A multi-source information fusion early warning method for tailings dam instability

Abstract

The application provides a tailing dam instability trend multi-source information fusion early warning method. The method comprises the following steps: collecting tailing dam parameters of a tailing dam, and determining first multi-source early warning parameters from the tailing dam parameters; obtaining a topographic and geomorphic model of the tailing dam, and establishing a multi-scale multi-factor coupled mechanical model of the tailing dam based on the topographic and geomorphic model and the first multi-source early warning parameters; inputting the tailing dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam-bursting process of the tailing dam, and obtaining a prediction simulation result; and early warning the instability of the tailing dam based on the prediction simulation result. Thus, the present scheme uses the multi-scale multi-factor coupled mechanical model for early warning, considers the physical mechanism and interaction of the tailing dam at different scales, and can more accurately predict the instability trend of the tailing dam by coupling analysis of multiple factors, thereby improving the early warning precision.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tailings dam safety monitoring and tailings dam early warning, and in particular to a tailings dam instability trend multi-source information fusion early warning method. BACKGROUND

[0002] Safety monitoring of tailings dams is an important work in the field of non-coal mine safety. Due to problems such as rainfall, weathering, earthquakes, improper construction and maintenance, blasting, seepage, etc., the dam body will be unstable, which will lead to the destruction of the dam body and cause huge economic losses and casualties.

[0003] Existing tailings dam instability early warning technologies are mostly focused on specific engineering cases, lack overall integrated monitoring capabilities, have not been systematically studied, and have not formed an integrated monitoring system. SUMMARY

[0004] The present application aims to at least partially solve one of the technical problems in the related art.

[0005] To this end, the first object of the present application is to propose a tailings dam instability trend multi-source information fusion early warning method to achieve high-precision integrated monitoring and early warning of tailings dams, improve the diversity of monitoring technology in tailings dam instability early warning technology, and further improve the accuracy and real-time performance of early warning.

[0006] The second object of the present application is to propose a tailings dam instability trend multi-source information fusion early warning system.

[0007] The third object of the present application is to propose a tailings dam instability trend multi-source information fusion early warning device.

[0008] The fourth object of the present application is to propose an electronic device.

[0009] The fifth object of the present application is to propose a computer-readable storage medium.

[0010] The sixth object of the present application is to propose a computer program product.

[0011] To achieve the above objects, the first aspect of the present application proposes a tailings dam instability trend multi-source information fusion early warning method, comprising: collecting tailings dam parameters of a tailings dam, and determining first multi-source early warning parameters from the tailings dam parameters; obtaining a topographic and geomorphic model of the tailings dam, and based on the topographic and geomorphic model and the first multi-source early warning parameters, establishing a multi-scale multi-factor coupled mechanical model of the tailings dam; inputting the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam failure process of the tailings dam, and obtaining a prediction simulation result; based on the prediction simulation result, early warning of instability of the tailings dam.

[0012] To achieve the above object, the second aspect of the present application proposes a tailings dam instability trend multi-source information fusion early warning system, comprising: a monitoring sensor, a UAV, a base station, and a tailings dam early warning platform, wherein the monitoring sensor and the UAV are connected to the base station based on an ad hoc network, and the base station is connected to the tailings dam early warning platform based on a wireless network; the monitoring sensor is configured to collect first tailings dam parameters of a tailings dam; the UAV is configured to collect second tailings dam parameters of the tailings dam; the base station is configured to acquire the first tailings dam parameters and the second tailings dam parameters collected by the monitoring sensor and the UAV based on the ad hoc network, take the first tailings dam parameters and the second tailings dam parameters as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platform based on the wireless network; and the tailings dam early warning platform is configured to perform instability early warning on the tailings dam based on the tailings dam parameters and a multi-scale multi-factor coupled mechanical model.

[0013] To achieve the above object, the third aspect of the present application proposes a tailings dam instability trend multi-source information fusion early warning device, comprising: an acquisition module configured to acquire tailings dam parameters of a tailings dam and determine first multi-source early warning parameters from the tailings dam parameters; an establishment module configured to acquire a topographic and geomorphic model of the tailings dam and establish a multi-scale multi-factor coupled mechanical model of the tailings dam based on the topographic and geomorphic model and the first multi-source early warning parameters; a simulation module configured to input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to perform prediction simulation on a dam break process of the tailings dam and obtain a prediction simulation result; and an early warning module configured to perform early warning on instability of the tailings dam based on the prediction simulation result.

[0014] To achieve the above object, the fourth aspect of the present application proposes an electronic device, comprising: a processor; and a memory connected to the processor in communication; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory, so that the processor can execute the tailings dam instability trend multi-source information fusion early warning method of the first aspect of the present application.

[0015] To achieve the above object, the fifth aspect of the present application proposes a computer readable storage medium having a computer program stored thereon, wherein the computer program is configured to make the computer execute the tailings dam instability trend multi-source information fusion early warning method of the first aspect of the present application.

[0016] To achieve the above object, the sixth aspect of the present application proposes a computer program product comprising a computer program, wherein the computer program is executed by a processor to implement the tailings dam instability trend multi-source information fusion early warning method of the first aspect of the present application.

[0017] The tailings dam instability trend multi-source information fusion early warning method provided by the application, by collecting tailings dam parameters, determining first multi-source early warning parameters from the tailings dam parameters, and establishing a multi-scale multi-factor coupled mechanical model according to the first multi-source early warning parameters and the topography and geomorphology model of the tailings dam. Further, according to the multi-scale multi-factor coupled mechanical model and the tailings dam parameters, the dam break process of the tailings dam is simulated and predicted to obtain a prediction simulation result. Further, the instability of the tailings dam can be early warned based on the prediction simulation result. The multi-scale multi-factor coupled mechanical model is used for early warning, which considers the physical mechanism and interaction of the tailings dam at different scales, and through coupling analysis of multiple factors, the instability trend of the tailings dam can be more accurately predicted, and the early warning accuracy is improved. Further, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technology in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of the early warning are further improved, thereby ensuring the safety of people's life and property and environmental safety.

[0018] Additional aspects and advantages of the application will be set forth in part in the description that follows, and in part will become apparent to those skilled in the art upon examination of the following description and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] The above and / or additional aspects and advantages of the application will become apparent and be readily understood from the following description, taken in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flowchart of a tailings dam instability trend multi-source information fusion early warning method provided by an embodiment of the application is shown in the figure;

[0021] Figure 2 A flowchart of another tailings dam instability trend multi-source information fusion early warning method provided by an embodiment of the application is shown in the figure;

[0022] Figure 3 A flowchart of another tailings dam instability trend multi-source information fusion early warning method provided by an embodiment of the application is shown in the figure;

[0023] Figure 4 A flowchart of another tailings dam instability trend multi-source information fusion early warning method provided by an embodiment of the application is shown in the figure;

[0024] Figure 5 A structure diagram of a tailings dam instability trend multi-source information fusion early warning system provided by an embodiment of the application is shown in the figure;

[0025] Figure 6 A principle diagram of tailings dam multi-source information fusion early warning provided by an embodiment of the application is shown in the figure;

[0026] Figure 7A schematic diagram of the tailings dam early warning provided by the embodiment of the present application;

[0027] Figure 8 A structural schematic diagram of a tailings dam instability trend multi-source information fusion early warning device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0028] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0029] The tailings dam instability trend multi-source information fusion early warning method of the embodiment of the present application is described below with reference to the accompanying drawings.

[0030] Figure 1 is a flow chart of a tailings dam instability trend multi-source information fusion early warning method according to an exemplary embodiment, as shown in Figure 1 The tailings dam instability trend multi-source information fusion early warning method of the embodiment of the present application includes but is not limited to the following steps:

[0031] S101, collecting tailings dam parameters of the tailings dam, and determining first multi-source early warning parameters from the tailings dam parameters.

[0032] It should be noted that the execution subject of the tailings dam instability trend multi-source information fusion early warning method provided by the embodiment of the present application is an electronic device, which can be a terminal device. Alternatively, the terminal device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a personal computer (PC), etc. The embodiment of the present application is not limited specifically.

[0033] In some implementations, the tailings dam parameters include but are not limited to surface displacement, velocity, acceleration, vibration, inclination angle, magnetic declination angle, deep displacement, temperature, image, humidity, displacement, deformation, absolute displacement, image data of the tailings dam, infrared data, etc.

[0034] Optionally, the tailings dam can be monitored in multiple dimensions based on multiple sensors to obtain tailings dam parameters. Optionally, the multi-dimensional monitoring includes surface sensing monitoring, deep sensing monitoring, remote sensing satellite monitoring, unmanned aerial vehicle monitoring, ground-based radar monitoring, Global Positioning System (GPS) monitoring, and the like.

[0035] In the surface sensing monitoring, integrated sensing devices are installed on the surface of the tailings dam, which integrate three-axis inclination, speed, acceleration, vibration, and three-axis magnetic declination sensors to measure the kinematic and dynamic parameters of the tailings dam surface. The surface sensing monitoring provides surface displacement, speed, acceleration, vibration, inclination, and magnetic declination parameters.

[0036] In the deep sensing monitoring, chain integrated sensing devices are installed in the deep part of the tailings dam, which integrate three-axis inclination, speed, acceleration, vibration, and temperature sensors to measure the kinematic and dynamic parameters of the deep part of the tailings dam. The deep sensing monitoring provides deep displacement, speed, acceleration, vibration, inclination, and temperature parameters.

[0037] In the remote sensing satellite monitoring, remote sensing technology is used to obtain surface information of the tailings dam area through satellites or other carriers. The remote sensing satellite monitoring provides image, temperature, humidity, and other parameters.

[0038] In the unmanned aerial vehicle monitoring, a base station is installed on the tailings dam and unmanned aerial vehicles deployed therein are used for monitoring. The unmanned aerial vehicle monitoring provides tailings dam image data, infrared data, and other parameters.

[0039] In the ground-based radar monitoring, a planar scanning unit uses microwave signals for non-contact scanning measurement, and through high spatial resolution and monitoring accuracy, combined with step frequency continuous wave technology and interferometric measurement technology, the tailings dam is monitored for static and dynamic high-precision. The ground-based radar monitoring provides displacement, speed, vibration, deformation, and other parameters.

[0040] In the GPS monitoring, Global Navigation Satellite System (GNSS) receivers are deployed on the tailings dam to obtain three-dimensional coordinate change data of the tailings dam monitoring points. The GPS monitoring provides absolute displacement, speed, acceleration, and other parameters.

[0041] In some implementations, displacement, speed, acceleration, vibration, and inclination can be obtained from the tailings dam parameters as first multi-source early warning parameters.

[0042] In some implementations, displacement, speed, acceleration, vibration, and inclination can be obtained from the tailings dam parameters as first multi-source early warning parameters.

[0043] In some implementations, a topographic and geomorphic model of the tailings dam can be constructed according to some of the tailings dam parameters. For example, based on the image data and infrared data in the tailings dam parameters, the topographic and geomorphic model can be constructed.

[0044] Further, the tailings dam is divided into different regions according to the topographic and geomorphic model, such as the surface region, the deep region, the head region, the tail region, etc., and the mechanical properties corresponding to different regions are determined, so as to couple the first multi-element early warning parameters according to the mechanical properties, determine the coupling equation, and further construct the multi-scale and multi-factor coupled mechanical model of the tailings dam according to the topographic and geomorphic model and the coupling equation. For example, the first multi-element early warning parameters can be coupled according to the mechanical properties of the tailings sand in different regions.

[0045] That is, the appearance of the multi-scale and multi-factor coupled mechanical model of the tailings dam is provided by the topographic and geomorphic model, and the interaction between the tailings dam parameters is provided by the coupling equation, and thus the multi-scale and multi-factor coupled mechanical model that can simulate the dam failure of the tailings dam is composed.

[0046] S103, input the tailings dam parameters into the multi-scale and multi-factor coupled mechanical model to predict and simulate the dam failure process of the tailings dam, and obtain the prediction simulation result.

[0047] In some implementations, by inputting the tailings dam parameters into the multi-scale and multi-factor coupled mechanical model, the model iteratively simulates the dam failure process based on the tailings dam parameters to determine the parameter threshold of the tailings dam parameters, and the parameter threshold is taken as the prediction simulation result.

[0048] Optionally, during the iterative simulation, if the tailings dam does not fail during this iteration simulation, the tailings dam parameters used in this iteration simulation can be increased, such as increasing displacement, speed, etc., and the increased tailings dam parameters are used for the next simulation until the tailings dam fails, and the size of the tailings dam parameters when the dam fails is taken as the parameter threshold.

[0049] That is, assuming that the tailings dam parameters of the first iteration simulation are parameter 1, if the tailings dam does not fail using parameter 1, parameter 1 is increased to obtain parameter 2, and parameter 2 is re-input into the multi-scale and multi-factor coupled mechanical model for the second iteration, if the tailings dam still does not fail, parameter 2 is increased to obtain parameter 3, and parameter 3 is re-input into the multi-scale and multi-factor coupled mechanical model for the third iteration, if the tailings dam still does not fail, parameter 3 is increased, and so on, and in response to the tailings dam failing during the i-th iteration simulation, the size of parameter i is taken as the parameter threshold.

[0050] S104, based on the prediction simulation result, early warning of the instability of the tailings dam.

[0051] In some implementations, the early warning parameter threshold corresponding to the first multi-source early warning parameter can be determined from the parameter threshold, and the instability of the tailings dam can be early warned according to the size of the real-time early warning parameter and the parameter threshold. Optionally, according to the early warning parameter threshold, it can be judged whether the tailings dam has a risk of instability, if there is a risk of instability, the instability type and instability scale can be determined, and the early warning level can be determined according to the size of the early warning parameter and the parameter threshold, to early warn the instability of the tailings dam.

[0052] In the tailings dam instability trend multi-source information fusion early warning method provided by the embodiments of the present application, the tailings dam parameters are collected, and the first multi-source early warning parameter is determined from the tailings dam parameters, so as to establish a multi-scale multi-factor coupled mechanical model according to the first multi-source early warning parameter and the topography and geomorphology model of the tailings dam. Further, the dam-break process of the tailings dam is simulated and predicted according to the multi-scale multi-factor coupled mechanical model and the tailings dam parameters, and the prediction simulation result is obtained. Further, the instability of the tailings dam can be early warned based on the prediction simulation result. Using the multi-scale multi-factor coupled mechanical model for early warning considers the physical mechanism and interaction of the tailings dam at different scales, and through coupling analysis of multiple factors, the instability trend of the tailings dam can be more accurately predicted, and the early warning accuracy is improved. Further, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technology in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of the early warning are further improved, thereby ensuring the safety of people's life and property and environmental safety.

[0053] Figure 2 is a flow chart of a tailings dam instability trend multi-source information fusion early warning method according to an exemplary embodiment, as shown in Figure 2 The tailings dam instability trend multi-source information fusion early warning method of the embodiments of the present application includes but is not limited to the following steps:

[0054] S201, collecting tailings dam parameters of the tailings dam, and determining a first multi-source early warning parameter from the tailings dam parameters.

[0055] In the embodiments of the present application, the implementation of step S201 can be realized by any one of the embodiments of the present application, which is not limited here and will not be repeated.

[0056] S202, acquiring a panoramic image of the tailings dam, and modeling the tailings dam based on the panoramic image to obtain a three-dimensional entity physical model of the tailings dam as a panoramic model, which is used to describe the geographical and geomorphological information of the tailings dam.

[0057] It can be understood that the topography and geomorphology model of the tailings dam includes the panoramic model and the global model.

[0058] In some implementations, the panoramic image of the tailings dam can be obtained from the tailings dam parameters. For example, the panoramic image of the tailings dam can be determined according to the image data collected by the unmanned aerial vehicle. Further, based on the panoramic image, a three-dimensional entity physical model of the tailings dam can be established based on the digital twin technology, and used as a panoramic model.

[0059] S203, obtain position information of a plurality of tailings dams in a set range, and determine a global model according to the panoramic model of each of the plurality of tailings dams and the position information, the global model being used for positioning the tailings dam.

[0060] Optionally, the coordinate data, that is, the position information, of the plurality of tailings dams in the set range can be monitored based on GPS monitoring. The set range can be a certain administrative region. Optionally, the regional surface information of the plurality of tailings dams can also be obtained based on remote sensing satellite monitoring.

[0061] Further, based on the panoramic model, the position information of the tailings dam, and the surface information, a global model of the tailings dam is established to position the tailings dam.

[0062] S204, establish a multi-scale multi-factor coupled mechanical model of the tailings dam based on the topographic and geomorphic model and the first multi-source early warning parameter.

[0063] In the embodiments of the present application, the implementation of step S204 can be realized by any one of the embodiments of the present application, and here it is not limited, and will not be repeated.

[0064] S205, input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam failure process of the tailings dam, and obtain a prediction simulation result.

[0065] In the embodiments of the present application, the implementation of step S205 can be realized by any one of the embodiments of the present application, and here it is not limited, and will not be repeated.

[0066] S206, based on the prediction simulation result, early warning of the instability of the tailings dam.

[0067] In the embodiments of the present application, the implementation of step S206 can be realized by any one of the embodiments of the present application, and here it is not limited, and will not be repeated.

[0068] In the tailings dam instability trend multi-source information fusion early warning method provided by the embodiments of the present application, tailings dam parameters are collected, and first multi-source early warning parameters are determined from the tailings dam parameters. A multi-scale multi-factor coupled mechanical model is established according to the first multi-source early warning parameters, and a panoramic model and a global model of the tailings dam. Further, the dam-bursting process of the tailings dam is simulated and predicted according to the multi-scale multi-factor coupled mechanical model and the tailings dam parameters, and a prediction simulation result is obtained. Further, the instability of the tailings dam can be early warned based on the prediction simulation result. The multi-scale multi-factor coupled mechanical model is used for early warning, the physical mechanism and interaction of the tailings dam at different scales are considered, and the instability trend of the tailings dam can be more accurately predicted by coupling analysis of multiple factors, thereby improving the early warning accuracy. In turn, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technology in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of early warning are further improved, thereby ensuring the safety of people's life and property and environmental safety.

[0069] Figure 3 FIG. 1 is a flowchart of a tailings dam instability trend multi-source information fusion early warning method according to an example embodiment, as shown in FIG. 1, the tailings dam instability trend multi-source information fusion early warning method of the embodiments of the present application includes but is not limited to the following steps: Figure 3

[0070] S301, tailings dam parameters of the tailings dam are collected, and first multi-source early warning parameters are determined from the tailings dam parameters.

[0071] In the embodiments of the present application, the implementation mode of step S301 can be realized by any one of the embodiments of the present application, which is not limited here and will not be repeated.

[0072] S302, a topographic and geomorphic model of the tailings dam is obtained, and the topographic and geomorphic model includes a panoramic model and a global model.

[0073] In the embodiments of the present application, the implementation mode of step S302 can be realized by any one of the embodiments of the present application, which is not limited here and will not be repeated.

[0074] S303, the panoramic model corresponding to any tailings dam is determined based on the global model, and the tailings dam is regionally divided according to the panoramic model to determine different regions of the tailings dam.

[0075] In some implementations, the global model contains the position information of the plurality of tailings dams and the panoramic model corresponding to the plurality of tailings dams, any tailings dam can be selected from the global model, and the panoramic model corresponding to the tailings dam is determined.

[0076] ​Further, by using the panoramic model to divide the area of any tailings dam, the area division result of any tailings dam is obtained, and the tailings dam is divided into different areas, such as surface area, deep area, head area, tail area, etc.

[0077] In some implementations, the mechanical properties of the tailings sand in different areas of any tailings dam can be obtained, and the mathematical model corresponding to the mechanical properties is determined, so as to couple the first multi-source early warning parameters according to the mechanical properties and the mathematical model, and obtain the coupling equation between the first multi-source early warning parameters.

[0078] In some implementations, the mechanical properties of the tailings sand in different areas of any tailings dam can be obtained, and the mathematical model corresponding to the mechanical properties is determined, so as to couple the first multi-source early warning parameters according to the mechanical properties and the mathematical model, and obtain the coupling equation between the first multi-source early warning parameters.

[0079] Optionally, the soil properties and the solid-liquid mixing control equation of different areas can also be obtained, and the soil properties and the solid-liquid mixing control equation are coupled with the mechanical properties and the mathematical model to couple the first multi-source early warning parameters.

[0080] S305, based on the first multi-source early warning parameter, determine the constraint condition of the model, and based on the coupling equation and the constraint condition, determine the multi-scale multi-factor coupled mechanical model.

[0081] Optionally, the first multi-source early warning parameter can be used as a constraint condition of the model to constrain the output of the model, and then based on the coupling equation and the constraint condition, the multi-scale multi-factor coupled mechanical model can be determined.

[0082] Optionally, before determining the coupling equation, the multi-scale framework of the model can also be determined, and based on the multi-scale framework, the coupling equation and the constraint condition, the multi-scale multi-factor coupled mechanical model can be established.

[0083] S306, input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam failure process of the tailings dam, and obtain the prediction simulation result.

[0084] In the embodiments of the present application, the implementation manner of step S306 can be realized by any one of the embodiments of the present application, which is not limited herein and will not be repeated.

[0085] S307, based on the prediction simulation result, early warning of the instability of the tailings dam.

[0086] In the embodiments of the present application, the implementation manner of step S307 can be realized by any one of the embodiments of the present application, which is not limited herein and will not be repeated.

[0087] In the tailings dam instability trend multi-source information fusion early warning method provided by the embodiment, tailings dam parameters are collected, first multi-source early warning parameters are determined from the tailings dam parameters, coupling equations between the first multi-source early warning parameters are determined based on a panoramic model and a global model, and a multi-scale multi-factor coupled mechanical model is established according to the coupling equations and the first multi-source early warning parameters. Further, the dam collapse process of the tailings dam is simulated and predicted according to the multi-scale multi-factor coupled mechanical model and the tailings dam parameters, and a prediction simulation result is obtained. Further, the instability of the tailings dam can be early warned based on the prediction simulation result. The multi-scale multi-factor coupled mechanical model is used for early warning, the physical mechanisms and interactions of the tailings dam at different scales are considered, and the instability trend of the tailings dam can be more accurately predicted by coupling analysis of multiple factors, thereby improving the early warning accuracy. In turn, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technologies in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of early warning are further improved, thereby ensuring the safety of people's lives and property and environmental safety.

[0088] Figure 4 is a flowchart of a tailings dam instability trend multi-source information fusion early warning method according to an example embodiment, as shown in Figure 4 The tailings dam instability trend multi-source information fusion early warning method of the embodiment includes but is not limited to the following steps:

[0089] S401, tailings dam parameters of a tailings dam are collected, and first multi-source early warning parameters are determined from the tailings dam parameters.

[0090] In the embodiment, the implementation of step S401 can be realized by any of the embodiments of the application, and this is not limited here, and will not be repeated.

[0091] S402, a topographic and geomorphic model of the tailings dam is obtained, and a multi-scale multi-factor coupled mechanical model of the tailings dam is established based on the topographic and geomorphic model and the first multi-source early warning parameters.

[0092] In the embodiment, the implementation of step S402 can be realized by any of the embodiments of the application, and this is not limited here, and will not be repeated.

[0093] S403, the tailings dam parameters are input to the multi-scale multi-factor coupled mechanical model to predict and simulate the dam collapse process of the tailings dam, and a prediction simulation result is obtained.

[0094] In some implementations, the tailings dam parameters can be iteratively simulated by the multi-scale multi-factor coupled mechanical model. That is, for the first iteration simulation, the multi-scale multi-factor coupled mechanical model simulates the collected tailings dam parameters, in response to the tailings dam not failing, the collected tailings dam parameters are increased, and the next iteration simulation is performed based on the increased tailings dam parameters, until the tailings dam fails, and the value of the tailings dam parameters at the time of failure is taken as the parameter threshold, and the prediction simulation result is determined based on the parameter threshold. That is, the parameter threshold is taken as the prediction simulation result.

[0095] For example, assume that the tailings dam parameters of the first iteration simulation are parameter 1, if the tailings dam does not fail using parameter 1, parameter 1 is increased to obtain parameter 2, and parameter 2 is re-input into the multi-scale multi-factor coupled mechanical model for the second iteration, if the tailings dam still does not fail, parameter 2 is increased to obtain parameter 3, and parameter 3 is re-input into the multi-scale multi-factor coupled mechanical model for the third iteration, if the tailings dam still does not fail, parameter 3 is increased, and so on, in response to the tailings dam failing at the i th iteration simulation, the size of parameter i is taken as the parameter threshold.

[0096] S404, determining the parameter threshold corresponding to the first multi-source early warning parameter of the tailings dam from the prediction simulation result.

[0097] S405, real-time collection of the second multi-source early warning parameter of the tailings dam.

[0098] In some implementations, the prediction simulation result includes the parameter threshold corresponding to the tailings dam parameters, by querying the prediction simulation result, the parameter threshold corresponding to the first multi-source early warning parameter can be determined. By using multi-dimensional detection technology, the first multi-source early warning parameter of the tailings dam can be collected in real time, and the first multi-source early warning parameter collected in real time is taken as the second multi-source early warning parameter.

[0099] S406, determining the difference between the second multi-source early warning parameter and the parameter threshold.

[0100] S407, in response to the difference being less than or equal to a set difference threshold, early warning of the instability of the tailings dam.

[0101] Optionally, by calculating the difference between the second multi-source early warning parameter and the parameter threshold, the instability of the tailings dam can be warned according to the size between the difference and the set difference threshold.

[0102] Optionally, the difference threshold can be used to determine whether the tailings dam has a risk of instability, if there is a risk of instability, the type and scale of instability can be determined, and the warning level can be determined according to the size of the difference and the difference threshold, to perform early warning of the instability of the tailings dam.

[0103] For example, assume that the warning levels are divided into levels 1, 2, and 3, where the third level represents the most urgent, and the difference thresholds are threshold A and threshold B. If the difference is less than or equal to threshold A, the warning level is determined to be level 1. If the difference is greater than threshold A and less than or equal to threshold B, the warning level is determined to be level 2. If the difference is greater than threshold B, the warning level is determined to be level 3.

[0104] In the tailings dam instability trend multi-source information fusion early warning method provided by the embodiments, the tailings dam parameters are collected, and the first multi-source early warning parameter is determined from the tailings dam parameters, so as to establish a multi-scale multi-factor coupled mechanical model according to the first multi-source early warning parameter and the topography and geomorphology model of the tailings dam. Further, the dam-bursting process of the tailings dam is simulated and predicted according to the multi-scale multi-factor coupled mechanical model and the tailings dam parameters, and the parameter threshold corresponding to the tailings dam parameters is obtained as a prediction simulation result. Further, the instability of the tailings dam can be early warned based on the prediction simulation result and the real-time second multi-source early warning parameter of the tailings dam. The multi-scale multi-factor coupled mechanical model is used for early warning, the physical mechanism and interaction of the tailings dam at different scales are considered, and the instability trend of the tailings dam can be more accurately predicted by coupling analysis of multiple factors, thereby improving the early warning accuracy. Further, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technology in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of early warning are further improved, thereby ensuring the safety of people's life and property and environmental safety.

[0105] Figure 5 FIG. 1 is a structural diagram of a tailings dam instability trend multi-source information fusion early warning system according to an example embodiment. Figure 5 As shown in FIG. 1, the tailings dam instability trend multi-source information fusion early warning system 100 according to the embodiments includes:

[0106] The monitoring sensor 101, the unmanned aerial vehicle 102, the base station 103, and the tailings dam early warning platform 104, wherein the monitoring sensor 101 and the unmanned aerial vehicle 102 are connected with the base station 103 based on ad hoc networking, and the base station 103 is connected with the tailings dam early warning platform 104 based on wireless networking.

[0107] The monitoring sensor 101 is configured to collect first tailings dam parameters of the tailings dam.

[0108] The unmanned aerial vehicle 102 is configured to collect second tailings dam parameters of the tailings dam.

[0109] The base station 103 is configured to acquire the first tailings dam parameters and the second tailings dam parameters collected by the monitoring sensor and the unmanned aerial vehicle based on ad hoc networking, and take the first tailings dam parameters and the second tailings dam parameters as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platform based on wireless networking.

[0110] The tailing dam early warning platform 104 is configured to perform instability early warning on the tailing dam based on tailing dam parameters and a multi-scale multi-factor coupled mechanical model.

[0111] In some implementations, the monitoring sensor 101 includes a surface sensing monitoring sensor 111, a deep sensing monitoring sensor 112, a remote sensing satellite monitoring sensor 113, a ground-based radar monitoring sensor 114, and a Global Positioning System (GPS) monitoring sensor 115.

[0112] Optionally, the surface sensing monitoring sensor 111 is configured to collect surface displacement, velocity, acceleration, vibration, inclination, and magnetic declination of the tailing dam.

[0113] Optionally, the deep sensing monitoring sensor 112 is configured to collect deep displacement, velocity, acceleration, vibration, inclination, and temperature of the tailing dam.

[0114] Optionally, the remote sensing satellite monitoring sensor 113 is configured to collect images, temperature, and humidity of the tailing dam.

[0115] Optionally, the ground-based radar monitoring sensor 114 is configured to collect displacement, velocity, vibration, and deformation of the tailing dam.

[0116] Optionally, the GPS monitoring sensor 115 is configured to collect absolute displacement, velocity, and acceleration of the tailing dam.

[0117] That is, the first tailing dam parameters can be obtained from the above-mentioned multiple monitoring sensors, wherein the first tailing dam parameters at least include surface displacement, velocity, acceleration, vibration, inclination, magnetic declination, deep displacement, temperature, images, humidity, displacement, deformation, and absolute displacement.

[0118] In some implementations, the unmanned aerial vehicle 102 is further configured to collect image data and infrared data of the tailing dam as second tailing dam parameters.

[0119] In some implementations, when the tailing dam early warning platform 104 obtains tailing dam parameters composed of the first tailing dam parameters and the second tailing dam parameters from the base station 103 based on the ad hoc network, the tailing dam early warning platform 104 is further configured to determine first multi-source early warning parameters from the tailing dam parameters, obtain a panoramic model and a global model in advance, construct a multi-scale multi-factor coupled mechanical model based on the panoramic model and the global model and the first multi-source early warning parameters, input the tailing dam parameters into the multi-scale multi-factor coupled mechanical model, and perform prediction simulation on the dam collapse process of the tailing dam to obtain a prediction simulation result. Then, instability early warning can be performed on the tailing dam according to the prediction simulation result.

[0120] Optionally, the tailings dam parameters are input into the multi-scale multi-factor coupled mechanical model for the first iteration simulation, and in response to the tailings dam not collapsing, the tailings dam parameters are increased and input into the multi-scale multi-factor coupled mechanical model for the next iteration until the tailings dam collapses, and the prediction simulation result is obtained.

[0121] Optionally, the value of the tailings dam parameters at the time of collapse can be taken as a parameter threshold, and the prediction simulation result is determined based on the parameter threshold.

[0122] That is, assuming that the tailings dam parameters of the first iteration simulation are parameter 1, if the tailings dam does not collapse using parameter 1, parameter 1 is increased to obtain parameter 2, and parameter 2 is re-input into the multi-scale multi-factor coupled mechanical model for the second iteration, if the tailings dam still does not collapse, parameter 2 is increased to obtain parameter 3, and parameter 3 is re-input into the multi-scale multi-factor coupled mechanical model for the third iteration, if the tailings dam still does not collapse, parameter 3 is increased, and so on, and in response to the tailings dam collapsing at the i th iteration simulation, parameter i is taken as the parameter threshold.

[0123] The tailings dam instability trend multi-source information fusion early warning system provided by the embodiments of the present application collects tailings dam parameters through monitoring sensors and unmanned aerial vehicles, and transmits them to the tailings dam early warning platform through the base station to early warn the instability of the tailings dam. In the early warning process, the multi-scale multi-factor coupled mechanical model is used, which considers the physical mechanisms and interactions of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be more accurately predicted, and the early warning accuracy is improved. Further, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technology in the tailings dam instability early warning technology is improved, and the accuracy and real-time performance of the early warning are further improved, thereby ensuring the safety of people's lives and property and environmental safety.

[0124] Figure 6 The principle diagram of tailings dam multi-source information fusion early warning is shown, as shown in Figure 6 The base station, the monitoring sensors, the remote sensing satellite, the global navigation satellite receiver, the unmanned aerial vehicle, the ground-based radar, and the photovoltaic panel are provided. The base station and the monitoring devices are powered by the photovoltaic panel and the high-density battery, the information transmission between the monitoring devices and the base station is transmitted through the ad hoc network, the base station collects information and sends it to the tailings dam early warning platform through the wireless network, and the tailings dam early warning platform predicts the tailings dam warning situation through the early warning model and sends it to the user end. Figure 6 The tailings dam early warning platform in the tailings dam early warning platform includes the panoramic model of the tailings dam, the global model, and the multi-scale multi-factor coupled mechanical model.

[0125] Figure 6In specific embodiments, the monitoring sensors include: surface sensing monitoring sensors, deep sensing monitoring sensors, remote sensing satellite monitoring sensors, ground-based radar monitoring sensors, and GPS monitoring sensors.

[0126] Figure 7 A schematic diagram of early warning of a tailings dam is shown. By multi-dimensional monitoring of the tailings dam, tailings dam parameters are obtained, and first multi-source early warning parameters are determined from the tailings dam parameters. Based on image data and infrared data obtained by unmanned aerial vehicle monitoring, a panoramic model of the tailings dam can be established, and based on the panoramic model, tailings dam parameters obtained by remote sensing satellite monitoring and GPS monitoring, a panoramic model can be established. Further, based on the first multi-source early warning parameters, and the panoramic model and the global model, a multi-scale multi-factor coupled mechanical model of the tailings dam can be established, to use the multi-scale multi-factor coupled mechanical model to simulate and predict the dam failure process of the tailings dam, to obtain a prediction simulation result.

[0127] By inputting the tailings dam parameters into the multi-scale multi-factor coupled mechanical model, the tailings dam parameters are iteratively simulated by the multi-scale multi-factor coupled mechanical model to determine the parameter threshold corresponding to the tailings dam parameters, and the parameter threshold is taken as the prediction simulation result. Further, based on the prediction simulation result, and the second multi-source early warning parameters of the tailings dam collected in real time, the instability of the tailings dam can be early warned.

[0128] In order to realize the above-mentioned embodiments, the present application further provides a tailings dam instability trend multi-source information fusion early warning device.

[0129] Figure 8 A structural schematic diagram of a tailings dam instability trend multi-source information fusion early warning device provided by an embodiment of the present application is shown.

[0130] As Figure 8 shown, the tailings dam instability trend multi-source information fusion early warning device 200 includes:

[0131] The acquisition module 201 is configured to acquire tailings dam parameters of a tailings dam, and determine first multi-source early warning parameters from the tailings dam parameters.

[0132] The establishment module 202 is configured to obtain a topographic and geomorphic model of the tailings dam, and based on the topographic and geomorphic model and the first multi-source early warning parameters, establish a multi-scale multi-factor coupled mechanical model of the tailings dam.

[0133] The simulation module 203 is configured to input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model, to perform prediction simulation on the dam failure process of the tailings dam, and obtain a prediction simulation result.

[0134] The early warning module 204 is configured to early warn the instability of the tailings dam based on the prediction simulation result.

[0135] In a possible implementation manner of the embodiment of the present application, the establishing module 202 is further configured to: acquire a panoramic image of the tailings dam, and model the tailings dam based on the panoramic image to obtain a three-dimensional entity physical model of the tailings dam as the panoramic model, wherein the panoramic model is used to describe geographical and topographical information of the tailings dam; acquire position information of a plurality of tailings dams within a set range, and determine the global model according to the panoramic model of each of the plurality of tailings dams and the position information, wherein the global model is used to locate the tailings dam.

[0136] In a possible implementation manner of the embodiment of the present application, the establishing module 202 is further configured to: determine a panoramic model corresponding to any tailings dam based on the global model, and divide the any tailings dam into different regions according to the panoramic model to determine the different regions of the any tailings dam; determine mechanical properties corresponding to the different regions and mathematical models corresponding to the mechanical properties, and couple the first multi-source early warning parameters based on the mechanical properties and the mathematical models to obtain a coupling equation between the first multi-source early warning parameters; determine a constraint condition of the model based on the first multi-source early warning parameters, and determine the multi-scale multi-factor coupled mechanical model based on the coupling equation and the constraint condition.

[0137] In a possible implementation manner of the embodiment of the present application, the simulation module 203 is further configured to: iteratively simulate the tailings dam parameters by using the multi-scale multi-factor coupled mechanical model; for the first iteration simulation, the multi-scale multi-factor coupled mechanical model simulates the collected tailings dam parameters, in response to the tailings dam not collapsing, increases the collected tailings dam parameters, and performs next iteration simulation based on the increased tailings dam parameters until the tailings dam collapses, and takes a value of the tailings dam parameters at the time of collapse as a parameter threshold; and determines the prediction simulation result based on the parameter threshold.

[0138] In a possible implementation manner of the embodiment of the present application, the early warning module 204 is further configured to: determine a parameter threshold corresponding to the first multi-source early warning parameter of the tailings dam from the prediction simulation result; collect a second multi-source early warning parameter of the tailings dam in real time; determine a difference between the second multi-source early warning parameter and the parameter threshold; and in response to the difference being less than or equal to a set difference threshold, early warning is performed on the instability of the tailings dam.

[0139] The tailings dam instability trend multi-source information fusion early warning device provided by the embodiment of the present application comprises a tailings dam parameter acquisition device, a first multi-source early warning parameter determination device, a multi-scale multi-factor coupled mechanical model establishment device, a tailings dam dam-break process simulation and prediction device, and a tailings dam instability early warning device.

[0140] It should be noted that the foregoing explanation and description of the tailings dam instability trend multi-source information fusion early warning method embodiment also applies to the tailings dam instability trend multi-source information fusion early warning device of the embodiment, which will not be described here again.

[0141] In order to achieve the above-mentioned embodiments, the present application further provides an electronic device, comprising a processor and a memory connected with the processor; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to realize the method provided by the foregoing embodiments.

[0142] In order to achieve the above-mentioned embodiments, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to realize the method provided by the foregoing embodiments.

[0143] In order to achieve the above-mentioned embodiments, the present application further provides a computer program product, comprising a computer program, which is executed by a processor to realize the method provided by the foregoing embodiments.

[0144] The collection, storage, use, processing, transmission, provision and application of user personal information involved in the present application comply with relevant laws and regulations and do not violate public order and good customs.

[0145] It is important to note that user's personal information shall be collected for legitimate and reasonable uses of the service and not shared or sold outside of those legitimate uses. Further, such collection / sharing shall occur after receiving the consent of the users, including but not limited to, informing the users to read the user agreement / user notice before using the function, and signing the agreement / authorization including the authorization of relevant user information. In addition, any necessary steps shall be taken to protect and secure access to such personal information data, and ensure that other individuals with access to the personal information data follow their privacy policies and procedures.

[0146] The present application contemplates that the embodiments can provide a user the option to enable or disable the collection of personal information data. In addition, certain data can be anonymized, pseudonymized, or de-identified to protect the privacy of the user. Further, the present application contemplates that the embodiments can provide the user with control over how data is collected about him or her and used by the present application.

[0147] In the preceding embodiments descriptions, the description referring to the terms “one embodiment”, “some embodiments”, “an example”, “a specific example”, or “some examples” etc. means that the particular feature, structure, material or characteristic being described is included in at least one embodiment or example of the present application. The illustrative appearances of the above-mentioned terms in the description are not necessarily referred to the same embodiment or example. Moreover, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples, without mutual

[0148] In addition, the terms “first”, “second”, etc. are used herein only to describe different instances, and do not imply or suggest relative importance or a number of the indicated technical features. Thus, the features defined with “first”, “second” can include at least one of the features, explicitly or implicitly. In the description of the present application, the meaning of “plurality” is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0149] Any process or method descriptions or blocks in flow charts or otherwise described herein can be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are possible. In some embodiments, the processes or methods described in flow charts or otherwise described herein can be tailored by reordering, removing, or adding steps, including according to the functionality involved.

[0150] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions stored in a computer readable medium, which can be executed by an instruction execution system, apparatus or device, such as a computer-based system, a processor-based system, or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or a combination of them. For the purposes of this specification, a "computer readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer readable medium can be a computer readable storage medium or a computer readable signal medium. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, a portable computer diskette (magnetic), a RAM (random access memory), a ROM (read only memory), an EPROM (erasable programmable ROM), EEPROM (electrically erasable programmable ROM), a storage

[0151] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, specifically configured hardware can be used to implement at least some of the functionality described herein. In another embodiment, software or firmware can be used to implement at least some of the functionality described herein, which would be processed by a general purpose computer or processor. Specifically, any of the following technologies, or combinations thereof, can be used to implement at least some of the functionality described herein: discrete logic circuits having logic gates for implementing logic functions upon data signals, application specific integrated circuits having logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and others.

[0152] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0153] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0154] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A tailings dam instability potential multi-source information fusion early warning method, characterized in that, The method comprises: collecting tailing dam parameters of a tailing dam, and determining first multi-source early warning parameters from the tailing dam parameters, the tailing dam parameters including but not limited to surface displacement, velocity, acceleration, vibration, inclination, magnetic declination, deep displacement, temperature, image, humidity, displacement, deformation, absolute displacement, image data and infrared data parameters of the tailing dam; obtaining a topographic and geomorphic model of the tailing dam, and establishing a multi-scale multi-factor coupled mechanical model of the tailing dam based on the topographic and geomorphic model and the first multi-source early warning parameters; inputting the tailing dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate a dam-break process of the tailing dam, and obtaining a prediction simulation result; based on the prediction simulation result, early warning of instability of the tailing dam is performed; the topographic and geomorphic model comprises a panoramic model and a global model, and the obtaining of the topographic and geomorphic model of the tailing dam comprises: obtaining panoramic images of the tailing dam, and modeling the tailing dam based on the panoramic images to obtain a three-dimensional entity physical model of the tailing dam as the panoramic model, the panoramic model being used to describe geographical and topographical information of the tailing dam; obtaining position information of a plurality of tailing dams within a set range, and determining the global model according to the panoramic models of the plurality of tailing dams respectively and the position information, the global model being used to position the tailing dams; the establishing of the multi-scale multi-factor coupled mechanical model of the tailing dam based on the topographic and geomorphic model and the first multi-source early warning parameters comprises: determining a panoramic model corresponding to any tailing dam based on the global model, and dividing the any tailing dam into different regions according to the panoramic model to determine different regions of the any tailing dam; determining mechanical properties corresponding to the different regions and mathematical models corresponding to the mechanical properties, and coupling the first multi-source early warning parameters based on the mechanical properties and the mathematical models to obtain coupling equations between the first multi-source early warning parameters; determining constraint conditions of the model based on the first multi-source early warning parameters, and determining the multi-scale multi-factor coupled mechanical model based on the coupling equations and the constraint conditions.

2. The method of claim 1, wherein, the inputting of the tailing dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam-break process of the tailing dam to obtain the prediction simulation result comprises: iterative simulation of the tailing dam parameters by the multi-scale multi-factor coupled mechanical model; for the first iterative simulation, the multi-scale multi-factor coupled mechanical model simulates the collected tailing dam parameters, in response to the tailing dam not breaking, the collected tailing dam parameters are increased, and the next iterative simulation is performed based on the increased tailing dam parameters until the tailing dam breaks, and a value of the tailing dam parameter at the time of the dam break is taken as a parameter threshold; the prediction simulation result is determined based on the parameter threshold.

3. The method of claim 2, wherein, the early warning of instability of the tailing dam based on the prediction simulation result comprises: determining a parameter threshold corresponding to the first multi-source early warning parameters of the tailing dam from the prediction simulation result; Collecting a second multi-source early warning parameter of the tailings dam in real time; Determining a difference between the second multi-source early warning parameter and the parameter threshold value; In response to the difference being less than or equal to a set difference threshold value, early warning of instability of the tailings dam.

4. A tailings dam instability potential multi-source information fusion early warning system, characterized in that, The method is applied to any one of claims 1-3, comprising: monitoring sensors, unmanned aerial vehicles, base stations, and tailings dam early warning platforms, wherein the monitoring sensors, the unmanned aerial vehicles are connected with the base stations based on ad hoc networks, and the base stations are connected with the tailings dam early warning platforms based on wireless networks; The monitoring sensors are configured to collect first tailings dam parameters of the tailings dam. The unmanned aerial vehicles are configured to collect second tailings dam parameters of the tailings dam. The base stations are configured to acquire the first tailings dam parameters and the second tailings dam parameters collected by the monitoring sensors and the unmanned aerial vehicles based on the ad hoc networks, take the first tailings dam parameters and the second tailings dam parameters as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platforms based on the wireless networks. The tailings dam early warning platforms are configured to perform instability early warning on the tailings dam based on the tailings dam parameters and a constructed multi-scale multi-factor coupled mechanical model.

5. The system of claim 4, wherein, The monitoring sensors comprise surface sensing monitoring sensors, deep sensing monitoring sensors, remote sensing satellite monitoring sensors, ground-based radar monitoring sensors, and global navigation satellite system (GPS) monitoring sensors. The surface sensing monitoring sensors are configured to collect surface displacement, velocity, acceleration, vibration, inclination, and magnetic declination of the tailings dam. The deep sensing monitoring sensors are configured to collect deep displacement, velocity, acceleration, vibration, inclination, and temperature of the tailings dam. The remote sensing satellite monitoring sensors are configured to collect images, temperature, and humidity of the tailings dam. The ground-based radar monitoring sensors are configured to collect displacement, velocity, vibration, and deformation of the tailings dam. The GPS monitoring sensors are configured to collect absolute displacement, velocity, and acceleration of the tailings dam. The first tailings dam parameters at least include surface displacement, velocity, acceleration, vibration, inclination, magnetic declination, deep displacement, temperature, images, humidity, displacement, deformation, and absolute displacement.

6. The system of claim 4, wherein, The unmanned aerial vehicles are further configured to: Collect image data and infrared data of the tailings dam as the second tailings dam parameters.

7. The system of claim 4, wherein, The tailings dam early warning platforms are further configured to: Input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model for the first iteration simulation, in response to the tailings dam not collapsing, increase the tailings dam parameters and input them into the multi-scale multi-factor coupled mechanical model for the next iteration, until the tailings dam collapses, and obtain the prediction simulation result.

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