A landslide early warning method and device considering comprehensive slope monitoring data

Abstract

This invention discloses a landslide early warning method and device considering full-area slope monitoring data, relating to the field of landslide early warning research. The method includes the following steps: constructing a monitoring point network based on the spatial distribution of slope monitoring points; identifying the deformation velocity thresholds at the generation and disappearance of each component in the monitoring point network, and constructing a corresponding persistence graph, where each component is a locally maximally connected subset formed by connecting several nodes in the monitoring point network through edges; calculating the distance between adjacent persistence graphs, quantifying the spatiotemporal evolution rate of the slope deformation field based on the distance, and determining a curve of the spatiotemporal evolution rate; determining whether the curve has a transition point, and issuing an early warning signal based on the determination result. The method provided by this invention can process massive monitoring data covering the entire slope area, providing a novel and effective approach for quantifying the spatiotemporal evolution behavior of the slope deformation field and revealing early warning signals of natural landslides.

Description

Technical Field

[0001] This application relates to the field of landslide early warning research, specifically to a landslide early warning method and device that considers monitoring data of the entire slope area. Background Technology

[0002] Landslides refer to the sudden instability of loose slope rock and soil masses under the influence of rainfall, earthquakes, and their own gravity. Under the combined action of gravity and external forces, the slope rock and soil mass undergoes internal micro-slippage and fracturing processes to develop and connect potential sliding surfaces, ultimately leading to instability and failure due to external macroscopic deformation. For a long time, real-time monitoring and time-series analysis of dangerous slopes, and issuing warning signals based on changes in monitoring indicators, have been crucial for landslide early warning. In recent years, the rapid development of monitoring technologies such as optical remote sensing and interferometric synthetic aperture radar (InSAR) has revolutionized landslide research. Their high-precision, wide-range deformation monitoring capabilities, as well as non-contact and multi-source data fusion characteristics, enable researchers to accurately capture slope deformation, track its evolution, and provide precise data for early warning systems, significantly improving the efficiency and reliability of landslide monitoring and research.

[0003] However, current landslide early warning technologies can only analyze displacement time-series data at a single monitoring point, making them highly susceptible to monitoring errors and prone to false alarms. They cannot utilize monitoring data covering the entire slope area, nor can they quantitatively characterize the spatiotemporal evolution of the slope deformation field.

[0004] Therefore, it is necessary to design a landslide early warning method that takes into account the monitoring data of the entire slope area in order to overcome the above problems. Summary of the Invention

[0005] This application provides a landslide early warning method, device, equipment, and computer-readable storage medium that considers monitoring data of the entire slope area. It can solve the problems in related technologies where landslide early warning can only analyze displacement time series data at a single monitoring point, is easily affected by monitoring errors and may result in false alarms, cannot utilize monitoring data covering the entire slope area, and cannot quantitatively characterize the spatiotemporal evolution behavior of the slope deformation field.

[0006] In a first aspect, embodiments of this application provide a landslide early warning method that considers comprehensive slope monitoring data, including:

[0007] Based on the spatial distribution of slope monitoring points, a monitoring point network is constructed;

[0008] Identify the deformation velocity threshold at the time of generation and destruction of each component in the monitoring point network, and construct the corresponding continuous graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges.

[0009] Calculate the distance between adjacent continuous plots, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate.

[0010] Determine whether the curve has a turning point, and issue an early warning signal based on the determination result.

[0011] In some embodiments, constructing a monitoring point network based on the spatial distribution of slope monitoring points includes:

[0012] Obtain monitoring data covering the entire slope area;

[0013] Each monitoring point is treated as a node in the network;

[0014] Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

[0015] In some embodiments, identifying the deformation velocity thresholds at the generation and disappearance of each component in the monitoring point network and constructing the corresponding persistence graph includes:

[0016] The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold.

[0017] Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network;

[0018] The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network.

[0019] The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data.

[0020] In some embodiments, calculating the distance between adjacent continuous maps, quantifying the spatiotemporal evolution rate of the slope deformation field based on the distance, and determining the curve of the spatiotemporal evolution rate includes:

[0021] According to the formula:

[0022] d(p,φ(p))=max(|η birth,p-η birth , φ(p) |,|η death,p -η death , φ(p) |)

[0023] Determine the distance d(p,φ(p)) between point p and point φ(p), where η birth,p and η death,p φ(p) represents the deformation rate thresholds when the component corresponding to point p appears and disappears, respectively, and φ(p) represents the corresponding mapping point of point p in another continuous graph PD′ through mapping φ.

[0024] According to the formula:

[0025]

[0026] Determine the Wasserstein distance increment WD of the slope deformation field at the current moment. Δt,t , where PD and PD′ are two adjacent component persistence graphs; p indicates traversing all points in the persistence graph PD; Δt represents the time interval between the two persistence graphs, in days.

[0027] According to the formula:

[0028] WD t =WD t-1 +WD Δt,t (PD,PD′)

[0029] Get the total Wortherstein distance from WD at the current moment. t WD t-1 WD t Let represent the total Wasserstein distances obtained at times t and t-1, respectively;

[0030] Based on the Wasserstein distance, the spatiotemporal evolution rate of the slope deformation field is quantified, and a curve of the spatiotemporal evolution rate is determined.

[0031] In some embodiments, determining whether the curve has a turning point and issuing a warning signal based on the determination result includes:

[0032] The Bernaola-Galvan algorithm was used to detect the curve of the spatiotemporal evolution rate.

[0033] When the curve of the spatiotemporal evolution rate shows a turning point, an early warning signal is issued;

[0034] No warning signal is issued when the curve of the spatiotemporal evolution rate does not show any turning points.

[0035] Secondly, embodiments of this application provide a landslide early warning device that considers comprehensive slope monitoring data, the device comprising:

[0036] The construction unit is used to construct a monitoring point network based on the spatial distribution of slope monitoring points. The construction unit is also used to identify the deformation rate threshold when each component in the monitoring point network is generated and destroyed, and to construct the corresponding persistence graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges.

[0037] The calculation unit is used to calculate the distance between adjacent continuous graphs, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate.

[0038] The judgment unit is used to determine whether there is a turning point in the curve and to issue a warning signal based on the judgment result.

[0039] In some embodiments, the building unit is used for:

[0040] Obtain monitoring data covering the entire slope area;

[0041] Each monitoring point is treated as a node in the network;

[0042] Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

[0043] In some embodiments, the building unit is further configured to:

[0044] The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold.

[0045] Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network;

[0046] The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network.

[0047] The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data.

[0048] Thirdly, a computer device is provided, comprising: a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the method described in any one of the first aspects.

[0049] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed by a processor, it implements the method described in any one of the first aspects.

[0050] The beneficial effects of the technical solutions provided in this application include:

[0051] By constructing a monitoring point network based on the spatial distribution of slope monitoring points, identifying the deformation rate thresholds at the generation and disappearance of each component in the monitoring point network, and constructing a corresponding persistence graph, wherein the component is a locally maximally connected subset formed by connecting several nodes in the monitoring point network through edges, calculating the distance between adjacent persistence graphs, quantifying the spatiotemporal evolution rate of the slope deformation field based on the distance, and determining the curve of the spatiotemporal evolution rate, determining whether there are transition points in the curve, and issuing early warning signals based on the determination results, this method can process massive monitoring data covering the entire slope area, providing a new and effective approach for quantifying the spatiotemporal evolution behavior of the slope deformation field and revealing early warning signals of natural landslides. Attached Figure Description

[0052] Figure 1 This is a schematic flowchart of a landslide early warning method that considers full-area slope monitoring data in an embodiment of the present invention;

[0053] Figure 2 The landslide monitoring data of Xinmo Village obtained based on InSAR monitoring in this embodiment of the invention;

[0054] Figure 3 This is a schematic diagram illustrating the construction of the monitoring point network in an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram of the spatial filtering process in an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of the spatiotemporal evolution rate of the slope deformation field in an embodiment of the present invention;

[0057] Figure 6 This is a schematic diagram illustrating the determination of the existence of a transition point in an embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of a landslide early warning device that considers full-area slope monitoring data in an embodiment of the present invention;

[0059] Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0061] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0062] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0063] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0064] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0066] In one aspect, embodiments of this application provide a landslide early warning method that takes into account the monitoring data of the entire slope area.

[0067] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the landslide early warning method that considers comprehensive slope monitoring data for this application. Figure 1 As shown, the method includes:

[0068] S1. Construct a monitoring point network based on the spatial distribution of slope monitoring points;

[0069] It is worth noting that before step S1, monitoring data covering the entire slope area is acquired using monitoring technologies such as optical remote sensing or interferometric synthetic aperture radar (InSAR), which can yield data such as... Figure 2 The image shows typical slope monitoring data, where each point represents a monitoring point, and the color depth represents the total displacement during the entire monitoring period.

[0070] Each monitoring point is treated as a node in the network, where the node attribute represents the deformation rate of the corresponding monitoring point at the current moment. Other monitoring points within a certain range around each monitoring point are considered its neighboring nodes, and an edge is added between each node and its neighboring nodes. Based on this, the following can be used: Figure 3 The original discrete distribution of monitoring points is transformed into a monitoring point network.

[0071] S2. Identify the deformation velocity threshold when each component in the monitoring point network is generated and destroyed, and construct the corresponding continuous graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges.

[0072] Specifically, firstly, the maximum deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold. Then, nodes and edges with deformation rates greater than this threshold are selected to construct a monitoring point sub-network. Finally, the structure of each component is extracted from this monitoring point sub-network. Here, a component refers to a locally maximally connected subset formed by connecting several nodes through edges. Any pair of nodes within the same component can reach each other through a specific path, while nodes between different components cannot reach each other.

[0073] Secondly, continuously decrease the aforementioned deformation rate threshold and repeat the above operation. For example... Figure 4As shown, during this process, it can be observed that each component appears or disappears as the deformation rate threshold decreases. The appearance of a component is defined as the appearance of its first node, while the disappearance of a component is defined as its merging with another previously appearing component. The deformation rate threshold corresponding to the appearance or disappearance of a component is also recorded.

[0074] Finally, based on the deformation rate threshold corresponding to the appearance or disappearance of each component, a continuous network graph of monitoring points is constructed, with the deformation rate threshold at the appearance of the component as the Y-axis data and the deformation rate threshold at the disappearance of the component as the X-axis data. For example... Figure 5 As shown, each point in the persistence graph represents a deformation rate threshold corresponding to the appearance and disappearance of a component. Based on this, spatial dimensionality reduction of the monitoring point network can be achieved while preserving the essential topological features.

[0075] S3. Calculate the distance between adjacent continuous plots, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate;

[0076] During the slope monitoring period, a persistent graph of the aforementioned monitoring point network was obtained on each monitoring date. This persistent graph contains topological information about the appearance and disappearance of each component structure, representing the essential attributes of the monitoring point network. Therefore, quantifying the distance between two adjacent persistent graphs of the monitoring point network is equivalent to quantifying the spatiotemporal evolution rate of the slope deformation field at the corresponding time. Specifically, the Wasserstein distance is used to quantify the difference between two adjacent persistent graphs.

[0077] S4. Determine whether there is a turning point in the curve, and issue a warning signal based on the determination result.

[0078] In some embodiments, constructing a monitoring point network based on the spatial distribution of slope monitoring points includes:

[0079] Obtain monitoring data covering the entire slope area;

[0080] Each monitoring point is treated as a node in the network;

[0081] Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

[0082] In some embodiments, identifying the deformation velocity thresholds at the generation and disappearance of each component in the monitoring point network and constructing the corresponding persistence graph includes:

[0083] The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold.

[0084] Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network;

[0085] The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network.

[0086] The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data.

[0087] In some embodiments, calculating the distance between adjacent continuous maps, quantifying the spatiotemporal evolution rate of the slope deformation field based on the distance, and determining the curve of the spatiotemporal evolution rate includes:

[0088] According to the formula:

[0089] d(p,φ(p))=max(|η birth,p -η birth , φ(p) |,|η death,p -η death , φ(p) |)

[0090] Determine the distance d(p,φ(p)) between point p and point φ(p), where η birth,p and η death,p φ(p) represents the deformation rate thresholds when the component corresponding to point p appears and disappears, respectively, and φ(p) represents the corresponding mapping point of point p in another continuous graph PD′ through mapping φ.

[0091] Since there are many different choices of mapping φ, the final Wasserstein distance is the minimum distance obtained under all possible mappings φ.

[0092] According to the formula:

[0093]

[0094] Determine the Wasserstein distance increment WD of the slope deformation field at the current moment. Δt,t , where PD and PD′ are two adjacent component persistence graphs; p indicates traversing all points in the persistence graph PD; Δt represents the time interval between the two persistence graphs, in days.

[0095] According to the formula:

[0096] WD t =WD t-1 +WD Δt,t(PD,PD′)

[0097] Get the total Wortherstein distance from WD at the current moment. t WD t-1 WD t Let represent the total Wasserstein distances obtained at times t and t-1, respectively;

[0098] Based on the Wasserstein distance, the spatiotemporal evolution rate of the slope deformation field is quantified, and a curve of the spatiotemporal evolution rate is determined.

[0099] In some embodiments, determining whether the curve has a turning point and issuing a warning signal based on the determination result includes:

[0100] The Bernaola-Galvan algorithm was used to detect the curve of the spatiotemporal evolution rate.

[0101] When the curve of the spatiotemporal evolution rate shows a turning point, an early warning signal is issued;

[0102] No warning signal is issued when the curve of the spatiotemporal evolution rate does not show any turning points.

[0103] Specifically, based on the aforementioned spatiotemporal evolution rate curve of the slope deformation field, the Bernaola-Galvan algorithm is used to detect whether there are transition points. The presence of transition points indicates that the slope deformation field is undergoing an accelerated evolution process, suggesting that the current slope is in an unstable state, and a landslide warning signal can be issued; conversely, the absence of transition points indicates that the slope deformation field is in a relatively stable evolutionary state, with no risk of catastrophic instability, and no warning is needed. Figure 6 As indicated by the arrow, after using the Bernaola-Galvan algorithm to detect the spatiotemporal evolution rate curve of the aforementioned deformation field, a turning point was found in the evolution curve. Compared with the actual landslide data used in this invention, this turning point provided an early warning signal of an impending landslide 131 days before the landslide occurred, proving that this invention has good implementation effect and can indeed achieve early warning of landslides while considering the monitoring data of the entire slope area.

[0104] By constructing a monitoring point network based on the spatial distribution of slope monitoring points, identifying the deformation rate thresholds at the generation and disappearance of each component in the monitoring point network, and constructing a corresponding persistence graph, wherein the component is a locally maximally connected subset formed by connecting several nodes in the monitoring point network through edges, calculating the distance between adjacent persistence graphs, quantifying the spatiotemporal evolution rate of the slope deformation field based on the distance, and determining the curve of the spatiotemporal evolution rate, determining whether there are transition points in the curve, and issuing early warning signals based on the determination results, this method can process massive monitoring data covering the entire slope area, providing a new and effective approach for quantifying the spatiotemporal evolution behavior of the slope deformation field and revealing early warning signals of natural landslides.

[0105] Secondly, embodiments of this application also provide a landslide device that takes into account the monitoring data of the entire slope area.

[0106] In one embodiment, reference is made to Figure 7 , Figure 7 This is a functional module diagram of an embodiment of a landslide device that takes into account the monitoring data of the entire slope area in this application. (See diagram below.) Figure 7 As shown, the device includes:

[0107] The construction unit is used to construct a monitoring point network based on the spatial distribution of slope monitoring points. The construction unit is also used to identify the deformation rate threshold when each component in the monitoring point network is generated and destroyed, and to construct the corresponding persistence graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges.

[0108] The calculation unit is used to calculate the distance between adjacent continuous graphs, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate.

[0109] The judgment unit is used to determine whether there is a turning point in the curve and to issue a warning signal based on the judgment result.

[0110] In some embodiments, the building unit is used for:

[0111] Obtain monitoring data covering the entire slope area;

[0112] Each monitoring point is treated as a node in the network;

[0113] Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

[0114] In some embodiments, the building unit is further configured to:

[0115] The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold.

[0116] Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network;

[0117] The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network.

[0118] The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data.

[0119] In some embodiments, the computing unit is used for:

[0120] According to the formula:

[0121] d(p,φ(p))=max(|η birth,p -η birth , φ(p) |,|η death,p -η death , φ(p) |)

[0122] Determine the distance d(p,φ(p)) between point p and point φ(p), where η birth,p and η death,p φ(p) represents the deformation rate thresholds when the component corresponding to point p appears and disappears, respectively, and φ(p) represents the corresponding mapping point of point p in another continuous graph PD′ through mapping φ.

[0123] According to the formula:

[0124]

[0125] Determine the Wasserstein distance increment WD of the slope deformation field at the current moment. Δt,t , where PD and PD′ are two adjacent component persistence graphs; p indicates traversing all points in the persistence graph PD; Δt represents the time interval between the two persistence graphs, in days.

[0126] According to the formula:

[0127] WD t =WD t-1 +WD Δt,t (PD,PD′)

[0128] Get the total Wortherstein distance from WD at the current moment. t WD t-1 WDt Let represent the total Wasserstein distances obtained at times t and t-1, respectively;

[0129] Based on the Wasserstein distance, the spatiotemporal evolution rate of the slope deformation field is quantified, and a curve of the spatiotemporal evolution rate is determined.

[0130] In some embodiments, the determining unit is used for:

[0131] The Bernaola-Galvan algorithm was used to detect the curve of the spatiotemporal evolution rate.

[0132] When the curve of the spatiotemporal evolution rate shows a turning point, an early warning signal is issued;

[0133] No warning signal is issued when the curve of the spatiotemporal evolution rate does not show any turning points.

[0134] The functions of each module in the landslide device that considers the whole-area slope monitoring data correspond to the steps in the landslide method embodiment that considers the whole-area slope monitoring data. Their functions and implementation processes will not be described in detail here.

[0135] Thirdly, this application provides a landslide device that considers full-area slope monitoring data. The landslide device that considers full-area slope monitoring data can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0136] Reference Figure 8 , Figure 8 This is a schematic diagram of the hardware structure of a landslide device that considers full-area slope monitoring data, as described in an embodiment of this application. In this embodiment, the landslide device considering full-area slope monitoring data may include a processor, a memory, a communication interface, and a communication bus.

[0137] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0138] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces. These interfaces enable interconnection of internal components within the landslide monitoring equipment that incorporates comprehensive slope monitoring data, as well as interfaces for interconnection between the landslide monitoring equipment and other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0139] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0140] The processor can be a general-purpose processor, which can call a landslide program that considers the full-area slope monitoring data stored in the memory and execute the landslide method considering the full-area slope monitoring data provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the landslide program considering the full-area slope monitoring data is called can be referred to the various embodiments of the landslide method considering the full-area slope monitoring data of this application, and will not be repeated here.

[0141] Those skilled in the art will understand that Figure 8 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0142] Fourthly, embodiments of this application also provide a readable storage medium.

[0143] This application has a readable storage medium storing a landslide program that takes into account slope monitoring data, wherein when the landslide program that takes into account slope monitoring data is executed by a processor, it implements the steps of the landslide method that takes into account slope monitoring data as described above.

[0144] The method implemented when the landslide procedure considering the whole slope monitoring data is executed can be referred to in the various embodiments of the landslide method considering the whole slope monitoring data in this application, and will not be repeated here.

[0145] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0146] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0147] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A landslide early warning method considering comprehensive slope monitoring data, characterized in that, The method includes: Based on the spatial distribution of slope monitoring points, a monitoring point network is constructed; Identify the deformation velocity threshold when each component in the monitoring point network is generated and destroyed, and construct the corresponding continuous graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges. Calculate the distance between adjacent continuous plots, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate. Determine whether the curve graph has a turning point, and issue an early warning signal based on the determination result; The process of identifying the deformation velocity threshold at which each component in the monitoring point network is generated and destroyed, and constructing the corresponding persistence graph, includes: The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold. Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network; The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network. The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data. The calculation of the distance between adjacent continuous maps, the quantification of the spatiotemporal evolution rate of the slope deformation field based on the distance, and the determination of the spatiotemporal evolution rate curve include: According to the formula: Determine point p and Distance between points ,in and These represent the deformation rate thresholds at the appearance and disappearance of the component corresponding to point p, respectively. Represents point p through mapping In another continuous graph The corresponding mapping point in; According to the formula: Determine the Wasserstein distance increment of the slope deformation field at the current moment. ,in , These are the continuation graphs of two adjacent components; p indicates the continuation graph... Iterate through all the points in the array. Indicates the time interval between two continuum plots, in days; According to the formula: Get the total Wortherstein distance from WD at the current moment. t ,in , They represent and The total Wasserstein distance obtained at time t; Based on the Wasserstein distance, the spatiotemporal evolution rate of the slope deformation field is quantified, and a curve of the spatiotemporal evolution rate is determined.

2. The landslide early warning method considering full-area slope monitoring data as described in claim 1, characterized in that, The construction of a monitoring point network based on the spatial distribution of slope monitoring points includes: Obtain monitoring data covering the entire slope area; Each monitoring point is treated as a node in the network; Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

3. A landslide early warning method considering full-area slope monitoring data as described in claim 1, characterized in that, The step of determining whether the curve graph has a turning point and issuing a warning signal based on the determination result includes: The Bernaola-Galvan algorithm was used to detect the curve of the spatiotemporal evolution rate. When the curve of the spatiotemporal evolution rate shows a turning point, an early warning signal is issued; No warning signal is issued when the curve of the spatiotemporal evolution rate does not show any turning points.

4. A landslide early warning device that considers comprehensive slope monitoring data, characterized in that, The device includes: The construction unit is used to construct a monitoring point network based on the spatial distribution of slope monitoring points. The construction unit is also used to identify the deformation rate threshold when each component in the monitoring point network is generated and destroyed, and to construct the corresponding persistence graph. The component is a local maximum connected subset formed by connecting several nodes in the monitoring point network through edges. The process of identifying the deformation velocity threshold at which each component in the monitoring point network is generated and destroyed, and constructing the corresponding persistence graph, includes: The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold. Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network; The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network. The deformation rate threshold corresponding to the appearance or disappearance of each component is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data. The calculation unit is used to calculate the distance between adjacent continuous plots, quantify the spatiotemporal evolution rate of the slope deformation field based on the distance, and determine the curve of the spatiotemporal evolution rate. The calculation of the distance between adjacent continuous maps, the quantification of the spatiotemporal evolution rate of the slope deformation field based on the distance, and the determination of the spatiotemporal evolution rate curve include: According to the formula: Determine point p and Distance between points ,in and These represent the deformation rate thresholds at the appearance and disappearance of the component corresponding to point p, respectively. Represents point p through mapping In another continuous graph The corresponding mapping point in; According to the formula: Determine the Wasserstein distance increment of the slope deformation field at the current moment. ,in , These are the continuation graphs of two adjacent components; p indicates the continuation graph... Iterate through all the points in the array. Indicates the time interval between two continuum plots, in days; According to the formula: Get the total Wortherstein distance from WD at the current moment. t ,in , They represent and The total Wasserstein distance obtained at time t; Based on the Wasserstein distance, the spatiotemporal evolution rate of the slope deformation field is quantified, and a curve of the spatiotemporal evolution rate is determined. The judgment unit is used to determine whether there is a turning point in the curve and to issue a warning signal based on the judgment result.

5. A landslide early warning device considering full-area slope monitoring data as described in claim 4, characterized in that, The building unit is used for: Obtain monitoring data covering the entire slope area; Each monitoring point is treated as a node in the network; Each monitoring point is considered as a neighboring node within a certain range around it. An edge is added between each node and its neighboring nodes to complete the construction of the monitoring point network.

6. A landslide early warning device considering full-area slope monitoring data as described in claim 5, characterized in that, The building unit is also used for: The maximum value of the deformation rate at all monitoring points in the monitoring point network is selected as the deformation rate threshold. Select nodes and edges in the monitoring point network whose deformation rate is greater than the threshold to construct a monitoring point subnetwork, and extract the structure of each component from the monitoring point network; The deformation rate threshold is continuously reduced, and nodes and edges in the monitoring point network with deformation rates greater than the threshold are repeatedly selected to construct a monitoring point sub-network, and the structure of each component is extracted from the monitoring point sub-network. For each component that appears or disappears, a deformation rate threshold is recorded, and a continuous network graph of monitoring points is constructed with the deformation rate threshold when the component appears as the Y-axis data and the deformation rate threshold when it disappears as the X-axis data.

7. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the computer program, when executed by the processor, performs the steps as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 3.

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