An automatic early warning method and system for remote sensing monitoring of geological disasters

Abstract

The present application provides an automated early warning method and system for remote sensing monitoring of geological disasters. When determining the important analysis strategy for the target geological disaster monitoring attributes, the geological disaster monitoring information of the target geological disaster monitoring attributes in the first geological disaster risk stage can be matched with the important analysis strategy triggered by the first geological disaster type zoning to determine the results of the important analysis strategy for the target geological disaster monitoring attributes. By comparing the matching metric values ​​of the collected geological disaster monitoring information and type constraints with the key early warning content, the technical effect of determining the important analysis strategy for the target geological disaster monitoring attributes can also achieve the technical effect of improving the accuracy and reliability of geological disaster risk early warnings.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an automated early warning method and system for remote sensing monitoring of geological disasters. Background Art

[0002] With the continuous development of big data, big data is bound to gradually become the core asset and main competitiveness of the company. The company's business development and product evolution are also gradually moving towards how to make good use of the value of data. At present, the combination of big data and various industries is becoming more and more close. For example, big data + user mining, big data + online office, big data + smart medical care and big data + geological disasters. However, when applying big data to geological disaster early warning technology, there may be problems of confused early warning and inaccurate early warning, making it difficult to ensure the reliability and accuracy of geological disaster early warning. Summary of the invention

[0003] In view of this, the present application provides an automated early warning method and system for remote sensing monitoring of geological disasters.

[0004] In a first aspect, an automatic early warning method for geological disasters monitored by remote sensing is provided, comprising:

[0005] Obtaining first geological disaster monitoring information of a target geological disaster monitoring attribute and a first risk hazard that triggers the first geological disaster monitoring information;

[0006] Based on the first risk hazard, a first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes in a first geological disaster risk stage is obtained, wherein the first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes is geological disaster monitoring information triggered by a first geological disaster type partition, and the first risk hazard matches a second risk hazard of the first geological disaster type partition;

[0007] On the premise that there is first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute;

[0008] The important analysis strategies of the target geological disaster monitoring attributes are added to the analysis strategy library of the target geological disaster monitoring attributes so that the corresponding target analysis strategies can be selected to carry out early warning of the corresponding geological disaster risks.

[0009] In the present application, the first geological disaster monitoring information of the target geological disaster monitoring attribute is obtained, including:

[0010] Obtaining X geological disaster monitoring information of the target geological disaster monitoring attribute in the second geological disaster risk stage, the continuous maximum constraint value of the first geological disaster risk stage is greater than the continuous maximum constraint value of the second geological disaster risk stage;

[0011] Determining classification key data based on a set of geological disaster monitoring information of the target geological disaster monitoring attributes in the third geological disaster risk stage, wherein the third geological disaster risk stage is local information of the second geological disaster risk stage;

[0012] Determine a matching metric value between each geological hazard monitoring information in the X geological hazard monitoring information and the classification key data;

[0013] The geological disaster monitoring information corresponding to the maximum matching metric value is determined as the first geological disaster monitoring information.

[0014] In the present application, the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes has first target geological disaster monitoring information related to the first geological disaster monitoring information, including:

[0015] Determine a matching metric value between the first geological disaster monitoring information and each geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes;

[0016] On the premise that there is second geological disaster monitoring information whose matching metric value with the first geological disaster monitoring information is greater than the target metric value in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the second geological disaster monitoring information is determined as the first target geological disaster monitoring information.

[0017] In this application, we include:

[0018] Obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute includes:

[0019] Obtaining derived risk data of the target geological disaster monitoring attribute, wherein the first geological disaster monitoring information includes the derived geological disaster monitoring information;

[0020] The geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage obtained based on the first risk hazard includes:

[0021] Based on the first risk hazard, derived geological disaster monitoring information of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage is obtained, wherein the geological disaster monitoring information queue includes the derived geological disaster monitoring information.

[0022] In this application, we include:

[0023] Under the premise that the first cluster of geological disaster monitoring attribute geological disaster monitoring information queue does not have the first target geological disaster monitoring information related to the first geological disaster monitoring information, a second cluster of geological disaster monitoring attribute geological disaster monitoring information queue triggered by the second geological disaster type partition in the target partition segment is obtained, wherein the target partition segment is a partition segment with the first geological disaster type partition as the center point and a preset number of partitions as upper and lower segments;

[0024] On the premise that there is second target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the second target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute.

[0025] In the present application, before determining the important analysis strategy corresponding to the second target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, the method further includes:

[0026] The second geological disaster type partition matches the second important analysis strategy queue corresponding to the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the second cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the second important analysis strategy queue.

[0027] In the present application, before determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, the method further includes:

[0028] The first geological disaster type partition matches the first important analysis strategy queue corresponding to the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the first cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the first important analysis strategy queue.

[0029] In a second aspect, an automated early warning system for remote sensing monitoring of geological disasters is provided, comprising a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0030] An automated early warning method and system for remote sensing monitoring of geological disasters provided in an embodiment of the present application comprises the following steps: obtaining first geological disaster monitoring information of a target geological disaster monitoring attribute and a first risk hazard that triggers the first geological disaster monitoring information; obtaining a first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes within a first geological disaster risk stage based on the first risk hazard, wherein the first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes are geological disaster monitoring information triggered by a first geological disaster type partition, and the first risk hazard matches a second risk hazard in the first geological disaster type partition; on the premise that the first target geological disaster monitoring information related to the first geological disaster monitoring information is present in the first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes, determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, and then adding the important analysis strategy of the target geological disaster monitoring attribute to the analysis strategy library of the target geological disaster monitoring attribute so that the corresponding target analysis strategy can be selected for early warning of the corresponding geological disaster risk. In this way, when determining the important analysis strategy for the target geological disaster monitoring attributes, the geological disaster monitoring information of the target geological disaster monitoring attributes in the first geological disaster risk stage can be matched with the important analysis strategy triggered by the first geological disaster type zoning to determine the results of the important analysis strategy for the target geological disaster monitoring attributes. By comparing the matching metric values ​​of the collected geological disaster monitoring information and type constraints with the key warning content, the technical effect of determining the important analysis strategy for the target geological disaster monitoring attributes can be achieved, and the technical effect of improving the accuracy and reliability of geological disaster risk warnings can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 A flowchart of an automated early warning method for geological disasters monitored by remote sensing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0034] See also Figure 1 , shows an automated early warning method for geological disasters monitored by remote sensing, which may include the technical solutions described in the following steps 201 to 204.

[0035] Step 201, obtaining first geological disaster monitoring information of a target geological disaster monitoring attribute and a first risk hazard that triggers the first geological disaster monitoring information.

[0036] Exemplarily, the geological disaster monitoring attribute can be understood as monitoring the factors that cause geological disasters, such as rainfall, micro-earthquake conditions, etc.

[0037] Step 202, based on the first risk hazard, obtain the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage, wherein the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes is the geological disaster monitoring information triggered by the first geological disaster type partition, and the first risk hazard matches the second risk hazard of the first geological disaster type partition.

[0038] For example, the risk points of potential geological disasters may include landslides, ground fissures, and collapse.

[0039] Step 203, on the premise that there is first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the first target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute.

[0040] Exemplarily, the important analysis strategy lesson is understood as a model for analyzing traditional Chinese medicine information.

[0041] Step 204, adding the important analysis strategies of the target geological disaster monitoring attributes to the analysis strategy library of the target geological disaster monitoring attributes so that the corresponding target analysis strategies can be selected to carry out early warning of the corresponding geological disaster risks.

[0042] Exemplarily, in an independently implemented embodiment, the first geological disaster monitoring information may include but is not limited to the derived geological disaster monitoring information of the target geological disaster monitoring attribute. The risk trigger risk is deployed in the first geological disaster type partition, and multiple risk trigger risks can be deployed in the first geological disaster type partition. The first geological disaster type partition matches multiple geological disaster monitoring information triggers, and based on the matching situation, it can be determined whether the risk trigger risk is a risk trigger risk in the first geological disaster type partition.

[0043] Further, in an independently implemented embodiment, the first geological disaster monitoring information may include but is not limited to geological disaster monitoring information collected through a derivative thread. The first geological disaster monitoring information includes derivative risk data of target geological disaster monitoring attributes.

[0044] In an independently implemented embodiment, after obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute and a group of geological disaster monitoring information in the first geological disaster risk stage triggered by the first geological disaster type partition, the first geological disaster monitoring information is matched with each geological disaster monitoring information in the group of geological disaster monitoring information. On the premise that there is the first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the first target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute.

[0045] In a replaceable embodiment, first geological disaster monitoring information of a target geological disaster monitoring attribute and a first risk hazard that triggers the first geological disaster monitoring information are obtained; based on the first risk hazard, a first cluster of geological disaster monitoring attribute geological disaster monitoring information queues within a first geological disaster risk stage are obtained, wherein the first cluster of geological disaster monitoring attribute geological disaster monitoring information queues are geological disaster monitoring information triggered by a first geological disaster type partition, and the first risk hazard matches a second risk hazard in the geological disaster type partition; on the premise that there is first target geological disaster monitoring information related to the first geological disaster monitoring information in the first cluster of geological disaster monitoring attribute geological disaster monitoring information queues, the important analysis strategy corresponding to the first target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute, so that when determining the important analysis strategy of the target geological disaster monitoring attribute, the geological disaster monitoring information of the target geological disaster monitoring attribute within the first geological disaster risk stage can be matched with the important analysis strategy triggered by the first geological disaster type partition to determine the result of the important analysis strategy of the target geological disaster monitoring attribute, and the technical effect of determining the important analysis strategy of the target geological disaster monitoring attribute by searching for multi-dimensional positioning standard data in the spatial area corresponding to the collected sample and by comparing the matching metric values ​​of the collected data and the type constraint conditions with the key warning content.

[0046] In this embodiment, obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute may include the following contents.

[0047] Step 1: obtain X geological disaster monitoring information of the target geological disaster monitoring attributes in the second geological disaster risk stage, where X is a positive integer greater than 2, and the continuous maximum constraint value of the first geological disaster risk stage is greater than the continuous maximum constraint value of the second geological disaster risk stage.

[0048] Step 2, determining classification key data based on a set of geological disaster monitoring information of target geological disaster monitoring attributes in the third geological disaster risk stage, wherein the third geological disaster risk stage is a risk stage between the second geological disaster risk stage;

[0049] Step 3, determining a matching metric value between each geological hazard monitoring information in the X geological hazard monitoring information and the classification key data;

[0050] Step 4: determine the geological disaster monitoring information corresponding to the maximum matching metric value as the first geological disaster monitoring information.

[0051] In an independently implemented embodiment, in order to obtain first geological disaster monitoring information with a higher quantitative result associated with a set of geological disaster monitoring information, multiple geological disaster monitoring information that can be verified in the third geological disaster risk stage is obtained, and the multiple geological disaster monitoring information is integrated to determine the matching metric between multiple attribute contents of the target geological disaster monitoring attribute in the second geological disaster risk stage and the description result obtained by integration, and the geological disaster monitoring information with the maximum matching metric value is determined as the first geological disaster monitoring information. The geological disaster monitoring information with the maximum matching metric value is determined as the first geological disaster monitoring information.

[0052] Exemplarily, the first target geological disaster monitoring information related to the first geological disaster monitoring information is included in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, and the following contents may be included: determining a matching metric value between the first geological disaster monitoring information and each geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes;

[0053] On the premise that there is second geological disaster monitoring information whose matching metric value with the first geological disaster monitoring information is greater than the target metric value in a geological disaster monitoring information queue of a cluster of geological disaster monitoring attributes, the second geological disaster monitoring information is determined as the first target geological disaster monitoring information.

[0054] In an independently implemented embodiment, the target value of the above target metric value may be 1.6, and the target metric value may be set based on needs.

[0055] Exemplarily, obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute may include the following contents: obtaining the derivative risk data of the target geological disaster monitoring attribute, wherein the first geological disaster monitoring information includes the derivative geological disaster monitoring information. Obtaining a geological disaster monitoring information queue of a cluster of geological disaster monitoring attributes within the first geological disaster risk stage based on the first risk hazard may include the following contents: obtaining the derivative geological disaster monitoring information of the first cluster of geological disaster monitoring attributes within the first geological disaster risk stage based on the first risk hazard, wherein the geological disaster monitoring information queue includes the derivative geological disaster monitoring information.

[0056] Based on the above foundation, the above method may also include the following contents: on the premise that there is no first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, obtaining a geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes triggered by the second geological disaster type partition in the target partition segment, wherein the target partition segment is a partition segment with the first geological disaster type partition as the center point and a preset number of partitions as upper and lower segments; on the premise that there is second target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, determining the important analysis strategy corresponding to the second target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute.

[0057] In an independently implemented embodiment, in the case where the geological disaster monitoring information of a cluster of geological disaster monitoring attributes triggered in the first geological disaster category partition does not have the first target geological disaster monitoring information related to the first target user, that is, the target geological disaster monitoring attribute is not displayed in the first geological disaster category partition as an important analysis strategy, an important analysis strategy within a period of time triggered by the second geological disaster category partition that needs to be mined and processed within the first constraint condition with the first geological disaster category partition as the center point can be obtained. Among them, the second geological disaster category partition can include no less than one.

[0058] In an independently implemented embodiment, before determining the important analysis strategy corresponding to the second target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, the above method may also include the following contents: the second geological disaster type partition matches the second important analysis strategy queue corresponding to the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the second cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the second important analysis strategy queue.

[0059] Furthermore, before determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, the above method may also include the following contents: the first geological disaster type partition matches the first important analysis strategy queue corresponding to the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the first cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the first important analysis strategy queue.

[0060] By obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute and the first risk hazard that triggers the first geological disaster monitoring information; based on the first risk hazard, a geological disaster monitoring information queue of a first cluster of geological disaster monitoring attributes in the first geological disaster risk stage is obtained, wherein the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes is the geological disaster monitoring information triggered by the first geological disaster type partition, and the first risk hazard matches the second risk hazard of the first geological disaster type partition; on the premise that there is the first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the first target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute, and then the important analysis strategy of the target geological disaster monitoring attribute is added to the analysis strategy library of the target geological disaster monitoring attribute so that the corresponding target analysis strategy can be selected to carry out early warning of the corresponding geological disaster risk. In this way, when determining the important analysis strategy for the target geological disaster monitoring attributes, the geological disaster monitoring information of the target geological disaster monitoring attributes in the first geological disaster risk stage can be matched with the important analysis strategy triggered by the first geological disaster type zoning to determine the results of the important analysis strategy for the target geological disaster monitoring attributes. By comparing the matching metric values ​​of the collected geological disaster monitoring information and type constraints with the key warning content, the technical effect of determining the important analysis strategy for the target geological disaster monitoring attributes can be achieved, and the technical effect of improving the accuracy and reliability of geological disaster risk warnings can be achieved.

[0061] On the basis of the above, an automatic early warning method and system device for remote sensing monitoring of geological disasters are provided, which are applied to an automatic early warning system for remote sensing monitoring of geological disasters. The device comprises:

[0062] An attribute triggering module, used to obtain first geological disaster monitoring information of a target geological disaster monitoring attribute, and trigger a first risk hazard of the first geological disaster monitoring information;

[0063] A data acquisition module, used for obtaining a first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes in a first geological disaster risk stage based on the first risk hazard, wherein the first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes is geological disaster monitoring information triggered by a first geological disaster type partition, and the first risk hazard matches a second risk hazard of the first geological disaster type partition;

[0064] A strategy warning module, for determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, on the premise that there is first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes;

[0065] The strategy selection module is used to add the important analysis strategies of the target geological disaster monitoring attributes to the analysis strategy library of the target geological disaster monitoring attributes so that the corresponding target analysis strategies can be selected to carry out early warning of corresponding geological disaster risks.

[0066] On the basis of the above, an automatic early warning system for remote sensing monitoring of geological disasters is shown, including a processor and a memory that communicate with each other, and the processor is used to read and execute a computer program from the memory to implement the above method.

[0067] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0068] In summary, based on the above scheme, by obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute and the first risk hazard that triggers the first geological disaster monitoring information; based on the first risk hazard, a geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage is obtained, wherein the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes is the geological disaster monitoring information triggered by the first geological disaster type partition, and the first risk hazard matches the second risk hazard of the first geological disaster type partition; on the premise that there is the first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the first target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute, and then the important analysis strategy of the target geological disaster monitoring attribute is added to the analysis strategy library of the target geological disaster monitoring attribute so that the corresponding target analysis strategy can be selected for early warning of the corresponding geological disaster risk. In this way, when determining the important analysis strategy for the target geological disaster monitoring attributes, the geological disaster monitoring information of the target geological disaster monitoring attributes in the first geological disaster risk stage can be matched with the important analysis strategy triggered by the first geological disaster type zoning to determine the results of the important analysis strategy for the target geological disaster monitoring attributes. By comparing the matching metric values ​​of the collected geological disaster monitoring information and type constraints with the key warning content, the technical effect of determining the important analysis strategy for the target geological disaster monitoring attributes can be achieved, and the technical effect of improving the accuracy and reliability of geological disaster risk warnings can be achieved.

[0069] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software such as executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0070] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other beneficial effects that may be obtained.

[0071] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0072] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0073] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0074] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0075] The computer program codes required for the operation of each part of the present application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0076] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0077] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0078] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used for the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of the present application are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0079] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, instructions, publications, documents, etc., cited in this application are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the content of this application, documents that limit the broadest scope of the claims of this application (currently or later attached to this application) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the attached materials of this application are inconsistent or conflicting with the content described in this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0080] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

[0081] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An automatic early warning method for remote sensing monitoring of geological disasters, characterized in that: include: Obtaining first geological disaster monitoring information of a target geological disaster monitoring attribute and a first risk hazard that triggers the first geological disaster monitoring information; Based on the first risk hazard, a first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes in a first geological disaster risk stage is obtained, wherein the first cluster of geological disaster monitoring information queues of geological disaster monitoring attributes is geological disaster monitoring information triggered by a first geological disaster type partition, and the first risk hazard matches a second risk hazard of the first geological disaster type partition; On the premise that there is first target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute; Adding the important analysis strategies of the target geological disaster monitoring attributes to the analysis strategy library of the target geological disaster monitoring attributes so that the corresponding target analysis strategies can be selected to carry out early warning of the corresponding geological disaster risks; The first geological disaster monitoring information of obtaining the target geological disaster monitoring attribute includes: Obtaining X geological disaster monitoring information of the target geological disaster monitoring attribute in the second geological disaster risk stage, the continuous maximum constraint value of the first geological disaster risk stage is greater than the continuous maximum constraint value of the second geological disaster risk stage; Determining classification key data based on a set of geological disaster monitoring information of the target geological disaster monitoring attributes in the third geological disaster risk stage, wherein the third geological disaster risk stage is local information of the second geological disaster risk stage; Determine a matching metric value between each of the X geological hazard monitoring information and the classification key data; Determine the geological disaster monitoring information corresponding to the maximum matching metric value as the first geological disaster monitoring information; The first target geological disaster monitoring information related to the first geological disaster monitoring information is included in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, including: Determine a matching metric value between the first geological disaster monitoring information and each geological disaster monitoring information in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes; On the premise that there is second geological disaster monitoring information whose matching metric value with the first geological disaster monitoring information is greater than the target metric value in the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, the second geological disaster monitoring information is determined as the first target geological disaster monitoring information.

2. The method according to claim 1, characterized in that include: Obtaining the first geological disaster monitoring information of the target geological disaster monitoring attribute includes: Obtaining derived risk data of the target geological disaster monitoring attribute, wherein the first geological disaster monitoring information includes derived geological disaster monitoring information; The geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage is obtained based on the first risk hazard, including: Based on the first risk hazard, derived geological disaster monitoring information of the first cluster of geological disaster monitoring attributes in the first geological disaster risk stage is obtained, wherein the geological disaster monitoring information queue includes the derived geological disaster monitoring information.

3. The method according to claim 1, characterized in that include: Under the premise that the first cluster of geological disaster monitoring attribute geological disaster monitoring information queue does not have the first target geological disaster monitoring information related to the first geological disaster monitoring information, a second cluster of geological disaster monitoring attribute geological disaster monitoring information queue triggered by the second geological disaster type partition in the target partition segment is obtained, wherein the target partition segment is a partition segment with the first geological disaster type partition as the center point and a preset number of partitions as upper and lower segments; On the premise that there is second target geological disaster monitoring information related to the first geological disaster monitoring information in the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, the important analysis strategy corresponding to the second target geological disaster monitoring information is determined as the important analysis strategy of the target geological disaster monitoring attribute.

4. The method according to claim 3, characterized in that Before determining the important analysis strategy corresponding to the second target geological disaster monitoring information as the important analysis strategy of the target geological disaster monitoring attribute, the method further includes: The second geological disaster type partition matches the second important analysis strategy queue corresponding to the geological disaster monitoring information queue of the second cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the second cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the second important analysis strategy queue.

5. The method according to claim 1, characterized in that Before determining the important analysis strategy corresponding to the first target geological disaster monitoring information as the important analysis strategy for the target geological disaster monitoring attribute, the method further includes: The first geological disaster type partition matches the first important analysis strategy queue corresponding to the geological disaster monitoring information queue of the first cluster of geological disaster monitoring attributes, wherein each geological disaster monitoring attribute in the first cluster of geological disaster monitoring attributes is matched with the important analysis strategy in the first important analysis strategy queue.

6. An automatic early warning system for remote sensing monitoring of geological disasters, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 5.

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