Landslide monitoring method, device and equipment based on recursive sequential estimation and medium

Abstract

The invention relates to the technical field of disaster prediction. The invention discloses a landslide monitoring method, device and equipment based on recursive sequential estimation and a medium, and the method comprises the steps: obtaining landslide monitoring data, and extracting time features in the landslide monitoring data; dividing the landslide monitoring data into a plurality of subsets based on a preset subset division rule according to time characteristics in the landslide monitoring data; calculating a target time sequence phase of each subset based on a preset phase calculation model; and based on a preset recursive sequential adjustment model, calculating landslide deformation parameters according to the target time sequence phase of each subset. The sizes of the subsets are determined according to the time characteristics, the flexibility is high, and therefore approximate real-time data processing is achieved. Through application of the recursive sequential adjustment model, deviation and jumping phenomena among subsets are effectively avoided, the accuracy of deformation time sequence analysis is improved, and the landslide disaster monitoring efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of disaster prediction, and in particular to a landslide monitoring method, device, equipment and medium based on recursive sequential estimation. Background Art

[0002] Landslide disasters are one of the most threatening disasters to people's lives, property and public infrastructure, especially in complex mountainous areas where landslide disasters are widespread. Complex mountainous areas are characterized by diverse terrain, significant elevation changes, diverse landforms, complex strata and lithology, developed folds and faults, active neotectonic movements, and frequent earthquakes. The occurrence of landslide disasters often causes huge casualties and economic losses, seriously threatening the safety of the people in mountainous areas and national infrastructure. Therefore, it is very important to monitor the deformation of landslides in complex mountainous areas. Observing and analyzing the deformation before a landslide disaster can objectively and truly reflect the deformation evolution process of the landslide body, and provide important decision-making basis for disaster prediction and early warning.

[0003] As a non-contact active remote sensing method, Synthetic Aperture Radar Interferometry (InSAR), especially Multi Temporal InSAR (MTI), has been widely used in landslide hazard mapping and monitoring. However, traditional MTI technology can only identify a limited number of measurement points (MP) in complex mountainous areas (dense vegetation and complex terrain), resulting in phase unwrapping errors, which increases the uncertainty of deformation estimation. Distributed scatterer radar interferometry (DS-InSAR) based on small baseline set (SBAS) technology has developed rapidly, but DS targets are susceptible to spatiotemporal decorrelation and only maintain coherence in interferograms with short spatiotemporal baselines. In order to maximize the spatial density of monitoring point targets, permanent scatterers (PS) and DS can be jointly analyzed to improve the reliability of phase unwrapping. In recent years, a large number of studies have focused on the automation and optimization of MTI processing, but the core time series analysis method still uses batch processing mode. When new observation data are added, the entire archived data set needs to be reprocessed, completely ignoring the existing results, which is not suitable for efficient observation data processing. Therefore, the current MTI method has the problem of low utilization of archived data and inability to achieve near real-time updates, resulting in low efficiency in landslide hazard monitoring and making it difficult to implement early warning of catastrophic landslide events. Therefore, finding a landslide monitoring method with high landslide hazard monitoring efficiency and strong early warning capability is a technical problem that needs to be solved. Summary of the invention

[0004] In view of the above situation, the embodiments of the present application provide a landslide monitoring method, device, equipment and medium based on recursive sequential estimation, aiming to solve the above problem or at least partially solve the above problem.

[0005] In a first aspect, the present application provides a landslide monitoring method based on recursive sequential estimation, comprising: Acquiring landslide monitoring data, and extracting time features from the landslide monitoring data; According to the time characteristics in the landslide monitoring data, based on a preset subset division rule, the landslide monitoring data is divided into a plurality of subsets; Based on a preset phase calculation model, the target time series phase of each subset is calculated; Based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset.

[0006] Exemplarily, the landslide monitoring data includes multi-view image data; The time characteristics include: a period for acquiring image data of one scene, and an acquisition time for each image data of one scene.

[0007] Exemplarily, according to the time characteristics in the landslide monitoring data, based on a preset subset division rule, the landslide monitoring data is divided into a plurality of subsets, including: According to the acquisition time of each scene of image data in the time feature, based on a preset first preset condition, the landslide monitoring data is divided into a plurality of initial data sets; According to the period of acquiring a scene of image data in the time feature, each of the initial data sets is divided into a plurality of subsets based on a plurality of preset second preset conditions.

[0008] Exemplarily, according to the period of acquiring a scene of image data in the time feature, based on a plurality of preset second preset conditions, each of the initial data sets is divided into a plurality of subsets, including: When the period of acquiring a scene of image data satisfies the first second preset condition, the initial data set is divided into subsets according to the first time length; When the period of acquiring the image data of a scene satisfies the second second preset condition, the initial data set is divided into subsets according to the second time length; When the period of acquiring a scene of image data satisfies the third second preset condition, the initial data set is divided into subsets according to the third time length.

[0009] Exemplarily, the last scene image data of the previous subset and the first scene image data of the next subset in two adjacent subsets are the same.

[0010] Exemplarily, based on a preset phase calculation model, calculating the optimal time series phase of each subset includes: Sorting the plurality of subsets according to time, and calculating the complex coherence matrix of each pixel in each subset; According to the complex coherence matrix of each pixel, based on the preset solution model, the target time series phase of each subset is obtained.

[0011] Exemplarily, based on a preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset, including: sorting the target time series phases of multiple subsets according to time; Based on a preset recursive sequential adjustment model, the landslide deformation parameters of the last subset are recursively obtained according to the target time series phases of multiple subsets. In the recursive process, the target time series phases of all subsets before the target subset are taken as the first target time series phase, and the target time series phase of the target subset is taken as the second target time series phase. The landslide deformation parameters of the target subset are obtained according to the first target time series phase and the second target time series phase.

[0012] In a second aspect, a landslide monitoring device based on recursive sequential estimation is provided, comprising: An acquisition module, used for acquiring landslide monitoring data and extracting time features in the landslide monitoring data; A partitioning module, configured to partition the landslide monitoring data into a plurality of subsets according to time features in the landslide monitoring data and based on a preset subset partitioning rule; A first calculation module, used for calculating the target time series phase of each subset based on a preset phase calculation model; The second calculation module is used to calculate the landslide deformation parameters according to the target time series phase of each subset based on a preset recursive sequential adjustment model.

[0013] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the landslide monitoring method based on recursive sequential estimation as described in the first aspect are implemented.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the landslide monitoring method based on recursive sequential estimation as described in the first aspect are implemented.

[0015] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects: In this application, the size of the subset is determined according to the time characteristics, which is highly flexible, thereby achieving near real-time data processing. The application of the recursive sequential adjustment model effectively avoids deviations and jumps between subsets, improves the accuracy of deformation time series analysis, and improves the efficiency of landslide disaster monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a schematic diagram of an application environment of a landslide monitoring method based on recursive sequential estimation in one embodiment of the present invention; Figure 2 is a flow chart of a landslide monitoring method based on recursive sequential estimation in one embodiment of the present invention; Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step S20; Figure 4 yes Figure 2 A schematic flow chart of a specific implementation of step S30; Figure 5 is a structural schematic diagram of a landslide monitoring device based on recursive sequential estimation in one embodiment of the present invention; Figure 6 is a schematic diagram of a structure of a computer device in one embodiment of the present invention; Figure 7 It is another structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such use is interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "including" and its variants are to be interpreted as open-ended terms meaning "including but not limited to".

[0019] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0020] As mentioned above, the current MTI method has the problem of low utilization rate of archived data and inability to achieve near real-time update, resulting in low efficiency of landslide disaster monitoring and making it difficult to implement early warning of catastrophic landslide events. In order to solve this technical problem, the embodiment of the present application provides a landslide monitoring method based on recursive sequential estimation.

[0021] The landslide monitoring method based on recursive sequential estimation provided by the embodiment of the present invention can be applied in the following aspects: Figure 1 In an application environment, the device side communicates with the server side through a network. The server side can obtain the landslide monitoring data through the device side, and extract the time characteristics in the landslide monitoring data; according to the time characteristics in the landslide monitoring data, based on the preset subset division rules, the landslide monitoring data is divided into multiple subsets; based on the preset phase calculation model, the target time series phase of each subset is calculated; based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset. In the present application, the size of the subset is determined according to the time characteristics, and it is highly flexible, thereby realizing near real-time data processing. The application of the recursive sequential adjustment model effectively avoids deviations and jump phenomena between subsets, improves the accuracy of deformation time series analysis, and improves the efficiency of landslide disaster monitoring.

[0022] The device side may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and portable wearable devices. The server side may be implemented by an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0023] See also Figure 2 As shown, Figure 2 A flow chart of a landslide monitoring method based on recursive sequential estimation provided by an embodiment of the present invention includes the following steps: S10: Acquire landslide monitoring data, and extract time features from the landslide monitoring data.

[0024] Exemplarily, the landslide monitoring data may be SAR image data simulated using a pure exponential decorrelation model, or may be real image data obtained through a remote sensing satellite.

[0025] Exemplarily, the landslide monitoring data includes multi-view image data, where a "view" refers to an image obtained by observing a target area when a satellite passes over it.

[0026] Exemplarily, the time features include: the period of acquiring image data of a scene, and the acquisition time of each image data. The period of acquiring image data of a scene refers to the time interval for acquiring images of the same area, which affects the timeliness of the image data. The acquisition time of each image data refers to the specific time of acquiring the image data, including year, month, day, hour, minute, and second, for example, 16:55:54 on February 1, 2025.

[0027] After step S10 and before step S20, the method further includes: sorting the landslide monitoring data according to the acquisition time of each scene of image data in the time feature.

[0028] S20: Dividing the landslide monitoring data into a plurality of subsets according to the time characteristics in the landslide monitoring data and based on a preset subset division rule.

[0029] For example, Figure 3 As shown, step S20 includes: S21: dividing the landslide monitoring data into a plurality of initial data sets according to the acquisition time of each scene of image data in the time feature and based on a preset first preset condition; Exemplarily, the first preset condition is used to divide the landslide monitoring data according to a preset time period, which may be one year, half a year, or one quarter. In this embodiment, the first preset condition is to divide the landslide monitoring data according to a year, and the data in an initial data set is data with a time span of one year, such as July 8, 2015 to July 7, 2016, or January 1, 2015 to December 31, 2015.

[0030] Alternatively, the first preset condition is used to divide the landslide monitoring data according to year, and the data in each initial data set belongs to the same year.

[0031] Exemplarily, the data in each initial data set is sorted by time, and the multiple initial data sets are sorted by time. The last scene image data in the previous initial data set and the first scene image data in the next initial data set of two adjacent initial data sets are the same.

[0032] S22: According to a period of acquiring a scene of image data in the time feature, based on a plurality of preset second preset conditions, each of the initial data sets is divided into a plurality of subsets.

[0033] Exemplarily, step S22 further includes: When the period of acquiring a scene of image data satisfies the first second preset condition, the initial data set is divided into subsets according to the first time length; When the period of acquiring the image data of a scene satisfies the second second preset condition, the initial data set is divided into subsets according to the second time length; When the period of acquiring a scene of image data satisfies the third second preset condition, the initial data set is divided into subsets according to the third time length.

[0034] Exemplarily, the first second preset condition is a first time period, such as 12 days; the second second preset condition is a second time period, such as 24 days; the third second preset condition is a third time period, such as 36 days. The first duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand, the second duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand, the third duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand, and the first duration, the second duration and the third duration are different. Specifically, in this embodiment, the third duration is longer than the second duration, the second duration is longer than the first duration, the first duration is one quarter, the second duration is half a year, and the third duration is one year.

[0035] For example, if the period for acquiring image data of a scene in an initial data set is 12 days, the first second preset condition is met, and the data in this initial data set is divided into subsets according to the duration of one quarter, that is, the data acquired in one year is divided into 4 subsets. If the period for acquiring image data of a scene in an initial data set is 24 days, the second second preset condition is met, and the data in this initial data set is divided into subsets according to the duration of half a year, that is, the data acquired in one year is divided into 2 subsets. If the period for acquiring image data of a scene in an initial data set is 36 days, the third second preset condition is met, and the data in this initial data set is divided into subsets according to the duration of one year, that is, the data acquired in one year is divided into 1 subset. In this way, the large data set containing landslide monitoring data Partition into multiple subsets of flexible size .

[0036] Exemplarily, in another embodiment, step S20 divides the landslide monitoring data into a plurality of subsets based on a preset subset division rule according to the time feature in the landslide monitoring data, and further includes: dividing the landslide monitoring data into a plurality of subsets based on a plurality of preset second preset conditions according to a period of obtaining a scene image data in the time feature; Exemplarily, the period for acquiring a scene image data is compared with the first second preset condition, the second second preset condition, and the third second preset condition, and the landslide monitoring data that meets the first second preset condition is divided into a first initial data set, and the first initial data set is divided into subsets according to a first time length; the landslide monitoring data that meets the second second preset condition is divided into a second initial data set, and the second initial data set is divided into subsets according to a second time length; the landslide monitoring data that meets the third second preset condition is divided into a third initial data set, and the third initial data set is divided into subsets according to a third time length.

[0037] Exemplarily, the first second preset condition is a first time period, such as 12 days, and the first duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand; the second second preset condition is a second time period, such as 24 days, and the second duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand; the third second preset condition is a third time period, such as 36 days, and the third duration is one year or half a year or one quarter or one month or half a month or other specific days set according to demand. The first duration, the second duration and the third duration are different. Specifically, in this embodiment, the third duration is longer than the second duration, the second duration is longer than the first duration, the first duration is one quarter, the second duration is half a year, and the third duration is one year.

[0038] For example, the period of obtaining image data of a scene in the first initial data set is 12 days, and the data in this initial data set are divided into subsets according to the duration of one quarter. The period of obtaining image data of a scene in the second initial data set is 24 days, and the data in this initial data set are divided into subsets according to the duration of half a year. The period of obtaining image data of a scene in the third initial data set is 36 days. The data in this initial data set are divided into subsets according to the duration of one year. In this way, a large data set containing landslide monitoring data will be Partition into multiple subsets of flexible size .

[0039] Exemplarily, the last scene image data of the previous subset and the first scene image data of the next subset in two adjacent subsets are the same.

[0040] Illustratively, in the process of dividing subsets in this embodiment, seasonal changes or uneven distribution of image acquisition time are taken into consideration, the subset size is flexible, and there is no strict requirement on the number of images in each subset, thereby achieving near real-time data processing.

[0041] S30: Calculate the target time series phase of each subset based on a preset phase calculation model.

[0042] Exemplarily, in the present application, operations such as subset data compression and generation of artificial interference graphs are not required, which saves time and improves data processing efficiency.

[0043] For example, Figure 4 As shown, step S30 includes: S31: Sort the plurality of subsets according to time, and calculate the complex coherence matrix of each pixel in each subset.

[0044] Exemplarily, each subset includes a plurality of pixels, where a pixel refers to a rock, mineral or other geological unit formed in a geological period.

[0045] For example, pixel On a homogeneous path shared within Homogeneous pixel points. A homogeneous path refers to an irregular pixel set composed of multiple homogeneous pixel points. Homogeneous pixel points refer to monitoring point targets with the same or similar properties.

[0046] Exemplarily, the complex coherence matrix of each pixel is calculated according to the following formula: :

[0047] In the formula, represents Hermitian conjugation, for A complex vector of scene regularization.

[0048] For example, the complex coherence matrix is ​​usually obtained by processing synthetic aperture radar interferometry (InSAR) data. It reflects the coherence changes of pixels in a time series and is the basis for phase optimization and estimation.

[0049] S32: According to the complex coherence matrix of each pixel and based on a preset solution model, the target time series phase of each subset is obtained.

[0050] Exemplarily, the target time series phase is an optimal time series phase, which is obtained by maximizing the likelihood function through an optimization algorithm.

[0051] Exemplarily, in this embodiment, the pre-set solution model includes a maximum likelihood estimation method and a phase linking method. The phase linking method and the maximum likelihood estimation method are used in combination to improve the accuracy and stability of phase estimation. Common phase linking algorithms include: phase triangulation algorithm (PTA), interferometric phase maximum likelihood estimator (EMI) based on eigendecomposition.

[0052] Maximum likelihood estimation is a statistical method used to estimate the optimal value of a parameter from data. In phase estimation, maximum likelihood estimation can be achieved by following the following steps: Constructing a likelihood function: Based on the probability distribution of the phase data, construct a likelihood function. For example, assuming that the phase data follows a certain distribution (such as a Gaussian distribution), the likelihood function can be expressed as a function of the phase parameter.

[0053] Optimization parameters: The likelihood function is maximized through the optimization algorithm to obtain the optimal phase parameters, that is, the optimal time series phase.

[0054] For example, the optimal time series phase of each subset is solved using the maximum likelihood estimation method:

[0055] in, ,here , is the Hadamard product. The optimal time series phase of each subset , where N refers to the number of scenes of image data in each subset.

[0056] For example, using the phase linking method, formula (2) can be expressed in a closed form:

[0057] in, is the number of iterations. n, m represent the positions in the complex coherence matrix. The iteration starting point assumes that the phase vector .

[0058] In the present application, the phase link is not independent and does not require data compression and generation of artificial interference patterns, because a head-to-tail connection mode is adopted to facilitate the connection of all subsets, which means that the last scene image of the previous subset is the first scene image of the next subset to achieve the connection of all subsets.

[0059] S40: Based on a preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset.

[0060] Exemplarily, step S40 calculates the landslide deformation parameters based on the preset recursive sequential adjustment model according to the target time series phase of each subset, including: S41: sorting the target time series phases of multiple subsets according to time; S42: Based on a preset recursive sequential adjustment model, the landslide deformation parameters of the last subset are recursively obtained according to the target time series phases of multiple subsets, wherein during the recursive process, the target time series phases of all subsets before the target subset are used as the first target time series phase, and the target time series phase of the target subset is used as the second target time series phase, and the landslide deformation parameters of the target subset are obtained according to the first target time series phase and the second target time series phase.

[0061] Exemplarily, the time series phases of multiple subsets are sequentially connected using a recursive sequential adjustment model. In the recursive sequential adjustment process, the best initial estimate of the second target time series phase is derived from the adjustment result of the first target time series phase. For example, in the first iteration, the target subset is the second subset. When calculating the landslide deformation parameters of the second subset, the first target time series phase is the target time series phase of the first subset, and the second target time series phase is the target time series phase of the second subset. When calculating, the best initial estimate of the second target time series phase comes from the adjustment result of the first target time series phase; in the second iteration, the target subset is the third subset. When calculating the landslide deformation parameters of the third subset, the first target time series phase is the integration of the target time series phase of the first subset and the target time series phase of the second subset, and the second target time series phase is the target time series phase of the third subset. When calculating, the best initial estimate of the second target time series phase comes from the adjustment result of the first target time series phase; in this way, when the target subset is the last subset, the landslide deformation parameters of the last subset are calculated, the first target time series phase is the integration of the target time series phases of all subsets before the last subset, and the second target time series phase is the target time series phase of the last subset. When calculating, the best initial estimate of the second target time series phase comes from the adjustment result of the first target time series phase. In this way, all subsets are connected in sequence by recursion.

[0062] Exemplarily, the recursive process includes: based on a preset recursive sequential adjustment model, according to the first target time series phase and the second target time series phase, obtaining the best initial estimate of the second target time series phase; based on the preset recursive sequential adjustment model, obtaining the landslide deformation parameters of the target subset according to the best initial estimate of the second target time series phase.

[0063] Exemplarily, the observation equation and the recursive equation of the first target time series phase and the second target time series phase can be expressed as:

[0064] In the formula, is the first target time series phase; , , , are the landslide deformation parameters, coefficient matrix, observation noise vector, and weight matrix of the phase corresponding subset of the first target time series respectively; is the second target time series phase; , , , They are respectively the landslide deformation parameters, coefficient matrix, observation noise vector and weight matrix of the target subset.

[0065] Set the adjustment value of the first target time series phase and the covariance matrix for:

[0066] and As a parameter The prior information of , that is, as the best initial estimate of the phase of the second target time series, the adjustment value of the phase of the second target time series is and its covariance matrix Updated to:

[0067] Gain Matrix It can be expressed as:

[0068] The newly added images are located in the last subset. The landslide deformation parameters of the newly added images can be obtained by recursive sequential adjustment model, and the updated deformation time series can be estimated by numerical integration.

[0069] It can be seen that in the above scheme, the size of the subset in this application is determined according to the time characteristics, which is highly flexible, thereby achieving near real-time data processing. The application of the recursive sequential adjustment model effectively avoids deviations and jumps between subsets, improves the accuracy of deformation time series analysis, and improves the efficiency of landslide disaster monitoring.

[0070] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0071] In one embodiment, a landslide monitoring device based on recursive sequential estimation is provided, and the landslide monitoring device based on recursive sequential estimation corresponds one-to-one to the landslide monitoring method based on recursive sequential estimation in the above embodiment. Figure 5As shown, the landslide monitoring device based on recursive sequential estimation includes an acquisition module 101, a division module 102, a first calculation module 103, and a second calculation module 104. The functional modules are described in detail as follows: An acquisition module 101 is used to acquire landslide monitoring data and extract time features from the landslide monitoring data; A division module 102, configured to divide the landslide monitoring data into a plurality of subsets according to the time characteristics in the landslide monitoring data and based on a preset subset division rule; A first calculation module 103, used to calculate the target time series phase of each subset based on a preset phase calculation model; The second calculation module 104 is used to calculate the landslide deformation parameters according to the target time series phase of each subset based on a preset recursive sequential adjustment model.

[0072] Specifically, the acquisition module 101 is further used to: The landslide monitoring data are sorted according to the acquisition time of each scene of image data in the time feature.

[0073] Specifically, the division module 102 is further used to: According to the acquisition time of each scene of image data in the time feature, based on a preset first preset condition, the landslide monitoring data is divided into a plurality of initial data sets; According to the period of acquiring a scene of image data in the time feature, each of the initial data sets is divided into a plurality of subsets based on a plurality of preset second preset conditions.

[0074] Specifically, the division module 102 is further used to: When the period of acquiring a scene of image data satisfies the first second preset condition, the initial data set is divided into subsets according to the first time length; When the period of acquiring the image data of a scene satisfies the second second preset condition, the initial data set is divided into subsets according to the second time length; When the period of acquiring a scene of image data satisfies the third second preset condition, the initial data set is divided into subsets according to the third time length.

[0075] Specifically, the division module 102 is further used to: According to the period of acquiring a scene of image data in the time feature, based on a plurality of preset second preset conditions, the landslide monitoring data is divided into a plurality of subsets.

[0076] Specifically, the division module 102 is further used to: The last scene image data of the former subset and the first scene image data of the latter subset in two adjacent subsets are the same.

[0077] Specifically, the first calculation module 103 is further used to: The plurality of subsets are sorted according to time, and the complex coherence matrix of each pixel in each subset is calculated.

[0078] According to the complex coherence matrix of each pixel, based on the preset solution model, the target time series phase of each subset is obtained.

[0079] Specifically, the second calculation module 104 is further used to: sorting the target time series phases of multiple subsets according to time; Based on a preset recursive sequential adjustment model, the landslide deformation parameters of the last subset are recursively obtained according to the target time series phases of multiple subsets. In the recursive process, the target time series phases of all subsets before the target subset are taken as the first target time series phase, and the target time series phase of the target subset is taken as the second target time series phase. The landslide deformation parameters of the target subset are obtained according to the first target time series phase and the second target time series phase.

[0080] The size of the subset in the present invention is determined according to the time characteristics, which is highly flexible, thereby achieving near real-time data processing. The application of the recursive sequential adjustment model effectively avoids deviations and jumps between subsets, improves the accuracy of deformation time series analysis, and improves the efficiency of landslide disaster monitoring.

[0081] The specific definition of the landslide monitoring device based on recursive sequential estimation can be found in the above definition of the landslide monitoring method based on recursive sequential estimation, which will not be repeated here. Each module in the above-mentioned landslide monitoring device based on recursive sequential estimation can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0082] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external device end through a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the service side of a landslide monitoring method based on recursive sequential estimation.

[0083] In one embodiment, a computer device is provided. The computer device may be a device end, and its internal structure diagram may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, the functions or steps on the device side of a landslide monitoring method based on recursive sequential estimation are realized.

[0084] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquiring landslide monitoring data, and extracting time features from the landslide monitoring data; According to the time characteristics in the landslide monitoring data, based on a preset subset division rule, the landslide monitoring data is divided into a plurality of subsets; Based on a preset phase calculation model, the target time series phase of each subset is calculated; Based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset.

[0085] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquiring landslide monitoring data, and extracting time features from the landslide monitoring data; According to the time characteristics in the landslide monitoring data, based on a preset subset division rule, the landslide monitoring data is divided into a plurality of subsets; Based on a preset phase calculation model, the target time series phase of each subset is calculated; Based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset.

[0086] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the device side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0087] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0088] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0089] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A landslide monitoring method based on recursive sequential estimation, characterized in that: include: Acquiring landslide monitoring data, and extracting time features from the landslide monitoring data; According to the time characteristics in the landslide monitoring data, based on a preset subset division rule, the landslide monitoring data is divided into a plurality of subsets; Based on a preset phase calculation model, the target time series phase of each subset is calculated; Based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset.

2. The landslide monitoring method based on recursive sequential estimation according to claim 1 is characterized in that: The landslide monitoring data includes multi-view image data; The time characteristics include: a period for acquiring image data of one scene, and an acquisition time for each image data of one scene.

3. The landslide monitoring method based on recursive sequential estimation according to claim 1 is characterized in that: According to the time characteristics in the landslide monitoring data, based on the preset subset division rules, the landslide monitoring data is divided into multiple subsets, including: According to the acquisition time of each scene of image data in the time feature, based on a preset first preset condition, the landslide monitoring data is divided into a plurality of initial data sets; According to the period of acquiring a scene of image data in the time feature, each of the initial data sets is divided into a plurality of subsets based on a plurality of preset second preset conditions.

4. The landslide monitoring method based on recursive sequential estimation according to claim 3 is characterized in that: According to the period of acquiring a scene of image data in the time feature, each of the initial data sets is divided into a plurality of subsets based on a plurality of preset second preset conditions, including: When the period of acquiring a scene of image data satisfies the first second preset condition, the initial data set is divided into subsets according to the first time length; When the period of acquiring the image data of a scene satisfies the second second preset condition, the initial data set is divided into subsets according to the second time length; When the period of acquiring a scene of image data satisfies the third second preset condition, the initial data set is divided into subsets according to the third time length.

5. The landslide monitoring method based on recursive sequential estimation according to claim 1 is characterized in that: The last scene image data of the former subset and the first scene image data of the latter subset in two adjacent subsets are the same.

6. The landslide monitoring method based on recursive sequential estimation according to claim 1 is characterized in that: Based on the preset phase calculation model, the optimal time series phase of each subset is calculated, including: Sorting the plurality of subsets according to time, and calculating the complex coherence matrix of each pixel in each subset; According to the complex coherence matrix of each pixel, based on the preset solution model, the target time series phase of each subset is obtained.

7. The landslide monitoring method based on recursive sequential estimation according to claim 1 is characterized in that: Based on the preset recursive sequential adjustment model, the landslide deformation parameters are calculated according to the target time series phase of each subset, including: sorting the target time series phases of multiple subsets according to time; Based on a preset recursive sequential adjustment model, the landslide deformation parameters of the last subset are recursively obtained according to the target time series phases of multiple subsets. In the recursive process, the target time series phases of all subsets before the target subset are taken as the first target time series phase, and the target time series phase of the target subset is taken as the second target time series phase. The landslide deformation parameters of the target subset are obtained according to the first target time series phase and the second target time series phase.

8. A landslide monitoring device based on recursive sequential estimation, characterized in that: include: An acquisition module, used for acquiring landslide monitoring data and extracting time features in the landslide monitoring data; A partitioning module, configured to partition the landslide monitoring data into a plurality of subsets according to time features in the landslide monitoring data and based on a preset subset partitioning rule; A first calculation module, used for calculating the target time series phase of each subset based on a preset phase calculation model; The second calculation module is used to calculate the landslide deformation parameters according to the target time series phase of each subset based on a preset recursive sequential adjustment model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the landslide monitoring method based on recursive sequential estimation as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the landslide monitoring method based on recursive sequential estimation as claimed in any one of claims 1 to 7 are implemented.

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