Multi-source information fusion early warning method for instability situation of tailing dam

Abstract

The invention provides a tailing dam instability situation multi-source information fusion early warning method. The method comprises the steps that tailing dam parameters of a tailing dam are collected, and first multi-source early warning parameters are determined from the tailing dam parameters; obtaining a landform model of the tailing dam, and establishing a multi-scale multi-factor coupling mechanical model of the tailing dam based on the landform model and the first multi-source early warning parameter; inputting the parameters of the tailing dam into a multi-scale multi-factor coupling mechanical model to predict and simulate the dam break process of the tailing dam to obtain a prediction and simulation result; and on the basis of the prediction simulation result, early warning is carried out on instability of the tailing dam. Therefore, according to the scheme, the multi-scale multi-factor coupling mechanical model is used for early warning, physical mechanisms and interaction of the tailing dam on different scales are considered, the instability trend of the tailing dam can be predicted more accurately by performing coupling analysis on multiple factors, and the early warning precision is improved.

Description

Technical Field

[0001] The present application relates to the technical fields of tailings dam safety monitoring and tailings dam early warning, and in particular to a multi-source information fusion early warning method for tailings dam instability situation. Background Art

[0002] Safety monitoring of tailings dams is an important task in the field of non-coal mine safety. Tailings dams may become unstable due to rainfall, weathering, earthquakes, improper construction and maintenance, blasting, seepage and other problems, leading to dam failure, resulting in huge economic losses and casualties.

[0003] Existing tailings dam instability warning technologies are mostly concentrated on specific engineering cases, lack overall integrated monitoring capabilities, have not been systematically studied, and have not formed an integrated monitoring system. Summary of the invention

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

[0005] To this end, the first purpose of this application is to propose a multi-source information fusion early warning method for tailings dam instability situation, so as to realize 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 nature of the early warning.

[0006] The second purpose of this application is to propose a multi-source information fusion early warning system for tailings dam instability.

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

[0008] The fourth objective of the present application is to provide an electronic device.

[0009] A fifth objective of the present application is to provide a computer-readable storage medium.

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

[0011] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a multi-source information fusion early warning method for tailings dam instability situation, including: collecting tailings dam parameters of the tailings dam, and determining a first multi-source early warning parameter from the tailings dam parameters; obtaining a topographic model of the tailings dam, and establishing a multi-scale and multi-factor coupled mechanical model of the tailings dam based on the topographic model and the first multi-source early warning parameter; inputting the tailings dam parameters into the multi-scale and multi-factor coupled mechanical model to predict and simulate the dam break process of the tailings dam to obtain predictive simulation results; based on the predictive simulation results, issuing an early warning of the instability of the tailings dam.

[0012] To achieve the above-mentioned purpose, the second aspect of the present application proposes a multi-source information fusion early warning system for tailings dam instability situation, including: a monitoring sensor, an unmanned aerial vehicle, a base station, and a tailings dam early warning platform, wherein the monitoring sensor and the unmanned aerial vehicle are connected to the base station based on a self-organizing network, and the base station is connected to the tailings dam early warning platform based on a wireless network; the monitoring sensor is used to collect a first tailings dam parameter of the tailings dam; the unmanned aerial vehicle is used to collect a second tailings dam parameter of the tailings dam; the base station is used to obtain the first tailings dam parameter and the second tailings dam parameter collected by the monitoring sensor and the unmanned aerial vehicle based on the self-organizing network, and use the first tailings dam parameter and the second tailings dam parameter as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platform based on the wireless network; the tailings dam early warning platform is used to provide instability early warning for the tailings dam based on the tailings dam parameters and a constructed multi-scale and multi-factor coupled mechanical model.

[0013] To achieve the above-mentioned purpose, the third aspect of the present application proposes a multi-source information fusion early warning device for tailings dam instability situation, including: a collection module, used to collect tailings dam parameters of the tailings dam, and determine a first multi-source early warning parameter from the tailings dam parameters; an establishment module, used to obtain a topographic model of the tailings dam, and based on the topographic model and the first multi-source early warning parameter, establish a multi-scale and multi-factor coupled mechanical model of the tailings dam; a simulation module, used to input the tailings dam parameters into the multi-scale and multi-factor coupled mechanical model to predict and simulate the dam break process of the tailings dam to obtain a predictive simulation result; an early warning module, used to warn of the instability of the tailings dam based on the predictive simulation result.

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

[0015] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer-readable storage medium on which a computer program is stored, and the computer instructions are used to enable the computer to execute the multi-source information fusion early warning method for tailings dam instability situation described in the first aspect of the embodiment.

[0016] To achieve the above-mentioned purpose, the sixth embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the multi-source information fusion early warning method for tailings dam instability situation described in the above-mentioned first embodiment.

[0017] The multi-source information fusion early warning method for the instability of tailings dams provided in the present application collects tailings dam parameters and determines the first multi-source early warning parameters from the tailings dam parameters, so as to establish a multi-scale multi-factor coupling mechanical model according to the first multi-source early warning parameters and the topographic model of the tailings dam. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted to obtain the prediction simulation results. Further, the instability of the tailings dam can be warned based on the prediction simulation results. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately and the early warning accuracy can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, which improves the diversity of monitoring technologies in the instability early warning technology of the tailings dam, and further improves the accuracy and real-time nature of the early warning, thereby ensuring the safety of people's lives and property and environmental safety.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] Figure 1 A schematic diagram of a process of a multi-source information fusion early warning method for tailings dam instability situation provided in an embodiment of the present application;

[0021] Figure 2 A schematic flow chart of another method for multi-source information fusion early warning of tailings dam instability provided in an embodiment of the present application;

[0022] Figure 3 A schematic flow chart of another method for multi-source information fusion early warning of tailings dam instability provided in an embodiment of the present application;

[0023] Figure 4 A schematic flow chart of another method for multi-source information fusion early warning of tailings dam instability provided in an embodiment of the present application;

[0024] Figure 5 A schematic diagram of the structure of a multi-source information fusion early warning system for tailings dam instability provided in an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the principle of multi-source information fusion warning for tailings dams provided in an embodiment of the present application;

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

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

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the multi-source information fusion early warning method for tailings dam instability situation according to an embodiment of the present application with reference to the accompanying drawings.

[0030] Figure 1 is a flow chart of a method for fusion early warning of tailings dam instability situation multi-source information according to an exemplary embodiment. Figure 1 As shown, the multi-source information fusion early warning method for tailings dam instability situation in 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 a first multi-source early warning parameter from the tailings dam parameters.

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

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

[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, drone monitoring, ground-based radar monitoring, Global Positioning System (GPS) monitoring and other monitoring methods.

[0035] Among them, surface sensing monitoring installs integrated sensing equipment on the surface of the tailings dam. The equipment integrates three-axis inclination, velocity, acceleration, vibration, and three-axis magnetic declination sensors to measure the kinematic and dynamic parameters of the tailings dam surface. Surface sensing monitoring provides parameters such as surface displacement, velocity, acceleration, vibration, inclination, and magnetic declination.

[0036] Among them, deep sensing monitoring installs chain-type integrated sensing equipment in the deep part of the tailings dam. The equipment integrates three-axis inclination, velocity, acceleration, vibration, and temperature sensors to measure the kinematic and dynamic parameters of the deep part of the tailings dam. Deep sensing monitoring provides parameters such as deep displacement, velocity, acceleration, vibration, inclination, and temperature.

[0037] Among them, remote sensing satellite monitoring uses remote sensing technology to obtain surface information of the tailings dam area through satellites or other carriers. Remote sensing satellite monitoring provides parameters such as images, temperature, and humidity.

[0038] Among them, drone monitoring is carried out by installing a base station on the tailings dam and deploying unmanned aerial vehicles in it. Drone monitoring provides parameters such as tailings dam image data and infrared data.

[0039] Among them, ground-based radar monitoring uses microwave signals for non-contact scanning measurement through a planar scanning unit, obtains high spatial resolution and monitoring accuracy, and combines stepped frequency continuous wave technology and interferometric measurement technology to conduct static and dynamic high-precision monitoring of tailings dams. Ground-based radar monitoring provides parameters such as displacement, velocity, vibration, and deformation.

[0040] Among them, GPS monitoring obtains the three-dimensional coordinate change data of the monitoring points of the tailings dam by deploying Global Navigation Satellite System (GNSS) receivers at the tailings dam. GPS monitoring provides parameters such as absolute displacement, velocity, and acceleration.

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

[0042] S102, obtaining a topographic model of the tailings dam, and establishing a multi-scale and multi-factor coupled mechanical model of the tailings dam based on the topographic model and the first multi-source early warning parameter.

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

[0044] Furthermore, the tailings dam is divided into different areas according to the topographic model, such as the surface area, the deep area, the head area, the tail area, etc., and the mechanical properties corresponding to the different areas are determined, so as to couple the first multivariate warning parameter according to the mechanical properties, determine the coupling equation, and then establish a multi-scale multi-factor coupling mechanical model of the tailings dam according to the topographic model and the coupling equation. For example, the first multivariate warning parameter can be coupled according to the mechanical properties of the tailings sand in different areas.

[0045] In other words, the appearance of the multi-scale and multi-factor coupled mechanical model of the tailings dam is provided by the topographic model, and the interaction between the tailings dam parameters is provided by the coupling equations, thus forming a multi-scale and multi-factor coupled mechanical model that can simulate the break of the tailings dam.

[0046] S103, inputting the tailings dam parameters into a multi-scale multi-factor coupled mechanical model to perform a predictive simulation on the tailings dam breach process to obtain a predictive simulation result.

[0047] In some implementations, the tailings dam parameters are input into a multi-scale and multi-factor coupled mechanical model, which iteratively simulates the dam break process based on the tailings dam parameters to determine parameter thresholds of the tailings dam parameters, and uses the parameter thresholds as predictive simulation results.

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

[0049] That is to say, let the tailings dam parameter of the first iterative simulation be parameter 1. If the tailings dam does not break when using parameter 1, then 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 break, then 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 break, then parameter 3 is increased… In response to the tailings dam breaking during the i-th iterative simulation, the size of parameter i is used as the parameter threshold.

[0050] S104: Based on the prediction simulation results, an early warning is issued for instability of the tailings dam.

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

[0052] In the multi-source information fusion early warning method for the instability of the tailings dam provided in the embodiment of the present application, by collecting the tailings dam parameters and determining the first multi-source early warning parameters from the tailings dam parameters, a multi-scale multi-factor coupling mechanical model is established according to the first multi-source early warning parameters and the topographic model of the tailings dam. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted to obtain the prediction simulation results. Further, the instability of the tailings dam can be warned based on the prediction simulation results. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately, and the accuracy of the early warning can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, which improves the diversity of monitoring technologies in the instability early warning technology of the tailings dam, and further improves the accuracy and real-time nature of the early warning, thereby ensuring the safety of people's lives and property and environmental safety.

[0053] Figure 2 is a flow chart of a method for fusion early warning of tailings dam instability situation multi-source information according to an exemplary embodiment. Figure 2 As shown, the multi-source information fusion early warning method for tailings dam instability situation in the embodiment 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 embodiment of the present application, the implementation method of step S201 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

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

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

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

[0059] S203, obtaining location information of multiple tailings dams within a set range, and determining a global model based on respective panoramic models and location information of the multiple tailings dams, wherein the global model is used to locate the tailings dams.

[0060] Optionally, the coordinate data, that is, the location information, of multiple tailings dams within a set range can be monitored based on GPS monitoring. The set range can be a certain administrative district. Optionally, the surface information of multiple tailings dam areas can also be obtained based on remote sensing satellite monitoring.

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

[0062] S204, based on the topographic model and the first multi-source warning parameters, a multi-scale and multi-factor coupled mechanical model of the tailings dam is established.

[0063] In the embodiment of the present application, the implementation method of step S204 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0064] S205, inputting the tailings dam parameters into a multi-scale multi-factor coupled mechanical model to perform a predictive simulation on the tailings dam breach process to obtain a predictive simulation result.

[0065] In the embodiment of the present application, the implementation method of step S205 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0066] S206: Based on the prediction simulation results, an early warning is issued for instability of the tailings dam.

[0067] In the embodiment of the present application, the implementation method of step S206 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0068] In the multi-source information fusion early warning method for the instability of tailings dams provided in the embodiment of the present application, by collecting tailings dam parameters and determining the first multi-source early warning parameters from the tailings dam parameters, by obtaining the panoramic model and the global model of the tailings dam, a multi-scale multi-factor coupling mechanical model is established according to the first multi-source early warning parameters, as well as the panoramic model and the global model of the tailings dam. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted to obtain the prediction simulation results. Further, the instability of the tailings dam can be warned based on the prediction simulation results. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately, and the early warning accuracy can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technologies in the instability early warning technology of the tailings dam is improved, and the accuracy and real-time nature of the early warning are further improved, thereby ensuring the safety of people's lives and property and environmental safety.

[0069] Figure 3 is a flow chart of a method for fusion early warning of tailings dam instability situation multi-source information according to an exemplary embodiment. Figure 3 As shown, the multi-source information fusion early warning method for tailings dam instability situation in the embodiment of the present application includes but is not limited to the following steps:

[0070] S301, collecting tailings dam parameters of the tailings dam, and determining a first multi-source early warning parameter from the tailings dam parameters.

[0071] In the embodiment of the present application, the implementation method of step S301 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0072] S302, obtaining a topographic model of the tailings dam, the topographic model including a panoramic model and a global model.

[0073] In the embodiment of the present application, the implementation method of step S302 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0074] S303, determining a panoramic model corresponding to any tailings dam based on the global model, and dividing any tailings dam into regions according to the panoramic model to determine different regions of any tailings dam.

[0075] In some implementations, the global model includes location information of multiple tailings dams and panoramic models corresponding to the multiple tailings dams. Any tailings dam can be selected from the global model and the panoramic model corresponding to the tailings dam can be determined.

[0076] Furthermore, by using the panoramic model to divide any tailings dam into regions, the regional division results of any tailings dam are obtained, and the tailings dam is divided into different regions, such as the surface region, the deep region, the head region, the tail region, etc.

[0077] S304, determining mechanical properties corresponding to different regions and mathematical models corresponding to the mechanical properties, and coupling the first multi-source warning parameters based on the mechanical properties and the mathematical model to obtain coupling equations between the first multi-source warning parameters.

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

[0079] Optionally, the soil properties and solid-liquid mixing control equations of different regions may be obtained, and the soil properties and solid-liquid mixing control equations may be coupled with the mechanical properties and mathematical models to couple the first multi-source warning parameters.

[0080] S305, based on the first multi-source warning parameter, determine the constraints of the model, and based on the coupling equations and the constraints, determine the multi-scale multi-factor coupling mechanical model.

[0081] Optionally, the first multi-source 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 equations and the constraint conditions, a multi-scale and multi-factor coupled mechanical model can be determined.

[0082] Optionally, before determining the coupling equations, the multi-scale framework of the model may also be determined, and based on the multi-scale framework, coupling equations and constraints, a multi-scale and multi-factor coupling mechanical model may be established.

[0083] S306, inputting the tailings dam parameters into a multi-scale multi-factor coupled mechanical model to perform a predictive simulation on the tailings dam breach process to obtain a predictive simulation result.

[0084] In the embodiment of the present application, the implementation method of step S306 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0085] S307: Based on the prediction simulation results, an early warning is issued for the instability of the tailings dam.

[0086] In the embodiment of the present application, the implementation method of step S307 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0087] In the multi-source information fusion early warning method for the instability of the tailings dam provided in the embodiment of the present application, the tailings dam parameters are collected, and the first multi-source early warning parameters are determined from the tailings dam parameters, and the coupling equations between the first multi-source early warning parameters are determined based on the panoramic model and the global model, and a multi-scale multi-factor coupling mechanical model is established according to the coupling equation and the first multi-source early warning parameters. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted to obtain the prediction simulation results. Further, the instability of the tailings dam can be warned based on the prediction simulation results. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction 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 can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technologies in the early warning technology of the instability of the tailings dam is improved, and the accuracy and real-time nature of the early warning are further improved, thereby ensuring the safety of people's lives and property and environmental safety.

[0088] Figure 4 is a flow chart of a method for fusion early warning of tailings dam instability situation multi-source information according to an exemplary embodiment. Figure 4 As shown, the multi-source information fusion early warning method for tailings dam instability situation in the embodiment of the present application includes but is not limited to the following steps:

[0089] S401, collecting tailings dam parameters of the tailings dam, and determining a first multi-source early warning parameter from the tailings dam parameters.

[0090] In the embodiment of the present application, the implementation method of step S401 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0091] S402, obtaining a topographic model of the tailings dam, and establishing a multi-scale and multi-factor coupled mechanical model of the tailings dam based on the topographic model and the first multi-source warning parameters.

[0092] In the embodiment of the present application, the implementation method of step S402 can be implemented by any method in the embodiments of the present application, which is not limited here and will not be repeated.

[0093] S403, inputting the tailings dam parameters into a multi-scale multi-factor coupled mechanical model to perform a predictive simulation on the tailings dam breach process to obtain a predictive simulation result.

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

[0095] As an example, assume that the tailings dam parameter of the first iterative simulation is parameter 1. If the tailings dam does not burst when parameter 1 is used, 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 burst, 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 burst, parameter 3 is increased… In response to the tailings dam bursting during the i-th iterative simulation, the size of parameter i is used as the parameter threshold.

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

[0097] S405, collecting the second multi-source warning parameters of the tailings dam in real time.

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

[0099] S406: Determine the difference between the second multi-source warning parameter and the parameter threshold.

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

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

[0102] Optionally, the difference threshold can be used to determine whether the tailings dam is at 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 based on the difference and the difference threshold to provide an instability warning for the tailings dam.

[0103] For example, the warning levels are divided into level one, level two and level three, where the level three warning level is the most urgent, and the difference thresholds are threshold A and threshold B respectively. If the difference is less than or equal to threshold A, the warning level is determined to be level one; if the difference is greater than threshold A and less than or equal to threshold B, the warning level is determined to be level two; if the difference is greater than threshold B, the warning level is determined to be level three.

[0104] In the multi-source information fusion early warning method for the instability of tailings dams provided in the embodiment of the present application, by collecting tailings dam parameters and determining the first multi-source early warning parameters from the tailings dam parameters, a multi-scale multi-factor coupling mechanical model is established according to the first multi-source early warning parameters and the topographic model of the tailings dam. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted, and the parameter threshold corresponding to the tailings dam parameters is obtained as the prediction simulation result. Further, the instability of the tailings dam can be warned based on the prediction simulation results and the real-time second multi-source early warning parameters of the tailings dam. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately, and the early warning accuracy can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, the diversity of monitoring technologies in the early warning technology of tailings dam instability can be improved, and the accuracy and real-time nature of the early warning can be further improved, thereby ensuring the safety of people's lives and property and environmental safety.

[0105] Figure 5 is a structural diagram of a tailings dam instability situation multi-source information fusion early warning system according to an exemplary embodiment. Figure 5 As shown, the tailings dam instability situation multi-source information fusion early warning system 100 of the embodiment of the present application includes:

[0106] Monitoring sensor 101, drone 102, base station 103, tailings dam early warning platform 104, wherein the monitoring sensor 101 and drone 102 are connected to the base station 103 based on an ad hoc network, and the base station 103 is connected to the tailings dam early warning platform 104 based on a wireless network.

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

[0108] The drone 102 is used to collect the second tailings dam parameters of the tailings dam.

[0109] Base station 103 is used to obtain the first tailings dam parameter and the second tailings dam parameter collected by monitoring sensors and drones based on an ad hoc network, and use the first tailings dam parameter and the second tailings dam parameter as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platform based on a wireless network.

[0110] The tailings dam early warning platform 104 is used to provide instability early warning for the tailings dam based on the tailings dam parameters and the constructed multi-scale and 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 navigation satellite system (GPS) monitoring sensor 115 .

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

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

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

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

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

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

[0118] In some implementations, the drone 102 is further used to collect image data and infrared data of the tailings dam as the second tailings dam parameters.

[0119] In some implementations, when the tailings dam early warning platform 104 obtains the tailings dam parameters consisting of the first tailings dam parameters and the second tailings dam parameters from the base station 103 based on the self-organizing network, the tailings dam early warning platform 104 is further used to: determine the first multi-source early warning parameter from the tailings dam parameters, obtain the pre-constructed panoramic model and global model, and construct a multi-scale multi-factor coupling mechanical model based on the panoramic model and the global model, as well as the first multi-source early warning parameter, and input the tailings dam parameters into the multi-scale multi-factor coupling mechanical model to predict and simulate the dam-breaking process of the tailings dam to obtain the prediction simulation results. Then, according to the prediction simulation results, the tailings dam can be warned of instability.

[0120] Optionally, the tailings dam parameters are input into a multi-scale and multi-factor coupled mechanical model for the first iterative simulation. In response to the tailings dam not breaking, the tailings dam parameters are increased and input into the multi-scale and multi-factor coupled mechanical model for the next iteration until the tailings dam breaks, thereby obtaining a predicted simulation result.

[0121] Optionally, the value of the tailings dam parameter at the time of dam breach may be used as a parameter threshold, and the prediction simulation result may be determined based on the parameter threshold.

[0122] That is to say, let the tailings dam parameter of the first iterative simulation be parameter 1. If the tailings dam does not break when using parameter 1, then 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 break, then 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 break, then parameter 3 is increased… In response to the tailings dam breaking during the i-th iterative simulation, parameter i is used as the parameter threshold.

[0123] In the multi-source information fusion early warning system for the instability of tailings dams provided in the embodiment of the present application, the parameters of the tailings dams are collected by monitoring sensors and drones, and transmitted to the tailings dam early warning platform through base stations to warn of the instability of the tailings dams. In the early warning process, a multi-scale and multi-factor coupling mechanical model is used, which takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately and the accuracy of the early warning can be improved. In this way, high-precision integrated monitoring and early warning of tailings dams can be achieved, which improves the diversity of monitoring technologies in the early warning technology of tailings dam instability, and further improves the accuracy and real-time nature of early warning, thereby ensuring the safety of people's lives and property and the environment.

[0124] Figure 6 The figure shows the principle diagram of multi-source information fusion warning of tailings dam. Figure 6 As shown, a base station, several monitoring sensors, a remote sensing satellite and global navigation satellite receiver, a drone, a ground-based radar, and several photovoltaic panels are set up. The base station and monitoring equipment are powered by photovoltaic panels and high-density batteries, and the information transmission between monitoring equipment and base stations is transmitted through a self-organizing network. The base station collects information and sends it to the tailings dam early warning platform through a wireless network. The tailings dam early warning platform uses the early warning model to forecast the tailings dam warning situation and send it to the user end. Figure 6 The tailings dam early warning platform includes a panoramic model, a global model, and a multi-scale and multi-factor coupled mechanical model of the tailings dam.

[0125] Figure 6The 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 The figure shows a schematic diagram of early warning for tailings dams. The tailings dam is monitored in multiple dimensions to obtain tailings dam parameters, and the first multi-source early warning parameters are determined from the tailings dam parameters. Based on the image data and infrared data acquired by drone monitoring, a panoramic model of the tailings dam can be established, and based on the panoramic model, the tailings dam parameters acquired by remote sensing satellite monitoring and GPS monitoring, a panoramic model can be established. Furthermore, based on the first multi-source early warning parameter, as well as the panoramic model and the global model, a multi-scale multi-factor coupled mechanical model of the tailings dam can be established, so as to use the multi-scale multi-factor coupled mechanical model to simulate and predict the dam break process of the tailings dam to obtain the prediction simulation results.

[0127] By inputting the tailings dam parameters into the multi-scale multi-factor coupled mechanical model, the multi-scale multi-factor coupled mechanical model iteratively simulates the tailings dam parameters to determine the parameter thresholds corresponding to the tailings dam parameters, and the parameter thresholds are used as the prediction simulation results. Furthermore, based on the prediction simulation results and the second multi-source warning parameters of the tailings dam collected in real time, the instability of the tailings dam can be warned.

[0128] In order to implement the above-mentioned embodiment, the present application also proposes a multi-source information fusion early warning device for tailings dam instability situation.

[0129] Figure 8 A schematic diagram of the structure of a multi-source information fusion early warning device for tailings dam instability provided in an embodiment of the present application.

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

[0131] A collection module 201 is used to collect tailings dam parameters of the tailings dam and determine a first multi-source early warning parameter from the tailings dam parameters;

[0132] Establishing module 202, for acquiring a topographic model of the tailings dam, and establishing a multi-scale and multi-factor coupled mechanical model of the tailings dam based on the topographic model and the first multi-source early warning parameters;

[0133] A simulation module 203 is used to input the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to perform a prediction simulation on the dam-breaking process of the tailings dam to obtain a prediction simulation result;

[0134] The early warning module 204 is used to issue an early warning of the instability of the tailings dam based on the prediction simulation results.

[0135] In a possible implementation of an embodiment of the present application, the establishment module 202 is also used to: obtain a panoramic image of the tailings dam, and model the tailings dam based on the panoramic image to obtain a three-dimensional solid physical model of the tailings dam as the panoramic model, and the panoramic model is used to describe the geographical and geomorphic information of the tailings dam; obtain the location information of multiple tailings dams within a set range, and determine the global model based on the panoramic models of each of the multiple tailings dams and the location information, and the global model is used to locate the tailings dam.

[0136] In a possible implementation method of an embodiment of the present application, the establishment module 202 is also used to: determine a panoramic model corresponding to any tailings dam based on the global model, and divide the any tailings dam into regions according to the panoramic model to determine different regions of the any tailings dam; determine the mechanical properties corresponding to the different regions, and the mathematical model corresponding to the mechanical properties, and based on the mechanical properties and the mathematical model, couple the first multi-source warning parameters to obtain the coupling equations between the first multi-source warning parameters; based on the first multi-source warning parameters, determine the constraints of the model, and based on the coupling equations and the constraints, determine the multi-scale and multi-factor coupled mechanical model.

[0137] In a possible implementation of the embodiment of the present application, the simulation module 203 is also used to: iteratively simulate the tailings dam parameters by the multi-scale and multi-factor coupled mechanical model; for the first iterative simulation, the multi-scale and multi-factor coupled mechanical model simulates the collected tailings dam parameters, and in response to the tailings dam not bursting, the collected tailings dam parameters are increased, and the next iterative simulation is performed based on the increased tailings dam parameters until the tailings dam bursts, and the value of the tailings dam parameters at the time of the burst is used as the parameter threshold; the predicted simulation result is determined based on the parameter threshold.

[0138] In a possible implementation of an embodiment of the present application, the early warning module 204 is also used to: determine a parameter threshold corresponding to a first multi-source early warning parameter of the tailings dam from the prediction simulation results; collect a second multi-source early warning parameter of the tailings dam in real time; determine the 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, issue an early warning of instability of the tailings dam.

[0139] In the multi-source information fusion early warning device for the instability of the tailings dam provided in the embodiment of the present application, by collecting the tailings dam parameters and determining the first multi-source early warning parameters from the tailings dam parameters, a multi-scale multi-factor coupling mechanical model is established according to the first multi-source early warning parameters and the topographic model of the tailings dam. Further, according to the multi-scale multi-factor coupling mechanical model and the tailings dam parameters, the dam-breaking process of the tailings dam is simulated and predicted to obtain the prediction simulation results. Further, the instability of the tailings dam can be warned based on the prediction simulation results. The use of the multi-scale multi-factor coupling mechanical model for early warning takes into account the physical mechanism and interaction of the tailings dam at different scales. By coupling and analyzing multiple factors, the instability trend of the tailings dam can be predicted more accurately, and the early warning accuracy can be improved. In addition, high-precision integrated monitoring and early warning of the tailings dam can be realized, which improves the diversity of monitoring technologies in the instability early warning technology of the tailings dam, and further improves the accuracy and real-time nature of the early warning, thereby ensuring the safety of people's lives and property and environmental safety.

[0140] It should be noted that the above explanation of the embodiment of the method for early warning of the instability situation of tailings dam by multi-source information fusion is also applicable to the early warning device for instability situation of tailings dam by multi-source information fusion of this embodiment, and will not be repeated here.

[0141] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0142] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0143] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0144] The collection, storage, use, processing, transmission, provision and application of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0145] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0146] This application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, this application is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, risks can be minimized by limiting data collection and deleting data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of users.

[0147] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0148] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0149] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0150] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0151] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0152] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0153] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0154] The storage medium mentioned above may 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A multi-source information fusion early warning method for tailings dam instability, characterized in that: The method comprises: collecting tailings dam parameters of the tailings dam, and determining a first multi-source early warning parameter from the tailings dam parameters; Acquire a topographic model of the tailings dam, and establish a multi-scale and multi-factor coupled mechanical model of the tailings dam based on the topographic model and the first multi-source early warning parameters; Inputting the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam-breaking process of the tailings dam to obtain prediction simulation results; Based on the prediction simulation results, an early warning is issued for the instability of the tailings dam.

2. The method according to claim 1, characterized in that The topographic model includes a panoramic model and a global model. The step of obtaining the topographic model of the tailings dam includes: Acquire a panoramic image of the tailings dam, and model the tailings dam based on the panoramic image to obtain a three-dimensional solid physical model of the tailings dam as the panoramic model, wherein the panoramic model is used to describe the geographical and geomorphic information of the tailings dam; The location information of multiple tailings dams within a set range is obtained, and the global model is determined according to the respective panoramic models of the multiple tailings dams and the location information, and the global model is used to locate the tailings dams.

3. The method according to claim 2, characterized in that The multi-scale and multi-factor coupled mechanical model of the tailings dam is established based on the topographic model and the first multi-source early warning parameter, including: Determine a panoramic model corresponding to any tailings dam based on the global model, and divide the tailings dam into regions according to the panoramic model to determine different regions of the tailings dam; Determining mechanical properties corresponding to the different regions and mathematical models corresponding to the mechanical properties, and coupling the first multi-source warning parameters based on the mechanical properties and the mathematical model to obtain coupling equations between the first multi-source warning parameters; Based on the first multi-source warning parameters, the constraints of the model are determined, and based on the coupling equations and the constraints, the multi-scale and multi-factor coupled mechanical model is determined.

4. The method according to claim 1, characterized in that: The inputting the tailings dam parameters into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam-breaking process of the tailings dam to obtain the prediction simulation results includes: Iteratively simulating the tailings dam parameters by using the multi-scale multi-factor coupled mechanical model; For the first iterative simulation, the multi-scale multi-factor coupled mechanical model simulates the collected tailings dam parameters, and in response to the tailings dam not breaking, the collected tailings dam parameters are increased, and the next iterative simulation is performed based on the increased tailings dam parameters until the tailings dam breaks, and the value of the tailings dam parameter at the time of the break is used as the parameter threshold; The predicted simulation result is determined based on the parameter threshold.

5. The method according to claim 4, characterized in that The step of providing an early warning of instability of the tailings dam based on the prediction simulation result includes: Determining a parameter threshold corresponding to a first multi-source early warning parameter of the tailings dam from the prediction simulation result; collecting second multi-source early warning parameters of the tailings dam in real time; Determining a difference between the second multi-source warning parameter and the parameter threshold; In response to the difference being less than or equal to a set difference threshold, an early warning is issued for instability of the tailings dam.

6. A tailings dam instability situation multi-source information fusion early warning system, characterized in that: include: Monitoring sensors, drones, base stations, and tailings dam early warning platforms, wherein the monitoring sensors and drones are connected to the base stations based on a self-organizing network, and the base stations are connected to the tailings dam early warning platforms based on a wireless network; The monitoring sensor is used to collect the first tailings dam parameter of the tailings dam; The drone is used to collect second tailings dam parameters of the tailings dam; The base station is used to obtain the first tailings dam parameter and the second tailings dam parameter collected by the monitoring sensor and the drone based on the ad hoc network, and use the first tailings dam parameter and the second tailings dam parameter as tailings dam parameters, and send the tailings dam parameters to the tailings dam early warning platform based on the wireless network; The tailings dam early warning platform is used to provide instability early warning for the tailings dam based on the tailings dam parameters and the constructed multi-scale and multi-factor coupled mechanical model.

7. The system according to claim 6, characterized in that The monitoring sensors include: 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 sensor is used to collect the surface displacement, velocity, acceleration, vibration, inclination and magnetic declination of the tailings dam; The deep sensing monitoring sensor is used to collect the deep displacement, velocity, acceleration, vibration, inclination and temperature of the tailings dam; The remote sensing satellite monitoring sensor is used to collect images, temperature and humidity of the tailings dam; The ground-based radar monitoring sensor is used to collect the displacement, speed, vibration and deformation of the tailings dam; The GPS monitoring sensor is used to collect the absolute displacement, velocity and acceleration of the tailings dam; Among them, the first tailings dam parameters include at least: surface displacement, velocity, acceleration, vibration, inclination, magnetic declination, deep displacement, temperature, image, humidity, displacement, deformation, and absolute displacement.

8. The system according to claim 6, characterized in that The drone is also used for: The image data and infrared data of the tailings dam are collected as the parameters of the second tailings dam.

9. The system according to claim 6, characterized in that The tailings dam early warning platform is also used for: Determining a first multi-source early warning parameter from the tailings dam parameters; Based on the panoramic model and the global model, and the first multi-source early warning parameters, the multi-scale multi-factor coupled mechanical model is constructed, and the tailings dam parameters are input into the multi-scale multi-factor coupled mechanical model to predict and simulate the dam break process of the tailings dam to obtain prediction simulation results; According to the prediction simulation results, an instability warning is issued for the tailings dam.

10. The system according to claim 9, characterized in that The tailings dam early warning platform is also used for: The tailings dam parameters are input into the multi-scale multi-factor coupled mechanical model for the first iterative simulation. In response to the tailings dam not breaking, 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 breaks, thereby obtaining the predicted simulation results.

Images