Ground subsidence risk probability dynamic determination method, equipment and medium

Abstract

The invention provides a land subsidence risk probability dynamic determination method and device and a medium, and relates to the technical field of land subsidence risk probability determination, and the method comprises the steps: obtaining the corresponding area of each spatial attribute of each evaluation factor in a target region and the corresponding area of each land subsidence grade; determining a total land subsidence area corresponding to different land subsidence levels of each spatial attribute of each evaluation factor in the target area; determining the land subsidence risk probability corresponding to each evaluation factor under the land subsidence severity degrees of different land subsidence grades; determining the land subsidence risk probability of each land subsidence severity degree under each evaluation factor; determining the land subsidence risk probability of the target area; in the ground subsidence risk probability derivation process of the evaluation factors, whether the ground subsidence exists or not is considered, different grade division is carried out on the subsidence, the corresponding weight is set, and the determined ground subsidence risk probability is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of ground subsidence risk probability determination, and in particular to a method, device and medium for dynamically determining ground subsidence risk probability. Background Art

[0002] Overexploitation of groundwater resources in the process of urbanization has led to ground subsidence. Severe subsidence can damage urban infrastructure. Assessing ground subsidence risk is beneficial for disaster early warning. Traditional risk assessment methods mainly include expert experience method, hierarchical analysis method, mathematical statistics method, and machine learning method. Generally, weighted comprehensive calculation is performed through factor risk value and factor weight to determine risk zoning. The evaluation process of these methods is often qualitative and has a certain degree of ambiguity due to insufficient expert knowledge. The result is a deterministic risk zoning (high, medium, and low risk areas). The fuzzy weighted Bayesian model in the existing technology takes into account the ambiguity and uncertainty of the evaluation process. However, the derivation process of the ground subsidence risk probability of the evaluation factor only distinguishes whether subsidence occurs, and does not take into account the different severity of subsidence occurring in each evaluation factor, which leads to inaccurate determined ground subsidence risk probability. Summary of the Invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a method for dynamically determining land subsidence risk probability is provided, the method comprising the following steps:

[0005] S100, obtaining the area corresponding to each spatial attribute of each evaluation factor in the target area and the area corresponding to each ground subsidence level; wherein each evaluation factor corresponds to an evaluation factor weight, and each ground subsidence level corresponds to a ground subsidence level weight.

[0006] S200 , determining the total land subsidence area corresponding to different land subsidence levels occurring in the target area for each spatial attribute of each evaluation factor according to the land subsidence level weight.

[0007] S300, determining the ground subsidence risk probability corresponding to each evaluation factor under the ground subsidence severity of different ground subsidence levels based on the area corresponding to each spatial attribute within the target area and the total ground subsidence area corresponding to different ground subsidence levels occurring in the target area for each spatial attribute.

[0008] S400: Determine the probability of ground subsidence risk for each evaluation factor at each ground subsidence severity level based on the ground subsidence risk probability corresponding to each evaluation factor at different ground subsidence severity levels and the prior probability of ground subsidence risk for each evaluation factor.

[0009] S500 , determining the ground subsidence risk probability of the target area according to the ground subsidence risk probability of each ground subsidence severity under each evaluation factor and the evaluation factor weight corresponding to each evaluation factor.

[0010] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned method for dynamically determining the probability of ground subsidence risk.

[0011] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The method for dynamically determining the ground subsidence risk probability of the present invention divides the subsidence into grades according to the different degrees of subsidence occurring in the target area, sets different weights for different subsidence grades, and determines the total ground subsidence area corresponding to different ground subsidence grades occurring in the target area for each spatial attribute of each evaluation factor based on the ground subsidence grade weight; then the ground subsidence risk probability of the subsequent target area is determined; in the present invention, the derivation process of the ground subsidence risk probability of the evaluation factor not only considers whether the ground has settled, but also divides the subsidence into different grades and sets corresponding weights, highlighting the influence of the severity of the subsidence, that is, even if subsidence occurs, if the subsidence grade is small, the corresponding risk will not be very high, thereby making the determined ground subsidence risk probability more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A flow chart of a method for dynamically determining land subsidence risk probability provided by an embodiment of the present invention;

[0016] Figure 2 The overall flow chart of the EFWBM model ground subsidence risk probability derivation provided by the embodiment of the present invention;

[0017] Figure 3 A flow chart of conditional probability calculation in the EFWBM model provided in an embodiment of the present invention;

[0018] Figure 4 A schematic diagram of the dynamic EFWBM model framework provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] It should be noted that, based on this disclosure, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement such an apparatus and / or practice such a method.

[0021] The following will refer to Figure 1 The flowchart of the method for dynamically determining the probability of ground subsidence risk shown in the figure introduces a method for dynamically determining the probability of ground subsidence risk.

[0022] In this embodiment, it should be noted that there is a dynamic response process between groundwater resource changes and ground subsidence. The historical ground subsidence process has a certain impact on the current ground subsidence risk. Therefore, the ground subsidence risk has dynamic change characteristics and is affected by the historical ground subsidence process. Subsidence of different severity has different risks. In the risk assessment process, this dynamic nature, historical subsidence process and subsidence severity should be considered. The overall process of deriving the ground subsidence risk probability of the EFWBM model can be referred to Figure 2 The method for dynamically determining the land subsidence risk probability may include the following steps:

[0023] S100, obtaining the area corresponding to each spatial attribute of each evaluation factor in the target area and the area corresponding to each ground subsidence level; wherein each evaluation factor corresponds to an evaluation factor weight, and each ground subsidence level corresponds to a ground subsidence level weight.

[0024] In this example, the scenario targeted is ground subsidence caused by groundwater extraction. The ground subsidence risk caused by groundwater extraction is composed of three aspects: susceptibility, hazard, and vulnerability. Based on this, the evaluation factors are selected, including three primary indicators: susceptibility, hazard, and vulnerability. The combined performance of these three primary indicators reflects the ground subsidence risk. The susceptibility factor is related to the groundwater environment and geological conditions, the hazard factor is related to the degree of ground subsidence, and the vulnerability factor is related to the human production and living environment.

[0025] In this example, groundwater level changes, compressible layer thickness, and Quaternary thickness can be selected as secondary indicators of the susceptibility factor to reflect the ground subsidence caused by the combined effects of these factors.

[0026] The ground subsidence rate and cumulative ground subsidence can be selected as secondary indicators of the risk factor to quantify the harmfulness of the actual ground subsidence.

[0027] Building distribution, population density, GDP, and road distribution can be selected as secondary indicators of the vulnerability factor to reflect the risk of disasters to the city after ground subsidence occurs.

[0028] Furthermore, step S100 may include the following steps:

[0029] S110, gridding the target area.

[0030] S120, obtaining the number of grids corresponding to each spatial attribute of each evaluation factor and the number corresponding to each land subsidence level.

[0031] S130 , determining the area corresponding to each spatial attribute of each evaluation factor in the target area and the area corresponding to each land subsidence level based on the mapping relationship between the grid and the actual area.

[0032] In this embodiment, the target area is divided into grids, and the number of grids corresponding to the spatial attribute of each evaluation factor (such as "high / medium / low groundwater level change") is counted, and then mapped to the actual area (such as km 2 ); assign the value of each evaluation factor to the grid area where it is located to obtain the vector layer data of each evaluation factor; or obtain raster layer data with consistent resolution through spatial interpolation method; unify the spatial resolution to solve the problem of inconsistent scales of multi-source data; gridding facilitates computer automated processing and improves computing efficiency.

[0033] In this embodiment, each evaluation factor corresponds to an evaluation factor weight, and each ground subsidence level corresponds to a ground subsidence level weight; the evaluation factor weights and ground subsidence level weights can be obtained by analyzing a large amount of data; it should be noted that those skilled in the art can use the existing evaluation factor weights and ground subsidence level weight determination methods to determine the evaluation factor weights and ground subsidence level weights according to actual needs, which will not be elaborated here.

[0034] S200 , determining the total land subsidence area corresponding to different land subsidence levels occurring in the target area for each spatial attribute of each evaluation factor according to the land subsidence level weight.

[0035] In this embodiment, for each spatial attribute of each evaluation factor, the weighted total settlement area is calculated according to the settlement grade weight.

[0036] Furthermore, step S200 may include the following steps:

[0037] S210, obtaining the jth spatial attribute X of the i-th evaluation factor ij The area corresponding to the kth level of ground subsidence

[0038] In this embodiment, for example, in the area corresponding to the high spatial attribute of the groundwater level change evaluation factor, there will be an area corresponding to the kth level of ground subsidence. By counting this area, the area corresponding to the kth level of ground subsidence in the area corresponding to the spatial attribute can be obtained.

[0039] S220, according to Confirm X ij The total ground subsidence area corresponding to different ground subsidence levels in the target area Where T is the calculation time, h is the ground subsidence grade, w k is the weight of the kth ground subsidence level corresponding to the ground subsidence level; w k There is a positive correlation between the severity of land subsidence and the corresponding land subsidence grade.

[0040] In this embodiment, for example, if the groundwater over-exploitation area (evaluation factor) in a certain region has a mild subsidence area of ​​10 km 2 (weight 0.3), the weight is 5km 2 (weight 0.7), then the total weighted area = 10 × 0.3 + 5 × 0.7 = 6.5 km 2 .

[0041] The settlement areas of different levels are calculated by weight, that is, the more serious the settlement, the greater the weight, and the smaller the settlement, the smaller the weight. This highlights the impact of the severity of the settlement. That is, even if settlement occurs, if the settlement is small, the risk will not be very high. The difference in hazards of different settlement levels is reflected by weighting, avoiding the "homogenization" error of traditional methods.

[0042] S300, determining the ground subsidence risk probability corresponding to each evaluation factor under the ground subsidence severity of different ground subsidence levels based on the area corresponding to each spatial attribute within the target area and the total ground subsidence area corresponding to different ground subsidence levels occurring in the target area for each spatial attribute.

[0043] Furthermore, step S300 may include the following steps:

[0044] S310, get X ij The corresponding area N(X ij ).

[0045] S320, according to N(X ij ) and New Zealand T (X ij ), determine in w k The severity of land subsidence T k Next i-th evaluation factor Y i The corresponding ground subsidence risk probability P{Y i |T k}=NZ T (X ij ) / N(X ij ).

[0046] In this embodiment, according to the spatial attribute area N(X ij ) and weighted total settlement area NZT(X ij ), calculate the risk probability of each factor under a specific settlement level; the conditional probability calculation process in the EFWBM model is as follows Figure 3 shown.

[0047] For example: If the distribution area of ​​a geological type is N(X ij )=15km 2 , weighted total settlement area NZ T (X ij )=6.5km 2 , then the conditional probability is 6.5 / 15=0.43.

[0048] The traditional method only counts the subsidence area. In this embodiment, the level of hazard is distinguished by weight. For example, the contribution of the area of ​​severe subsidence to the risk is much higher than that of the same area of ​​mild subsidence. The spatial attribute area of ​​the evaluation factor is directly associated with the subsidence area, reflecting the "contribution rate of regional attributes to subsidence", thereby improving the accuracy of the calculation results.

[0049] S400: Determine the probability of ground subsidence risk for each evaluation factor at each ground subsidence severity level based on the ground subsidence risk probability corresponding to each evaluation factor at different ground subsidence severity levels and the prior probability of ground subsidence risk for each evaluation factor.

[0050] Furthermore, step S400 may include the following steps:

[0051] S410, obtain Y i Prior probability of land subsidence risk

[0052] S420, according to and P{Y i |T k}, confirm in Y i The next thing happened k The probability of land subsidence risk according to the severity of land subsidence Where j = 1, 2, ..., m; m is Y i The number of corresponding spatial attributes.

[0053] In this embodiment, combined with the prior probability and conditional probability P{Y i |T k}, derive the probability of a certain level of settlement occurring under a specific factor.

[0054] Prior probability Initial values ​​are assigned by experts or calculated from historical data (e.g., how often a certain evaluation factor has caused settlement in the past). Using Bayesian theorem to integrate new and old data, we continuously optimize probability accuracy and achieve dynamic probability correction.

[0055] S500 , determining the ground subsidence risk probability of the target area according to the ground subsidence risk probability of each ground subsidence severity under each evaluation factor and the evaluation factor weight corresponding to each evaluation factor.

[0056] Furthermore, step S500 may include the following steps:

[0057] S510, according to P(T k |Y i ) and Y i The corresponding evaluation factor weight h i, determine the ground subsidence risk probability of the target area

[0058] In this embodiment, the posterior probabilities of all evaluation factors and their weights are integrated to calculate the overall risk probability of the region; multi-factor collaborative analysis reflects the combined effects of multiple factors through a weighted product model to avoid errors dominated by a single factor.

[0059] Furthermore, after step S500, the method further includes the following steps:

[0060] S600: Construct a dynamic EFWBM model. The structure of the EFWBM model is a directed acyclic graph, in which the evaluation factor is the parent node and the ground subsidence risk is the child node.

[0061] S610, if T is the initial calculation time, then According to Y i It is obtained by the area proportion of each spatial attribute or the expert assignment.

[0062] S620, if T is not the initial calculation time, is the posterior probability corresponding to the last calculation moment.

[0063] In this embodiment, the dynamic EFWBM model construction process is as follows:

[0064] Since the occurrence process of ground subsidence is closely related to changes in groundwater levels, the impact of evaluation factors on ground subsidence risk also changes with changes in groundwater levels. The evaluation factors ground subsidence risk probability and ground subsidence risk both show dynamic change characteristics with changes in groundwater levels.

[0065] Therefore, this method proposes a dynamic risk probability derivation process to dynamically calculate the prior probability, conditional probability and posterior probability of the evaluation factor ground subsidence risk and the ground subsidence risk probability, such as Figure 4 shown.

[0066] When t>1 (i.e. the calculation time is not the initial time), the evaluation factor ground subsidence risk probability is the posterior probability of the previous time, and the evaluation factor ground subsidence risk conditional probability is calculated by the ground subsidence area at that time, thereby updating the ground subsidence risk probability at the current time. Specifically:

[0067] 1. The prior probability of the land subsidence risk evaluation factor has different assignment methods at different times, as shown in formula (1). This means that when there is a dynamic analysis over time, the prior probability of the land subsidence risk evaluation factor is the posterior probability at the previous moment.

[0068]

[0069] Among them, N(Y i ) is the area corresponding to the i-th evaluation factor; t=1 indicates that the calculation time is the initial calculation time, that is, the first calculation.

[0070] 2. The conditional probability of land subsidence risk of the evaluation factor will change at different times according to the current situation of land subsidence, as shown in formula (2). It means that at the tth moment (t>1), the spatial attribute X ij The area of ​​land subsidence occurring in the tth moment is obtained by weighted summing of the areas of land subsidence of different levels actually occurring at the tth moment.

[0071]

[0072] The area of ​​land subsidence occurring at time t is captured in real time using InSAR technology. This process, combined with real-time land subsidence, updates the land subsidence risk probability and the comprehensive land subsidence risk probability in the evaluation factor, more accurately reflecting changes in land subsidence risk following groundwater level changes.

[0073] The evaluation factor ground subsidence risk probability (i.e., the posterior probability of the parent node) changes with the changes in the prior probability and conditional probability.

[0074] 3. The probability of ground subsidence risk (child node) changes with the change of the probability of the evaluation factor ground subsidence risk (parent node), as shown in formula (3).

[0075]

[0076] In an exemplary embodiment, when the ground subsidence observation time scale t>1, taking the middle and upper part of the Chaobai River alluvial fan as an example, the observation time scale is 2003-2010 (t1), 2011-2014 (t2), and 2015-2017 (t3), the secondary indicators of the susceptibility factor are selected to derive the ground subsidence susceptibility probability, and the model accuracy is compared and verified. The ground subsidence rates of the three time ranges are obtained respectively, and the ground subsidence susceptibility probability at time t3 is obtained. The evaluation results are divided into three susceptibility levels: high, medium, and low. The changes in the ground subsidence rates at time t3 and time t2 are calculated, and the points where the subsidence rate increases are compared with the high susceptibility area, and the points where the subsidence rate decreases are compared with the low susceptibility area. The results are as follows: Figure 1 As shown:

[0077] Table 1

[0078]

[0079] Table 1 shows that the overall matching rate reaches 88%, indicating high model accuracy. Furthermore, combining the steps in the above embodiment, the unweighted land subsidence area obtained was used to derive the land subsidence susceptibility probability using the traditional FWBM method. Comparing the accuracy of the two models, the EFWBM model is more accurate in areas with reduced subsidence rates.

[0080] According to the different degrees of settlement in the target area, the settlement is divided into grades, and different weights are set for different settlement grades. Based on the ground settlement grade weights, the total ground settlement area corresponding to different ground settlement grades in the target area for each spatial attribute of each evaluation factor is determined; then the ground subsidence risk probability of the subsequent target area is determined; in the present invention, the ground subsidence risk probability derivation process of the evaluation factor not only considers whether the ground has settled, but also divides the settlement into different grades and sets corresponding weights, highlighting the influence of the severity of the settlement, that is, even if settlement occurs, if the settlement grade is small, the corresponding risk will not be very high, thereby making the determined ground subsidence risk probability more accurate.

[0081] Compared with the prior art, the method in this embodiment has at least the following beneficial effects:

[0082] (1) The dynamic EFWBM model in this embodiment calculates the risk probability of the evaluation factor in combination with the different severity levels of ground subsidence. Compared with the traditional FWBM model that only distinguishes whether subsidence occurs, the calculation process is more scientific and accurate.

[0083] (2) Combining the severity of ground subsidence at different times to deduce the risk probability at different times is more in line with the law of mutual influence between the ground subsidence process and the evaluation factors, making the evaluation results more consistent with the real-time ground subsidence occurrence and development mechanism, and can provide real-time auxiliary decision support for ground subsidence early warning and prevention and control.

[0084] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0085] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0086] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0087] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0088] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0089] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0090] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0091] The electronic device is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0092] The electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, the aforementioned at least one processor, the aforementioned at least one memory, and a bus connecting different system components (including the memory and the processor).

[0093] The memory stores program codes, which can be executed by the processor, so that the processor performs the steps of various embodiments described in this specification.

[0094] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0095] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0096] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0097] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0098] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0099] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0100] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for dynamically determining land subsidence risk probability, characterized in that: The method comprises the following steps: S100, obtaining the area corresponding to each spatial attribute of each evaluation factor within the target area and the area corresponding to each land subsidence level; wherein each evaluation factor corresponds to an evaluation factor weight, and each land subsidence level corresponds to a land subsidence level weight; S200, determining the total land subsidence area corresponding to different land subsidence levels occurring in the target area for each spatial attribute of each evaluation factor according to the land subsidence level weight; S300, determining the land subsidence risk probability corresponding to each evaluation factor at different land subsidence severity levels based on the area corresponding to each spatial attribute within the target area and the total land subsidence area corresponding to different land subsidence levels within the target area for each spatial attribute; S400, determining the probability of land subsidence risk for each evaluation factor at each land subsidence severity level under each evaluation factor based on the land subsidence risk probability corresponding to each evaluation factor at different land subsidence severity levels and the prior probability of land subsidence risk for each evaluation factor; S500 , determining the ground subsidence risk probability of the target area according to the ground subsidence risk probability of each ground subsidence severity under each evaluation factor and the evaluation factor weight corresponding to each evaluation factor.

2. The method for dynamically determining land subsidence risk probability according to claim 1, characterized in that: Step S100 includes the following steps: S110, gridding the target area; S120, obtaining the number of grids corresponding to each spatial attribute of each evaluation factor and the number corresponding to each land subsidence level; S130 , determining the area corresponding to each spatial attribute of each evaluation factor in the target area and the area corresponding to each land subsidence level based on the mapping relationship between the grid and the actual area.

3. The method for dynamically determining land subsidence risk probability according to claim 1, characterized in that: Step S200 includes the following steps: S210, obtaining the jth spatial attribute X of the i-th evaluation factor ij The area corresponding to the kth level of ground subsidence S220, according to Confirm X ij The total ground subsidence area corresponding to different ground subsidence levels in the target area Where T is the calculation time, h is the ground subsidence grade, w k is the weight of the kth ground subsidence level corresponding to the ground subsidence level; w k There is a positive correlation between the severity of land subsidence and the corresponding land subsidence grade.

4. The method for dynamically determining land subsidence risk probability according to claim 3, characterized in that: Step S300 includes the following steps: S310, get X ij The corresponding area N(X ij ); S320, according to N(X ij ) and New Zealand T (X ij ), determine in w k The severity of land subsidence T k Next i-th evaluation factor Y i The corresponding ground subsidence risk probability P{Y i |T k }=NZ T (X ij ) / N(X ij ).

5. The method for dynamically determining land subsidence risk probability according to claim 4, characterized in that: Step S400 includes the following steps: S410, obtain Y i Prior probability of land subsidence risk S420, according to and P{Y i |T k }, confirm in Y i The next thing happened k The probability of land subsidence risk according to the severity of land subsidence Where j = 1, 2, ..., m; m is Y i The number of corresponding spatial attributes.

6. The method for dynamically determining land subsidence risk probability according to claim 5, characterized in that: Step S500 includes the following steps: S510, according to P(T k |Y i ) and Y i The corresponding evaluation factor weight h i , determine the ground subsidence risk probability of the target area 7. The method for dynamically determining land subsidence risk probability according to claim 6, characterized in that: After step S500, the method further includes the following steps: S600: construct a dynamic EFWBM model; wherein the structure of the EFWBM model is a directed acyclic graph, in which the evaluation factor is the parent node and the ground subsidence risk is the child node; S610, if T is the initial calculation time, then According to Y i The area proportion of each spatial attribute or the expert assignment is obtained; S620, if T is not the initial calculation time, is the posterior probability corresponding to the last calculation moment.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program segment is loaded and executed by the processor to implement the method for dynamically determining the ground subsidence risk probability as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: The device comprises a processor and the non-transitory computer-readable storage medium of claim 8.

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