optical remote sensing data and ground distance product data, the first soil moisture content sequence of the target area is determined. The correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence is calculated. The labeled values ​​of water conservancy infrastructure, the first temporal deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into a disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model. Early warning is then issued based on the disaster occurrence probability value. This application integrates optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure to construct a disaster monitoring model. This overcomes the data silos and institutional deficiencies of traditional technologies and improves the accuracy of early warning for disasters occurring in water conservancy infrastructure in rivers and lakes within the target area. In some embodiments, step S102 may include, but is not limited to, steps S201 to S204: Step S201: Calculate the second temporal deformation sequence of single-view complex product data using the small baseline set interferometric synthetic aperture radar method. The temporal deformation sequence includes several temporal deformation values ​​and the latitude and longitude corresponding to each temporal deformation value. Step S202: Calculate the third temporal deformation sequence of the single-view complex product data using the permanent scatterer synthetic aperture radar interferometry method; Step S203: Calculate the distance between the first type of point and the second type of point in the preset coordinate system as the distance between the first points. The first type of point is any point in the second time-series deformation sequence, and the second type of point is any point in the third time-series deformation sequence. The points in the time-series deformation sequence are obtained by projecting the latitude and longitude corresponding to each time-series deformation value onto the preset coordinate system. Step S204: Determine the first temporal deformation sequence based on the second temporal deformation sequence, the third temporal deformation sequence, the distance between the first points, and the pixel distance.

[0034] The second temporal deformation sequence mentioned above can be a temporal deformation sequence arranged in chronological order.

[0035] The aforementioned third temporal deformation sequence can be a temporal deformation sequence arranged in chronological order.

[0036] The aforementioned preset coordinate system can be a geodetic coordinate system.

[0037] This application uses the small baseline set interferometric synthetic aperture radar method and the permanent scatterer synthetic aperture radar interferometry method to determine the first time-series deformation sequence, thereby improving the accuracy of the first time-series deformation sequence.

[0038] In some embodiments, step S204 may include, but is not limited to, steps S301 to S308: Step S301: Traverse the points in the second temporal deformation sequence, and select all points in the second temporal deformation sequence whose distance from any point in the third temporal deformation sequence is less than the first preset distance threshold, as the corresponding points in the second temporal deformation sequence, wherein the first preset distance threshold is one-tenth of the pixel distance; Step S302: Determine the fourth temporal deformation sequence, wherein the fourth temporal deformation sequence is a sequence composed of all points other than the corresponding points in the second temporal deformation sequence; Step S303: Calculate the first trend term of the third time-series deformation sequence using a time-series decomposition algorithm; calculate the second trend term of the fourth time-series deformation sequence using a time-series decomposition algorithm, wherein the first trend term belongs to the third time-series deformation sequence and the second trend term belongs to the fourth time-series deformation sequence. Step S304: Calculate the distance between the second type of point and the third type of point in the preset coordinate system, and use it as the distance between the second points, where the third type of point is any point in the fourth time-series deformation sequence; Step S305: Traverse the points in the third temporal deformation sequence and the fourth temporal deformation sequence, and filter out point pairs whose distance to the second point is less than the second preset distance threshold. The point pair consists of the points in the third temporal deformation sequence and the points in the fourth temporal deformation sequence whose distance to the second point is less than the second preset distance threshold. Step S306: Determine the third trend term corresponding to the points of all third time series deformation sequences in the point pair; determine the fourth trend term corresponding to the points of all fourth time series deformation sequences in the point pair, wherein the third trend term belongs to the first trend term and the fourth trend term belongs to the second trend term; Step S307: Replace the fourth trend term in the fourth time series deformation sequence with the third trend term to obtain the fifth time series deformation sequence; Step S308: Combine the third time-series deformation sequence and the fifth time-series deformation sequence to obtain the first time-series deformation sequence.

[0039] This application determines the third and fourth trend terms based on a time series decomposition algorithm, and then replaces the fourth trend term in the fourth time series deformation sequence with the third trend term to obtain the fifth time series deformation sequence. The third and fifth time series deformation sequences are combined to obtain the first time series deformation sequence. The trend term can reflect the core components of the long-term evolution direction of the data, thereby improving the accuracy of the first time series deformation sequence and providing more accurate data basis for subsequent disaster probability prediction.

[0040] In some embodiments, step S103 may include, but is not limited to, steps S401 to S407: Step S401: Extract the homopolarization data and crosspolarization data of the ground distance product data; Step S402: Filter the same polarization data to obtain filtered same polarization data; filter the cross-polarization data to obtain filtered cross-polarization data; Step S403: Geocode the filtered homopolarized data to obtain encoded homopolarized data; geocode the filtered cross-polarized data to obtain encoded cross-polarized data. Step S404: Perform radiometric calibration on the encoded co-polarized data to obtain the co-polarized backscattering coefficient; perform radiometric calibration on the encoded cross-polarized data to obtain the cross-polarized backscattering coefficient. Step S405: Determine the joint backscattering coefficient based on the same polarization backscattering coefficient, cross-polarization backscattering coefficient, and preset backscattering coefficient parameter values; Step S406: Calculate the normalized vegetation index and normalized water index of the optical remote sensing data; Step S407: Input the combined backscattering coefficient, normalized vegetation index and normalized water index into the soil moisture prediction model to obtain the first soil moisture sequence output by the soil moisture measurement model.

[0041] The aforementioned preset backscattering coefficient parameter values ​​include a first parameter value and a second parameter value. The first parameter value can be 0.6, and the second parameter value can be 0.4.

[0042] This application predicts soil moisture content by combining backscattering coefficient, normalized vegetation index, and normalized water index, which improves the accuracy of the first soil moisture content sequence and provides more accurate data for predicting the probability of subsequent disasters.

[0043] In some embodiments, step S405 may include, but is not limited to, steps S501 to S503: Step S501: Multiply the same polarization backscattering coefficient by the first parameter value to obtain the first coefficient; Step S502: Multiply the cross-polarization backscattering coefficient by the second parameter value to obtain the second coefficient; Step S503: Add the first coefficient and the second coefficient to obtain the joint backscattering coefficient.

[0044] This application improves the accuracy of joint backscattering coefficient calculation by giving different parameter values ​​for the co-polarized backscattering coefficient and the cross-polarized backscattering coefficient.

[0045] In some embodiments, step S104 may include, but is not limited to, steps S601 to S603: Step S601: Extract the first temporal deformation sequence within the preset time window as the second temporal deformation sequence; extract the first soil moisture content sequence within the preset time window as the second soil moisture content sequence. Step S602: Calculate the mean temporal deformation of the second temporal deformation sequence; calculate the mean soil moisture content of the second soil moisture content sequence; Step S603: Determine the correlation coefficient based on the second time-series deformation sequence, the second soil moisture content sequence, the time-series deformation mean, and the soil moisture content mean.

[0046] The aforementioned preset time window can be a value that can be preset according to actual needs. The time length of the preset time window is less than or equal to the time length of the first time-series deformation sequence, and the time length of the preset time window is less than or equal to the time length of the second soil moisture content sequence.

[0047] In step S603, the correlation coefficient is calculated based on the second time-series deformation sequence, the second soil moisture content sequence, the time-series deformation mean, and the soil moisture content mean using the following formula: ; in, The correlation coefficient is... The mean of time-series deformation, This represents the average soil moisture content. The starting point within the preset time window, This is the end time point of the first temporal deformation sequence. The second time-series deformation sequence The second time-series deformation value. The first in the second soil moisture content sequence The second soil moisture content value.

[0048] This application introduces a correlation coefficient to provide more accurate data for predicting the probability of subsequent disasters.

[0049] In some embodiments, before obtaining the labeled values ​​of the water infrastructure in the target area, the method further includes: Step S701: After obtaining the soil type, process type, geological condition type, construction grade and service life of the water conservancy infrastructure in the target area, the soil type, process type, geological condition type and construction grade of the water conservancy infrastructure are coded respectively by the unique thermal coding method to obtain the soil type code value, process type code value, geological condition type code value and construction grade code value. Step S702: Take the integer value of the number of years used and use the integer value as the value of the number of years used; Obtain the labeled values ​​of the water conservancy infrastructure in the target area, including: Step S703: Use the soil type code value, process type code value, geological condition type code value, construction grade code value, and service life mark value of the water conservancy infrastructure as the mark value of the water conservancy infrastructure.

[0050] The types of soil mentioned above may include, but are not limited to, clay, loam, and bentonite.

[0051] The aforementioned types of construction techniques may include, but are not limited to, earth-rock dams, concrete dams, and masonry embankments.

[0052] The aforementioned geological conditions may include, but are not limited to, soft foundations and rock foundations.

[0053] The aforementioned construction levels may include, but are not limited to, Level 1, Level 2, Level 3, Level 4, and Level 5.

[0054] The aforementioned service life refers to the historical service life of the water conservancy infrastructure in the target area.

[0055] The above-mentioned rounding of the years already used to the nearest integer can be used to round up the historical years of use of the water conservancy infrastructure in the target area.

[0056] This application calculates the probability of disasters occurring on river and lake embankments within a target area, providing early warnings of potential risks and hazards. It assists embankment inspection personnel in quickly locating risk areas, reducing their workload and improving their efficiency.

[0057] Additionally, refer to Figure 2 One embodiment of this application provides a water conservancy infrastructure risk early warning system, including a data acquisition module 1100, a first time-series deformation sequence determination module 1200, a first soil moisture content sequence determination module 1300, a correlation coefficient calculation module 1400, and an early warning module 1500, wherein: The data acquisition module 1100 is used to acquire optical remote sensing data, single-view complex product data, ground distance product data, pixel distance and annotation values ​​of water conservancy infrastructure in the target area. Among them, the single-view complex product data and ground distance product data are data of the satellite revisit period to which the time to be predicted belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. The first temporal deformation sequence determination module 1200 is used to determine the first temporal deformation sequence of the target area based on single-view complex product data and pixel distance; The first soil moisture content sequence determination module 1300 is used to determine the first soil moisture content sequence of the target area based on optical remote sensing data and ground distance product data; The correlation coefficient calculation module 1400 is used to calculate the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence; The early warning module 1500 is used to input the labeled values ​​of water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence and the correlation coefficient into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and to issue an early warning based on the disaster occurrence probability value.

[0058] This system acquires optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and ground distance product data are obtained from satellite revisit cycles of the predicted time obtained via double-orbit time-series synthetic aperture radar (SAPRA). The pixel distance is the pixel distance of the synthetic aperture radar data obtained via SAPRA. Based on the single-view complex product data and pixel distance, the system determines the first temporal deformation sequence of the target area. Based on the optical remote sensing data and ground distance product data, the system determines the first soil moisture content sequence of the target area. The system calculates the correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence. The labeled values ​​of water conservancy infrastructure, the first temporal deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into a disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model. Early warning is then issued based on this disaster occurrence probability value. This application integrates optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure to construct a disaster monitoring model. This overcomes the data silos and institutional deficiencies of traditional technologies and improves the accuracy of early warning for disasters occurring in water conservancy infrastructure in rivers and lakes within the target area.

[0059] It should be noted that the system embodiments described above are based on the same inventive concept as the method embodiments described above. Therefore, the relevant content of the method embodiments described above is also applicable to the system embodiments described above, and will not be repeated here.

[0060] Figure 3A schematic diagram of the hardware structure for risk early warning of water conservancy infrastructure provided in an embodiment of this application is shown.

[0061] The risk early warning device for water conservancy infrastructure may include a processor 301 and a memory 302 storing computer program instructions.

[0062] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0063] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0064] In some embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0065] The processor 301 reads and executes computer program instructions stored in the memory 302 to implement any of the water conservancy infrastructure risk early warning methods in the above embodiments.

[0066] In one example, the water conservancy infrastructure risk early warning device may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0067] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0068] Bus 310 includes hardware, software, or both, that couples components of a water infrastructure risk early warning device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0069] The water conservancy infrastructure risk early warning device can execute the water conservancy infrastructure risk early warning method in this application embodiment based on a three-dimensional design model, thereby achieving a combination of Figure 1 and Figure 2 The methods and systems for early warning of risks to water conservancy infrastructure are described.

[0070] Furthermore, in conjunction with the water infrastructure risk early warning method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the water infrastructure risk early warning methods in the above embodiments.

[0071] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0072] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0073] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0074] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0075] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for early warning of risks in water conservancy infrastructure, characterized in that, The aforementioned risk early warning methods for water conservancy infrastructure include: Acquire optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure for the target area. The single-view complex product data and the ground distance product data are both data of the satellite revisit period to which the predicted time belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. The first temporal deformation sequence of the target region is determined based on the single-view complex product data and pixel distance, specifically as follows: The second temporal deformation sequence of the single-view complex product data is calculated using the small baseline set interferometric synthetic aperture radar method, wherein the temporal deformation sequence includes several temporal deformation values ​​and the latitude and longitude corresponding to each temporal deformation value; The third temporal deformation sequence of the single-view complex product data was calculated using the permanent scatterer synthetic aperture radar interferometry method. Calculate the distance between the first type of point and the second type of point in the preset coordinate system, and use it as the distance between the first points. The first type of point is any point in the second time-series deformation sequence, and the second type of point is any point in the third time-series deformation sequence. The points in the time-series deformation sequence are obtained by projecting the latitude and longitude corresponding to each time-series deformation value onto the preset coordinate system. Based on the second temporal deformation sequence, the third temporal deformation sequence, the first point distance, and the pixel distance, the first temporal deformation sequence is determined as follows: Traverse the points in the second temporal deformation sequence, and filter out all points in the second temporal deformation sequence whose distance from any point in the third temporal deformation sequence is less than a first preset distance threshold, as the corresponding points in the second temporal deformation sequence, wherein the first preset distance threshold is one-tenth of the pixel distance; Determine a fourth temporal deformation sequence, wherein the fourth temporal deformation sequence is a sequence consisting of all points in the second temporal deformation sequence except for the points with the same name in the second temporal deformation sequence; The first trend term of the third time-series deformation sequence is calculated using a time-series decomposition algorithm; the second trend term of the fourth time-series deformation sequence is calculated using a time-series decomposition algorithm, wherein the first trend term belongs to the third time-series deformation sequence and the second trend term belongs to the fourth time-series deformation sequence; Calculate the distance between the second type of point and the third type of point in the preset coordinate system, and use it as the distance between the second points, wherein the third type of point is any point in the fourth temporal deformation sequence; Traverse the points in the third temporal deformation sequence and the fourth temporal deformation sequence, and filter out point pairs whose distance to the second point is less than a second preset distance threshold. The point pair consists of the points in the third temporal deformation sequence and the points in the fourth temporal deformation sequence whose distance to the second point is less than the second preset distance threshold. Determine the third trend term corresponding to all points of the third temporal deformation sequence in the point pair; determine the fourth trend term corresponding to all points of the fourth temporal deformation sequence in the point pair, wherein the third trend term belongs to the first trend term and the fourth trend term belongs to the second trend term; The third trend term is replaced with the fourth trend term in the fourth time series deformation sequence to obtain the fifth time series deformation sequence; The first temporal deformation sequence is obtained by combining the third temporal deformation sequence and the fifth temporal deformation sequence; Based on the optical remote sensing data and the ground distance product data, a first soil moisture content sequence for the target area is determined; Calculate the correlation coefficient between the first temporal deformation sequence and the first soil moisture content sequence; The labeled values ​​of the water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence, and the correlation coefficient are input into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and an early warning is issued based on the disaster occurrence probability value.

2. The method for early warning of risks in water conservancy infrastructure according to claim 1, characterized in that, The determination of the first soil moisture content sequence of the target area based on the optical remote sensing data and the ground distance product data includes: Extract the homopolarization and crosspolarization data of the ground distance product data; The same-polarization data is filtered to obtain filtered same-polarization data; the cross-polarization data is filtered to obtain filtered cross-polarization data. Geocoding is performed on the filtered homopolarized data to obtain encoded homopolarized data; geocoding is performed on the filtered cross-polarized data to obtain encoded cross-polarized data. Radiometric calibration is performed on the encoded homopolarized data to obtain homopolarized backscattering coefficients; radiometric calibration is performed on the encoded crosspolarized data to obtain crosspolarized backscattering coefficients. The joint backscattering coefficient is determined based on the same polarization backscattering coefficient, the cross-polarization backscattering coefficient, and the preset backscattering coefficient parameter value; Calculate the normalized vegetation index and normalized water index of the optical remote sensing data; The joint backscattering coefficient, the normalized vegetation index, and the normalized water index are input into the soil moisture content prediction model to obtain the first soil moisture content sequence output by the soil moisture content measurement model.

3. The method for early warning of risks in water conservancy infrastructure according to claim 2, characterized in that, The preset backscattering coefficient parameter values ​​include a first parameter value and a second parameter value. Determining the joint backscattering coefficient based on the same-polarization backscattering coefficient, the cross-polarization backscattering coefficient, and the preset backscattering coefficient parameter values ​​includes: Multiplying the same polarization backscattering coefficient by the first parameter value yields the first coefficient; Multiplying the cross-polarization backscattering coefficient by the second parameter value yields the second coefficient; The first coefficient and the second coefficient are added together to obtain the joint backscattering coefficient.

4. The method for early warning of risks in water conservancy infrastructure according to claim 1, characterized in that, The calculation of the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence includes: Extract the first temporal deformation sequence within a preset time window as the second temporal deformation sequence; extract the first soil moisture content sequence within a preset time window as the second soil moisture content sequence; Calculate the temporal deformation mean of the second temporal deformation sequence; calculate the soil moisture mean of the second soil moisture content sequence; The correlation coefficient is determined based on the second time-series deformation sequence, the second soil moisture content sequence, the mean time-series deformation, and the mean soil moisture content.

5. The method for early warning of risks in water conservancy infrastructure according to claim 4, characterized in that, Before obtaining the labeled values ​​of the water conservancy infrastructure in the target area, the method further includes: Having obtained the soil type, process type, geological condition type, construction grade, and service life of the water conservancy infrastructure in the target area, the soil type, process type, geological condition type, and construction grade of the water conservancy infrastructure are encoded using the unique thermal coding method to obtain soil type coding value, process type coding value, geological condition type coding value, and construction grade coding value. Take the integer value of the number of years used, and use the integer value as the value of the number of years used. The acquisition of labeled values ​​for water conservancy infrastructure in the target area includes: The soil type code, process type code, geological condition type code, construction grade code, and service life mark of the water conservancy infrastructure shall be used as the mark value of the water conservancy infrastructure.

6. A risk early warning system for water conservancy infrastructure, characterized in that, The water conservancy infrastructure risk early warning system includes: The data acquisition module is used to acquire optical remote sensing data, single-view complex product data, ground distance product data, pixel distance, and labeled values ​​of water conservancy infrastructure in the target area. The single-view complex product data and the ground distance product data are both data of the satellite revisit period to which the time to be predicted belongs, obtained by heavy orbit time-series synthetic aperture radar. The pixel distance is the pixel distance of synthetic aperture radar data obtained by heavy orbit time-series synthetic aperture radar. The first temporal deformation sequence determination module is used to determine the first temporal deformation sequence of the target region based on the single-view complex product data and pixel distance, specifically: The second temporal deformation sequence of the single-view complex product data is calculated using the small baseline set interferometric synthetic aperture radar method, wherein the temporal deformation sequence includes several temporal deformation values ​​and the latitude and longitude corresponding to each temporal deformation value; The third temporal deformation sequence of the single-view complex product data was calculated using the permanent scatterer synthetic aperture radar interferometry method. Calculate the distance between the first type of point and the second type of point in the preset coordinate system, and use it as the distance between the first points. The first type of point is any point in the second time-series deformation sequence, and the second type of point is any point in the third time-series deformation sequence. The points in the time-series deformation sequence are obtained by projecting the latitude and longitude corresponding to each time-series deformation value onto the preset coordinate system. Based on the second temporal deformation sequence, the third temporal deformation sequence, the first point distance, and the pixel distance, the first temporal deformation sequence is determined as follows: Traverse the points in the second temporal deformation sequence, and filter out all points in the second temporal deformation sequence whose distance from any point in the third temporal deformation sequence is less than a first preset distance threshold, as the corresponding points in the second temporal deformation sequence, wherein the first preset distance threshold is one-tenth of the pixel distance; Determine a fourth temporal deformation sequence, wherein the fourth temporal deformation sequence is a sequence consisting of all points in the second temporal deformation sequence except for the points with the same name in the second temporal deformation sequence; The first trend term of the third time-series deformation sequence is calculated using a time-series decomposition algorithm; the second trend term of the fourth time-series deformation sequence is calculated using a time-series decomposition algorithm, wherein the first trend term belongs to the third time-series deformation sequence and the second trend term belongs to the fourth time-series deformation sequence; Calculate the distance between the second type of point and the third type of point in the preset coordinate system, and use it as the distance between the second points, wherein the third type of point is any point in the fourth temporal deformation sequence; Traverse the points in the third temporal deformation sequence and the fourth temporal deformation sequence, and filter out point pairs whose distance to the second point is less than a second preset distance threshold. The point pair consists of the points in the third temporal deformation sequence and the points in the fourth temporal deformation sequence whose distance to the second point is less than the second preset distance threshold. Determine the third trend term corresponding to all points of the third temporal deformation sequence in the point pair; determine the fourth trend term corresponding to all points of the fourth temporal deformation sequence in the point pair, wherein the third trend term belongs to the first trend term and the fourth trend term belongs to the second trend term; The third trend term is replaced with the fourth trend term in the fourth time series deformation sequence to obtain the fifth time series deformation sequence; The first temporal deformation sequence is obtained by combining the third temporal deformation sequence and the fifth temporal deformation sequence; The first soil moisture content sequence determination module is used to determine the first soil moisture content sequence of the target area based on the optical remote sensing data and the ground distance product data; The correlation coefficient calculation module is used to calculate the correlation coefficient between the first time-series deformation sequence and the first soil moisture content sequence; The early warning module is used to input the labeled values ​​of the water conservancy infrastructure, the first time-series deformation sequence, the first soil moisture content sequence and the correlation coefficient into the disaster monitoring model to obtain the disaster occurrence probability value output by the disaster monitoring model, and to issue an early warning based on the disaster occurrence probability value.

7. A risk early warning device for water conservancy infrastructure, characterized in that, It includes at least one control processor and a memory for communicatively connecting to the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform a water infrastructure risk early warning method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions for causing a computer to execute a water conservancy infrastructure risk early warning method as described in any one of claims 1 to 5.

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