High slope disaster real-time intelligent early warning system and early warning method

Abstract

The invention relates to a high slope disaster real-time intelligent early warning system and early warning method. The high slope disaster real-time intelligent early warning system comprises a distributed optical fiber temperature measurement module, a multi-source monitoring data acquisition module, a big data processing and storage module and an AI intelligent analysis early warning module. Wherein the distributed optical fiber temperature measurement module comprises a plurality of distributed optical fiber sensors, and the distributed optical fiber sensors are arranged at intervals along the length and / or width direction of the high slope; the multi-source monitoring data acquisition module is arranged on the slope surface of the high slope; the big data processing and storage module is in signal connection with the distributed optical fiber temperature measurement module and the multi-source monitoring data acquisition module. The big data processing and storage module is used for real-time acquisition, cleaning, integration and storage of monitoring data. The AI intelligent analysis and early warning module is in data connection with the big data processing and storage module, and the AI intelligent analysis and early warning module can carry out high slope critical state recognition and intelligent early warning.

Description

Technical Field

[0001] The present application relates to the field of geological engineering monitoring technology, and in particular to a real-time intelligent early warning system and early warning method for high slope disasters. Background Art

[0002] As an important part of infrastructure such as mountain roads and reservoir dams, the stability of high slopes is crucial to engineering safety. However, due to the complex geological structure, changeable climate conditions and the influence of human activities, high slopes are often faced with disaster risks such as landslides and collapses. Traditional slope monitoring methods, such as manual inspections and single-point displacement monitoring, have problems such as limited monitoring range, single data, and poor warning timeliness, making it difficult to comprehensively and accurately assess the stability of slopes. Summary of the invention

[0003] A first aspect of the present application provides a real-time intelligent early warning system for high slope disasters, which can more comprehensively and accurately evaluate the stability state of the slope.

[0004] The real-time intelligent early warning system for high slope disasters provided in the first aspect of the present application comprises: a distributed optical fiber temperature measurement module, the distributed optical fiber temperature measurement module comprises a plurality of distributed optical fiber sensors, the distributed optical fiber sensors are arranged at intervals along the length and / or width direction of the high slope;

[0005] A multi-source monitoring data acquisition module, wherein the multi-source monitoring data acquisition module is arranged on the slope surface of the high slope;

[0006] A big data processing and storage module, wherein the big data processing and storage module is respectively connected to the distributed optical fiber temperature measurement module and the multi-source monitoring data acquisition module by signal, and the big data processing and storage module is used for real-time acquisition, cleaning, integration and storage of monitoring data;

[0007] An AI intelligent analysis and early warning module is data-connected to the big data processing and storage module, and the AI ​​intelligent analysis and early warning module is capable of identifying and intelligently warning the critical state of the high slope.

[0008] In addition, the high slope disaster real-time intelligent early warning system provided by the present application may also have the following additional technical features:

[0009] In an optional solution, the multi-source monitoring data acquisition module includes multiple GPS displacement monitors, multiple rainfall monitors and multiple soil moisture monitors. The GPS displacement monitors, the rainfall monitors and the soil moisture monitors are arranged at different positions of the high slope in different orientations, and there is a preset height difference between the multiple different positions.

[0010] In an optional scheme, the arrangement position of the distributed optical fiber sensors includes at least one of the potential sliding surface, crack development area and groundwater enrichment zone of the high slope, and the distributed optical fiber sensors are arranged at intervals along the length and width directions of the high slope to form a three-dimensional temperature measurement network.

[0011] In an optional solution, the AI ​​intelligent analysis and early warning module includes an early warning information generation and release module. When the prediction result of the AI ​​intelligent analysis and early warning module exceeds a preset early warning threshold, the early warning information generation and release module generates early warning information and pushes the information.

[0012] The second aspect of the present application provides a real-time intelligent early warning method for high slope disasters, comprising the following steps:

[0013] 1) Construct a distributed optical fiber temperature measurement network on high slopes;

[0014] 2) Deployment of multi-source monitoring data acquisition modules based on the specific geological characteristics and climatic conditions of the slope;

[0015] 3) Building a high-performance, scalable big data processing center that uses cloud computing and virtualization technologies to improve data processing efficiency and resource utilization, and to form a big data processing and storage module that can perform data cleaning, integration, storage, and analysis functions;

[0016] 4) Develop an AI intelligent analysis and early warning module with AI intelligent analysis and early warning functions to identify and intelligently warn of the critical state of high slopes.

[0017] In an optional solution, in step 1), the construction of the distributed optical fiber temperature measurement network includes: laying distributed optical fiber sensors in a preset area of ​​the high slope, the preset area including at least one of a potential sliding surface, a crack development area, and a groundwater enrichment zone; the distributed optical fiber sensors are arranged at intervals along the length and width direction of the high slope to form a three-dimensional temperature measurement network, ensuring that the temperature changes inside the high slope can be fully captured;

[0018] In step 2), the deployment of the multi-source monitoring data acquisition module includes: deploying GPS displacement monitors, rainfall monitors and soil moisture monitors, and the GPS displacement monitors, rainfall monitors and soil moisture monitors need to be distributed at different heights, directions and positions of the high slope to obtain comprehensive environmental parameter data.

[0019] In an optional solution, in step 4), the development of the AI ​​intelligent analysis and early warning module includes the following steps:

[0020] Extracting features related to high slope stability from the preprocessed data, including temperature change rate, displacement rate, and rainfall change, and establishing a high slope instability prediction model based on a deep learning algorithm;

[0021] Use historical monitoring data to train the model, and optimize model parameters through cross-validation and grid search methods to improve prediction accuracy;

[0022] According to the model prediction results, the warning threshold is set. When the prediction result exceeds the threshold, the warning information is automatically generated and sent to relevant personnel, and the warning information is visualized at the same time.

[0023] In an optional solution, in step 4), the identification and intelligent early warning of the critical state of the high slope include the following steps:

[0024] The distributed optical fiber temperature measurement network and the multi-source monitoring data acquisition module are used to collect temperature, displacement and rainfall change information in real time;

[0025] The collected data is cleaned, integrated, stored and analyzed through the big data processing and storage module, and the data is transmitted to the AI ​​intelligent analysis and early warning module;

[0026] When the prediction result of the AI ​​intelligent analysis and warning module exceeds the threshold, warning information is automatically generated and a warning is issued.

[0027] The beneficial effects of this application are:

[0028] The real-time intelligent early warning system and early warning method for high slope disasters in this application combine distributed optical fiber temperature measurement data with other monitoring data during data collection to improve the comprehensive judgment ability of the early warning system; during the early warning, the AI ​​intelligent analysis early warning module is used to automatically learn the characteristic patterns before the slope becomes unstable, accurately identify the critical state of the slope, and give early warning of the disaster process; at the same time, according to the newly collected monitoring data, the AI ​​prediction model is dynamically adjusted and optimized to improve the accuracy of the early warning; thereby realizing remote real-time monitoring of the slope status, reducing the risk of manual inspections, and improving monitoring efficiency.

[0029] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of the structure of the real-time intelligent early warning system for high slope disasters provided for this application;

[0031] Figure 2 It is a schematic diagram of the layout structure of the distributed optical fiber temperature measurement module and the multi-source monitoring data acquisition module;

[0032] Figure 3 for Figure 2 Schematic diagram of the side structure.

[0033] Figure numerals: distributed optical fiber temperature measurement module 1, distributed optical fiber sensor 11, high slope 2, multi-source monitoring data acquisition module 3, GPS displacement monitor 31, rainfall monitor 32, soil moisture monitor 33, big data processing and storage module 4, AI intelligent analysis and early warning module 5.

[0034] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application. DETAILED DESCRIPTION

[0035] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0036] It should be clear that the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other technical solutions obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0037] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0038] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0039] It should be noted that the directional words such as "upper", "lower", "left", and "right" described in the embodiments of the present application are described at the angles shown in the accompanying drawings and should not be understood as limiting the embodiments of the present application. In addition, in the context, it is also necessary to understand that when it is mentioned that an element is connected to another element "upper" or "lower", it can not only be directly connected to another element "upper" or "lower", but also indirectly connected to another element "upper" or "lower" through an intermediate element.

[0040] like Figure 1-3As shown, the first aspect of the embodiment of the present application provides a real-time intelligent early warning system for high slope disasters. The real-time intelligent early warning system for high slope disasters can monitor the temperature changes and other environmental parameters inside the slope in real time, accurately identify the critical state of the slope through big data processing and AI intelligent analysis, and provide early warning of the disaster process, thereby providing a scientific basis and decision-making support for slope safety management.

[0041] The real-time intelligent early warning system for high slope disasters includes a distributed optical fiber temperature measurement module 1, a multi-source monitoring data acquisition module 3, a big data processing and storage module 4, and an AI intelligent analysis and early warning module 5. Among them, the distributed optical fiber temperature measurement module 1 includes a plurality of distributed optical fiber sensors 11, which are arranged at intervals along the length and / or width direction of the high slope 2; the multi-source monitoring data acquisition module 3 is arranged on the slope surface of the high slope 2; the big data processing and storage module 4 is respectively connected to the distributed optical fiber temperature measurement module 1 and the multi-source monitoring data acquisition module 3 by signal, and the big data processing and storage module 4 is used for real-time collection, cleaning, integration and storage of monitoring data; the AI ​​intelligent analysis and early warning module 5 is connected to the big data processing and storage module 4 by data, and the AI ​​intelligent analysis and early warning module 5 can identify the critical state of the high slope 2 and provide intelligent early warning.

[0042] Specifically, in this embodiment, the distributed optical fiber temperature measurement module 1 and the multi-source monitoring data acquisition module 3 are arranged on the slope surface, the distributed optical fiber sensor 11 is used to monitor the temperature changes inside the slope in real time, and the big data processing and storage module 4 and the AI ​​intelligent analysis and early warning module 5 are located in a remote monitoring room; the distributed optical fiber temperature measurement module 1 and the multi-source monitoring data acquisition module 3 are connected to the big data processing and storage module 4 and the AI ​​intelligent analysis and early warning module 5 by cables and wireless transmission.

[0043] like Figure 2 As shown, in a specific embodiment, the multi-source monitoring data acquisition module 3 includes multiple GPS displacement monitors 31, multiple rainfall monitors 32 and multiple soil moisture monitors 33. The GPS displacement monitors 31, rainfall monitors 32 and soil moisture monitors 33 are arranged at different positions of the high slope 2 in different orientations, and there is a preset height difference between the multiple different positions, so as to obtain comprehensive environmental parameter data.

[0044] like Figure 2-3 As shown, in a specific embodiment, the arrangement position of the distributed optical fiber sensor 11 includes at least one of the potential sliding surface, crack development area and groundwater enrichment zone of the high slope 2. The distributed optical fiber sensor 11 is arranged at intervals along the length and width direction of the high slope 2 to form a three-dimensional temperature measurement network to capture the temperature changes inside the slope and reflect potential thermal anomalies or groundwater activities, thereby ensuring that the temperature changes inside the slope can be fully captured.

[0045] In a specific embodiment, the AI ​​intelligent analysis and early warning module 5 includes an early warning information generation and release module. When the prediction result of the AI ​​intelligent analysis and early warning module 5 exceeds a preset early warning threshold, the early warning information generation and release module generates early warning information and pushes the information.

[0046] A second aspect of the embodiment of the present application provides a real-time intelligent early warning method for high slope disasters, which mainly includes the following steps:

[0047] 1) Constructing a distributed optical fiber temperature measurement network: Distributed optical fiber sensors 11 are laid in a preset area of ​​the high slope 2, and the preset area includes at least one of a potential sliding surface, a crack development area, and a groundwater enrichment zone; the distributed optical fiber sensors 11 are arranged at intervals along the length and width of the high slope 2 to form a three-dimensional temperature measurement network, ensuring that the temperature changes inside the high slope 2 can be fully captured.

[0048] 2) Deployment of multi-source monitoring data acquisition module 3: Based on the specific geological characteristics and climatic conditions of the slope, deploy GPS displacement monitors 31, rainfall monitors 32 and soil moisture monitors 33. GPS displacement monitors 31, rainfall monitors 32 and soil moisture monitors 33 need to be distributed at different heights, directions and positions of the high slope 2 to obtain comprehensive environmental parameter data.

[0049] 3) Build a high-performance, scalable big data processing center. The big data processing center uses cloud computing and virtualization technologies to improve data processing efficiency and resource utilization, and form a big data processing and storage module that can perform data cleaning, integration, storage and analysis functions4.

[0050] 4) Development of AI intelligent analysis and early warning module 5 to identify the critical state of high slope 2 and intelligently warn; the development of AI intelligent analysis and early warning module 5 includes: extracting features related to the stability of high slope 2 from preprocessed data, such as temperature change rate, displacement rate, rainfall change, etc., and establishing a high slope 2 instability prediction model based on deep learning algorithm; using historical monitoring data to train the model, and optimizing model parameters through cross-validation, grid search and other methods to improve prediction accuracy; setting a warning threshold based on the model prediction results, and automatically generating warning information when the prediction result exceeds the threshold, and sending it to relevant personnel through SMS, email, APP push, etc., and visually displaying the warning information, such as warning maps, warning trend charts, etc., so that relevant personnel can quickly understand the slope status.

[0051] In a specific embodiment, in step 4), the identification and intelligent early warning of the critical state of the high slope 2 include the following steps:

[0052] Real-time collection of temperature, displacement and rainfall change information through a distributed optical fiber temperature measurement network and a multi-source monitoring data acquisition module 3;

[0053] The collected data is cleaned, integrated, stored and analyzed through the big data processing and storage module 4, and the data is transmitted to the AI ​​intelligent analysis and early warning module 5;

[0054] When the prediction result of the AI ​​intelligent analysis and early warning module 5 exceeds the threshold, early warning information is automatically generated and sent to relevant personnel through SMS, email, APP push, etc. At the same time, the system provides a visual display of early warning information, such as early warning maps, early warning trend charts, etc., so that relevant personnel can quickly understand the slope status and solve the problem.

[0055] The real-time intelligent early warning system and early warning method for high slope disasters in the present application combine distributed optical fiber temperature measurement data with other monitoring data during data collection to improve the comprehensive judgment ability of the early warning system; during early warning, the AI ​​intelligent analysis early warning module 5 is used to automatically learn the characteristic patterns before the slope becomes unstable, accurately identify the critical state of the slope, and give early warning of the disaster process. At the same time, according to the newly collected monitoring data, the AI ​​prediction model is dynamically adjusted and optimized to improve the accuracy of the early warning; thereby realizing remote real-time monitoring of the slope status, reducing the risk of manual inspections, and improving monitoring efficiency.

[0056] The above are only preferred 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 modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A real-time intelligent early warning system for high slope disasters, characterized in that: include: A distributed optical fiber temperature measurement module, wherein the distributed optical fiber temperature measurement module comprises a plurality of distributed optical fiber sensors, and the distributed optical fiber sensors are arranged at intervals along the length and / or width direction of the high slope; A multi-source monitoring data acquisition module, wherein the multi-source monitoring data acquisition module is arranged on the slope surface of the high slope; A big data processing and storage module, wherein the big data processing and storage module is respectively connected to the distributed optical fiber temperature measurement module and the multi-source monitoring data acquisition module by signal, and the big data processing and storage module is used for real-time acquisition, cleaning, integration and storage of monitoring data; An AI intelligent analysis and early warning module is data-connected to the big data processing and storage module, and the AI ​​intelligent analysis and early warning module is capable of identifying and intelligently warning the critical state of the high slope.

2. The high slope disaster real-time intelligent early warning system according to claim 1 is characterized in that: The multi-source monitoring data acquisition module includes multiple GPS displacement monitors, multiple rainfall monitors and multiple soil moisture monitors. The GPS displacement monitors, the rainfall monitors and the soil moisture monitors are arranged at different positions of the high slope in different orientations, and there is a preset height difference between the multiple different positions.

3. The high slope disaster real-time intelligent early warning system according to claim 1 or 2, characterized in that: The arrangement positions of the distributed optical fiber sensors include at least one of the potential sliding surface, crack development area and groundwater enrichment zone of the high slope. The distributed optical fiber sensors are arranged at intervals along the length and width directions of the high slope to form a three-dimensional temperature measurement network.

4. The high slope disaster real-time intelligent early warning system according to claim 1 or 2, characterized in that: The AI ​​intelligent analysis and early warning module includes an early warning information generation and release module. When the prediction result of the AI ​​intelligent analysis and early warning module exceeds a preset early warning threshold, the early warning information generation and release module generates early warning information and pushes the information.

5. A real-time intelligent early warning method for high slope disasters, characterized in that: The following steps are involved: 1) Construct a distributed optical fiber temperature measurement network on high slopes; 2) Deployment of multi-source monitoring data acquisition modules based on the specific geological characteristics and climatic conditions of the slope; 3) Building a high-performance, scalable big data processing center that uses cloud computing and virtualization technologies to improve data processing efficiency and resource utilization, and to form a big data processing and storage module that can perform data cleaning, integration, storage, and analysis functions; 4) Develop an AI intelligent analysis and early warning module with AI intelligent analysis and early warning functions to identify and intelligently warn of the critical state of high slopes.

6. The real-time intelligent early warning method for high slope disasters according to claim 5 is characterized in that: In step 1), the construction of the distributed optical fiber temperature measurement network includes: laying distributed optical fiber sensors in a preset area of ​​the high slope, the preset area includes at least one of a potential sliding surface, a crack development area and a groundwater enrichment zone; the distributed optical fiber sensors are arranged at intervals along the length and width of the high slope to form a three-dimensional temperature measurement network, ensuring that the temperature changes inside the high slope can be fully captured; In step 2), the deployment of the multi-source monitoring data acquisition module includes: deploying GPS displacement monitors, rainfall monitors and soil moisture monitors, and the GPS displacement monitors, rainfall monitors and soil moisture monitors need to be distributed at different heights, directions and positions of the high slope to obtain comprehensive environmental parameter data.

7. The real-time intelligent early warning method for high slope disasters according to claim 5 is characterized in that: In step 4), the development of the AI ​​intelligent analysis and early warning module includes the following steps: Extracting features related to high slope stability from the preprocessed data, including temperature change rate, displacement rate, and rainfall change, and establishing a high slope instability prediction model based on a deep learning algorithm; Use historical monitoring data to train the model, and optimize model parameters through cross-validation and grid search methods to improve prediction accuracy; According to the model prediction results, the warning threshold is set. When the prediction result exceeds the threshold, the warning information is automatically generated and sent to relevant personnel, and the warning information is visualized at the same time.

8. The real-time intelligent early warning method for high slope disasters according to any one of claims 5 to 7, characterized in that: In step 4), the identification and intelligent early warning of the critical state of the high slope include the following steps: The distributed optical fiber temperature measurement network and the multi-source monitoring data acquisition module are used to collect temperature, displacement and rainfall change information in real time; The collected data is cleaned, integrated, stored and analyzed through the big data processing and storage module, and the data is transmitted to the AI ​​intelligent analysis and early warning module; When the prediction result of the AI ​​intelligent analysis and warning module exceeds the threshold, warning information is automatically generated and a warning is issued.

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