Tailings dam deformation early warning method, device, electronic equipment and storage medium

Abstract

The present disclosure proposes a tailings dam deformation early warning method, device, electronic device and storage medium, including: obtaining the time series monitoring data and the first time series monitoring value of the target tailings dam at multiple target monitoring nodes; determining the maximum correlation based on the time series monitoring data and the first time series monitoring value; in response to the maximum correlation being greater than or equal to the correlation threshold, identifying the state of the target tailings dam; in response to the safety factor being less than or equal to the safety factor threshold, performing a rolling prediction on the target tailings dam; determining the risk occurrence level based on the creep curve, and determining the risk level based on the second time series monitoring value, and generating a risk warning based on the risk level and the risk occurrence level. Through the comprehensive monitoring and early warning system disclosed in the present disclosure, not only can a variety of monitoring means be realized for coordinated monitoring, so that various monitoring technologies are complemented by their advantages, but also all-round deformation monitoring of the tailings dam points, lines and surfaces can be realized, and accurate prediction and early warning of future deformation trends can be made.

Description

Technical Field

[0001] The present disclosure relates to the technical field of tailings dam monitoring, and in particular to a tailings dam deformation early warning method, device, electronic equipment and storage medium. Background Art

[0002] In current technology, deformation monitoring of tailings dams is basically carried out based on relatively single means, such as internal displacement sensor monitoring, satellite monitoring, drone monitoring, radar monitoring, and GNSS monitoring. Each monitoring method has its own advantages and limitations. Satellite sensors can provide high-resolution image data. By processing and analyzing these data, surface deformation information, including settlement, displacement, etc., can be obtained to determine whether there are potential safety risks in the tailings dam. Drone monitoring can not only obtain more detailed surface deformation information, but also realize fixed-point monitoring of specific areas, which is more targeted. Global Navigation Satellite System (GNSS) monitoring has the characteristics of high precision and strong real-time performance, and can provide accurate location information. By comparing and analyzing the measurement data at different time points, the trend and change of surface deformation can be found, and potential safety risks can be warned. Ground radar monitoring can monitor the slight deformation of the surface in real time, including settlement, cracks, etc. Through antenna arrays and data processing algorithms, high-precision deformation monitoring of tailings dams and surrounding areas can be achieved. Summary of the invention

[0003] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0004] To this end, one purpose of the present disclosure is to provide a tailings dam deformation early warning method.

[0005] The second objective of the present disclosure is to provide a tailings dam deformation early warning device.

[0006] A third objective of the present disclosure is to provide an electronic device.

[0007] A fourth object of the present disclosure is to provide a non-transitory computer-readable storage medium.

[0008] A fifth object of the present disclosure is to provide a computer program product.

[0009] To achieve the above-mentioned purpose, the first aspect of the present disclosure proposes a tailings dam deformation warning method, including: obtaining time-series monitoring data of a target tailings dam at multiple target monitoring nodes, and obtaining first time-series monitoring values ​​of the target tailings dam by multiple monitoring means; determining the maximum correlation of each target monitoring node based on the time-series monitoring data and the first time-series monitoring value; in response to the maximum correlation of any target monitoring node being greater than or equal to a correlation threshold, performing state identification on the target tailings dam to determine the safety factor of the target tailings dam; in response to the safety factor being less than or equal to the safety factor threshold, performing a rolling prediction on the target tailings dam to generate a creep curve of the target tailings dam and second time-series monitoring values ​​of multiple monitoring means at a time stamp to be predicted; determining the risk occurrence level of the target tailings dam at a time stamp to be predicted based on the creep curve, and determining the risk level of the target tailings dam at a time stamp to be predicted based on the second time-series monitoring value, and generating a risk warning based on the risk level and the risk occurrence level.

[0010] According to one embodiment of the present disclosure, determining the risk occurrence level of the target tailings dam at the time stamp to be predicted based on the creep curve includes: for any time stamp to be predicted, obtaining the tangent angle of the creep curve at the point corresponding to the time stamp to be predicted; matching the tangent angle with a preset tangent angle-risk level mapping relationship to determine the risk occurrence level of the target tailings dam.

[0011] According to one embodiment of the present disclosure, determining the risk level of the target tailings dam at the time stamp to be predicted based on the second time series monitoring value includes: obtaining the monitoring weight corresponding to each monitoring means; determining the corresponding candidate risk assessment value based on the second time series monitoring value of each monitoring means; and calculating the risk level of the target tailings dam at the time stamp to be predicted based on the candidate risk assessment value and the monitoring weight.

[0012] According to one embodiment of the present disclosure, the risk level of the target tailings dam at the time stamp to be predicted is calculated based on the candidate risk assessment value and the monitoring weight, including: multiplying the candidate risk assessment value of each monitoring means and the corresponding monitoring weight, and adding all the products to calculate a comprehensive risk value; comparing the comprehensive risk value with the risk value-risk level mapping relationship to determine the risk level of the target tailings dam at the time stamp to be predicted.

[0013] According to an embodiment of the present disclosure, the formula for determining the maximum degree of association of each target monitoring node based on the time series monitoring data and the first time series monitoring value is: Among them, the is the maximum correlation degree, X is the time series monitoring data, and Y is the first time series monitoring value.

[0014] According to one embodiment of the present disclosure, the state identification of the target tailings dam to determine the safety factor of the target tailings dam includes: establishing a simulation model of the target tailings dam; matching the time series monitoring data with the simulation data of the simulation model, and taking the simulation data with the largest matching value as the target simulation data; and obtaining the safety factor of the target simulation data as the safety factor of the target tailings dam.

[0015] According to one embodiment of the present disclosure, the method further includes: in response to the safety factor being less than or equal to a safety factor threshold, acquiring a dam body position corresponding to the safety factor.

[0016] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a tailings dam deformation warning device, including: an acquisition module, used to obtain time-series monitoring data of a target tailings dam at multiple target monitoring nodes, and obtain a first time-series monitoring value of the target tailings dam by multiple monitoring means; a calculation module, used to determine the maximum correlation of each target monitoring node based on the time-series monitoring data and the first time-series monitoring value; an identification module, used to identify the state of the target tailings dam in response to the maximum correlation of any target monitoring node being greater than or equal to the correlation threshold, so as to determine the safety factor of the target tailings dam; a generation module, used to perform a rolling prediction on the target tailings dam in response to the safety factor being less than or equal to the safety factor threshold, so as to generate a creep curve of the target tailings dam and a second time-series monitoring value of multiple monitoring means at a time stamp to be predicted; an early warning module, which determines the risk occurrence level of the target tailings dam at a time stamp to be predicted based on the creep curve, and determines the risk level of the target tailings dam at a time stamp to be predicted based on the second time-series monitoring value, and generates a risk warning based on the risk level and the risk occurrence level.

[0017] To achieve the above-mentioned purpose, the third aspect embodiment of the present disclosure proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the tailings dam deformation early warning method as described in the first aspect embodiment of the present disclosure.

[0018] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present disclosure proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the tailings dam deformation early warning method as described in the first aspect embodiment of the present disclosure.

[0019] To achieve the above-mentioned purpose, the fifth aspect of the present disclosure proposes a computer program product, including a computer program, which, when executed by a processor, is used to implement the tailings dam deformation early warning method as described in the first aspect of the present disclosure.

[0020] Through the comprehensive monitoring and early warning system disclosed in the present invention, not only can a variety of monitoring means be implemented for coordinated monitoring, so that various monitoring technologies can complement each other's advantages, all-round deformation monitoring of points, lines and surfaces of tailings dams can be realized, and future deformation trends can be accurately predicted and early warned, thereby improving the accuracy and reliability of deformation early warnings. It can also provide strong technical support for the safe management and environmental protection of tailings dams, help prevent potential risks, and protect public safety and the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a schematic diagram of a tailings dam deformation early warning method according to an embodiment of the present disclosure;

[0022] Figure 2 is a schematic diagram of another tailings dam deformation early warning method according to an embodiment of the present disclosure;

[0023] Figure 3 is a schematic diagram of another tailings dam deformation early warning method according to an embodiment of the present disclosure;

[0024] Figure 4 is a schematic diagram of another tailings dam deformation early warning method according to an embodiment of the present disclosure;

[0025] Figure 5 is a schematic diagram of a tailings dam deformation early warning device according to one embodiment of the present disclosure;

[0026] Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure are described in detail below, examples of which 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 disclosure, and should not be construed as limiting the present disclosure.

[0028] The acquisition, storage, use, and processing of data in the technical solution disclosed in this disclosure are in compliance with the relevant provisions of relevant laws and regulations.

[0029] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0030] Figure 1 Schematic diagram of a tailings dam deformation early warning method according to an embodiment of the present disclosure, Figure 1 As shown, the tailings dam deformation early warning method includes the following steps:

[0031] S101, obtaining time-series monitoring data of a target tailings dam at multiple target monitoring nodes, and obtaining first time-series monitoring values ​​of the target tailings dam by multiple monitoring means.

[0032] The tailings dam deformation warning method of the embodiment of the present application can be applied to the scenario of deformation monitoring of the target tailings dam. The executor of the tailings dam deformation warning of the embodiment of the present application can be the tailings dam deformation warning device of the embodiment of the present application, and the tailings dam deformation warning device can be set on an electronic device.

[0033] In the embodiment of the present disclosure, there may be multiple target monitoring nodes, and may include target monitoring nodes of various types, which are not limited here. For example, the target monitoring node may be a tailings pond infiltration line monitoring node, a pore water pressure monitoring node, a stress monitoring node, a reservoir water level monitoring node, and a rainfall monitoring node.

[0034] In the embodiments of the present disclosure, the monitoring means may include multiple methods, which are not limited herein. For example, the monitoring means may include Synthetic Aperture Radar (SAR) satellite monitoring, drone technology, GNSS system, radar technology, and internal displacement monitoring.

[0035] Through this multi-source monitoring data fusion technology, various monitoring technologies have been complemented. It can realize all-round deformation monitoring of tailings dam points, lines and surfaces, and accurately predict and warn of future deformation trends, thereby improving the accuracy and reliability of deformation warning.

[0036] S102: Determine the maximum correlation degree of each target monitoring node based on the time series monitoring data and the first time series monitoring value.

[0037] In the disclosed embodiment, the maximum correlation is a value representing the correlation between the monitoring node and the overall deformation monitoring of the target tailings dam. The larger the maximum correlation, the more likely it is that the target tailings dam slope is in a progressive destruction process and needs to be confirmed in the next step.

[0038] In the embodiment of the present disclosure, there may be multiple methods for determining the maximum correlation based on the time series monitoring data and the first time series monitoring value, and no limitation is made here.

[0039] In a possible implementation, the timing monitoring data and the first timing monitoring value can be calculated by a preset correlation algorithm to obtain the maximum correlation. The correlation algorithm is designed in advance and can be changed according to actual design requirements, and is not limited here.

[0040] In another possible implementation, the time series monitoring data and the first time series monitoring value can also be processed by a correlation generation model to generate a maximum correlation value. The correlation generation model is trained in advance and stored in the storage space of the electronic device for easy retrieval when needed.

[0041] S103: In response to a maximum correlation degree of any target monitoring node being greater than or equal to a correlation degree threshold, a state identification is performed on the target tailings dam to determine a safety factor of the target tailings dam.

[0042] In the embodiment of the present disclosure, the relevance threshold is designed in advance and can be changed according to actual design needs, and no limitation is made here. For example, the relevance threshold can be 0.4.

[0043] In the disclosed embodiment, when the maximum correlation of the target monitoring node is greater than or equal to the correlation threshold, it can be considered that the monitoring node is strongly correlated with the overall deformation of the target tailings dam, and safety monitoring is required.

[0044] It should be noted that the safety factor is a data value used to determine whether the target tailings dam has safety risks. The larger the safety factor, the more likely it is that the target tailings dam has not yet become unstable and there is no risk of disaster to the dam in the short term.

[0045] In the embodiment of the present disclosure, there are many methods for identifying the state of the target tailings dam to determine the safety factor of the target tailings dam, which are not limited here. For example, the safety factor of the target tailings dam can be determined by simulating the current working condition by establishing a simulation model of the target tailings dam, or the working condition parameters of the target tailings dam can be calculated by a preset safety factor algorithm to determine the safety factor of the target tailings dam.

[0046] It should be noted that, in response to the maximum correlation of any target monitoring node being less than the correlation threshold, it can be considered that the monitoring node is weakly correlated with the overall deformation of the target tailings dam, and no subsequent steps are required, and the process returns to step S101 to continue monitoring.

[0047] S104, in response to the safety factor being less than or equal to the safety factor threshold, performing a rolling prediction on the target tailings dam to generate a creep curve of the target tailings dam and second time series monitoring values ​​of multiple monitoring means at a time stamp to be predicted.

[0048] In the embodiment of the present disclosure, the safety factor threshold is a critical value for determining whether the target tailings dam has an accident risk. The safety factor threshold is designed in advance and can be changed according to actual design needs, and no limitation is made here. For example, the safety factor threshold can be 1.05.

[0049] When the safety factor is less than or equal to the safety factor threshold, it can be considered that the dam body of the target tailings dam is close to an unstable state, and it is necessary to enter the trend prediction stage to conduct risk assessment and risk warning for tailings dam disasters.

[0050] It should be noted that the creep curve is a curve that describes the plastic deformation behavior of the target tailings dam over time, and is used to describe the change of the stress-strain relationship of the target tailings dam over time.

[0051] In the disclosed embodiment, a pre-trained rolling prediction model may be used to predict the target tailings dam to generate a creep curve of the target tailings dam and second time series monitoring values ​​of multiple monitoring means at the time stamp to be predicted.

[0052] It should be noted that, in response to the safety factor being greater than the safety factor threshold, it can be considered that the dam body state of the target tailings dam is stable at this time, and no subsequent steps are required, and the process returns to the step in S101 to continue monitoring.

[0053] S105, determining the risk level of the target tailings dam at the time stamp to be predicted based on the creep curve, and determining the risk level of the target tailings dam at the time stamp to be predicted based on the second time series monitoring value, and generating a risk warning based on the risk level and the risk occurrence level.

[0054] It should be noted that the risk occurrence level is a level value that describes the possibility of risk occurring in the target tailings dam at the time stamp to be predicted. The higher the risk occurrence level, the more likely the risk is to occur.

[0055] The risk level is a level value that describes the severity of the risk of the target tailings dam at the time stamp to be predicted. The higher the risk level, the more serious the risk.

[0056] In an embodiment of the present disclosure, first, time-series monitoring data of a target tailings dam at multiple target monitoring points are obtained, and first time-series monitoring values ​​of the target tailings dam by multiple monitoring means are obtained. Then, based on the time-series monitoring data and the first time-series monitoring values, a maximum correlation degree of each target monitoring point is determined. Then, in response to the maximum correlation degree of any target monitoring point being greater than or equal to a correlation degree threshold, a state identification is performed on the target tailings dam to determine the safety factor of the target tailings dam. Then, in response to the safety factor being less than or equal to the safety factor threshold, a rolling prediction is performed on the target tailings dam to generate a creep curve of the target tailings dam and second time-series monitoring values ​​of multiple monitoring means at a time stamp to be predicted. Finally, the risk occurrence level of the target tailings dam at the time stamp to be predicted is determined based on the creep curve, and the risk level of the target tailings dam at the time stamp to be predicted is determined based on the second time-series monitoring values, and a risk warning is generated based on the risk level and the risk occurrence level. Through the comprehensive monitoring and early warning system disclosed in the present invention, not only can a variety of monitoring means be implemented for coordinated monitoring, so that various monitoring technologies can complement each other's advantages, all-round deformation monitoring of points, lines and surfaces of tailings dams can be realized, and future deformation trends can be accurately predicted and early warned, thereby improving the accuracy and reliability of deformation early warnings. It can also provide strong technical support for the safe management and environmental protection of tailings dams, help prevent potential risks, and protect public safety and the ecological environment.

[0057] In one possible implementation, the calculation of the correlation is characterized by the Pearson correlation coefficient, which is used to describe the degree of mutual influence between two variables, and whether there is a positive or negative correlation between the two variables X and Y. Based on the time series monitoring data and the first time series monitoring value, the formula for determining the maximum correlation of each target monitoring point is:

[0058]

[0059] in, is the maximum correlation, X is the time series monitoring data, and Y is the first time series monitoring value.

[0060] After the maximum correlation is calculated and obtained, the current correlation level can be determined by setting the maximum correlation to a preset judgment interval. For example, it can be as follows:

[0061] : Very strong correlation;

[0062] : Strong correlation;

[0063] : Moderately related;

[0064] : weak correlation;

[0065] : Very weak correlation or no correlation.

[0066] In the above embodiment, the risk level of the target tailings dam at the time stamp to be predicted is determined based on the creep curve, and the risk level of the target tailings dam at the time stamp to be predicted can also be determined by Figure 2 Explaining further, the method includes:

[0067] S201, for any time stamp to be predicted, obtaining the tangent angle of the creep curve at the point corresponding to the time stamp to be predicted.

[0068] It should be noted that the tangent angle can quantify the creep rate and can be used to describe the deformation rate of the target tailings dam.

[0069] In the disclosed embodiment, specific points can be selected on the creep curve and tangents can be drawn to calculate the angles between these tangents and the horizontal axis (time axis), which are the tangent angles at the corresponding points.

[0070] In another possible implementation, the tangent angle of the point corresponding to the time stamp to be predicted can be calculated by using a tangent angle calculation formula. The tangent angle calculation formula can be as follows:

[0071]

[0072] Where A is the slope value of the linear fitting equation of the tangent angle, i (i=1, 2, 3, ..., n) is the time sequence number: αi is the tangent angle of the cumulative displacement, is the average value of the tangent angles αi.

[0073] αi is calculated by the following formula

[0074]

[0075] Where B is the scale, that is:

[0076] When A<0, the slope is in the initial deformation stage; when A=0, the slope is in the constant speed deformation stage; when A>0, the slope is in the accelerated deformation stage.

[0077] S202, matching the tangent angle with a preset tangent angle-risk level mapping relationship to determine the risk occurrence level of the target tailings dam.

[0078] In the disclosed embodiment, the tangent angle-risk level mapping relationship is designed in advance, and may be obtained through experiments, or may be set based on the experience of experts or operators, and no limitation is made here.

[0079] For example, the tangent angle-risk level mapping relationship can be as follows:

[0080] When the tangent angle α≈45°, the tailings dam deformation is in the constant-speed deformation stage and the possibility of disaster is low.

[0081] When the tangent angle is 45°<α<80°, the tailings dam deformation enters the initial accelerated deformation stage and the possibility of disaster is low.

[0082] When the tangent angle is 80°≤α<85°, the tailings dam deformation enters the medium-accelerated deformation stage, and the possibility of disaster is high.

[0083] When the tangent angle α≥85°, the deformation of the tailings dam enters the accelerated deformation stage and the possibility of disaster is high.

[0084] In the disclosed embodiment, firstly, for any time stamp to be predicted, the tangent angle of the creep curve at the point corresponding to the time stamp to be predicted is obtained, and then the tangent angle is matched with the preset tangent angle-risk level mapping relationship to determine the risk level of the target tailings dam. Through the risk assessment method based on the tangent angle, the safety status of the tailings dam can be quickly assessed in an intuitive and quantitative manner, which can not only significantly improve the accuracy and efficiency of the safety monitoring of the tailings dam, but also provide strong technical support for the safety management and environmental protection of the tailings dam.

[0085] In the above embodiment, the risk level of the target tailings dam at the time stamp to be predicted is determined based on the second time series monitoring value, and the risk level of the target tailings dam at the time stamp to be predicted can also be determined by Figure 3 Explaining further, the method includes:

[0086] S301, obtaining the monitoring weight corresponding to each monitoring means.

[0087] It should be noted that the monitoring weights corresponding to various monitoring methods are designed in advance and can also be obtained by analyzing the actual situation of the target tailings dam. No limitation is made here.

[0088] In one possible implementation, the analytic hierarchy process can be used to perform a weight analysis on the risk levels calculated from the data collected by synthetic aperture radar interferometry (InSAR), drones, radars, GNSS, and internal displacement sensors. The weight values ​​of InSAR, drones, radars, GNSS, and internal displacement sensors range from [0 to 1], and the total weight is 1.

[0089] S302: Determine a corresponding candidate risk assessment value based on the second time series monitoring value of each monitoring means.

[0090] In the disclosed embodiment, there are many methods for determining the corresponding candidate risk assessment values ​​based on the second time series monitoring values ​​of each monitoring means, which are not limited here. For example, the second time series monitoring values ​​can be calculated by a preset risk assessment algorithm to determine the candidate risk assessment values ​​of each monitoring means. The second time series monitoring values ​​can also be processed by a pre-trained risk assessment model to generate candidate risk assessment values ​​for each monitoring means.

[0091] S303, based on the candidate risk assessment values ​​and monitoring weights, calculating the risk level of the target tailings dam at the time stamp to be predicted.

[0092] In the embodiment of the present disclosure, the candidate risk assessment value of each monitoring means and the corresponding monitoring weight can be first multiplied, and all products can be added to calculate the comprehensive risk value, and then the comprehensive risk value can be compared with the risk value-risk level mapping relationship to determine the risk level of the target tailings dam at the time stamp to be predicted.

[0093] It should be noted that the risk value-risk level mapping relationship is designed in advance and can be changed according to actual design requirements or tailings conditions. No limitation is made here.

[0094] For example, the risk value-risk level mapping relationship may include four levels, which are marked from low to high by blue [0~2.5], yellow [2.5~5.0], orange [5.0~7.5], and red [7.5~10]. The risk level of the target tailings dam at the time stamp to be predicted can be determined by determining in which interval the comprehensive risk value is located.

[0095] In the disclosed embodiment, the monitoring weight corresponding to each monitoring means is first obtained, and then the corresponding candidate risk assessment value is determined based on the second time series monitoring value of each monitoring means, and finally the risk level of the target tailings dam at the time stamp to be predicted is calculated based on the candidate risk assessment value and the monitoring weight. Therefore, by introducing monitoring weights and conducting comprehensive risk assessment based on multi-source data, not only can collaborative monitoring of multiple monitoring means be achieved, but also the accuracy and efficiency of tailings dam safety monitoring can be significantly improved, and strong technical support can be provided for the safety management and environmental protection of tailings dams.

[0096] In the above embodiment, the state of the target tailings dam is identified to determine the safety factor of the target tailings dam. Figure 4 Explaining further, the method includes:

[0097] S401, establishing a simulation model of the target tailings dam.

[0098] In the disclosed embodiment, sampling is performed at key positions of the dam body of the target tailings dam, and the sampled rock mass is subjected to compressive strength and shear strength tests to obtain parameters such as elastic modulus, Poisson's ratio, cohesion, internal friction angle, etc. Comprehensively considering the dam height, dam top water level, infiltration line, seepage, initial dam structural mechanical characteristics, dam body settlement state parameters, meteorological data, environmental vibration factors, and engineering geological data of the tailings dam, a three-dimensional geometric model of the tailings dam is drawn based on software such as AutoCAD and CATIA, material parameters, operating condition parameter conditions, boundary constraints, and initial stress conditions are set, and the influence of the seepage field on the stability of the dam body is considered at the same time; after setting the unit size and unit type and dividing the finite element grid, a finite element analysis of multiple working conditions (different soil layers, water levels, etc.) is completed based on the finite element strength reduction method, and a numerical simulation of the progressive destruction process of the slope throughout its life cycle is carried out to obtain the time series simulation data of the key nodes throughout their life cycle.

[0099] S402, matching the time series monitoring data with the simulation data of the simulation model, and taking the simulation data with the largest matching value as the target simulation data.

[0100] In the disclosed embodiment, a dynamic time warping method may be used to intelligently match the similarity between the key node timing monitoring data and the timing simulation data, and search for the simulation data with the greatest similarity to the timing monitoring data.

[0101] S403, obtaining the safety factor of the target simulation data as the safety factor of the target tailings dam.

[0102] It should be noted that in response to the safety factor being less than or equal to the safety factor threshold, the dam body position corresponding to the safety factor is obtained, and a risk warning is generated based on the dam body position, so that the position where the risk may or has occurred can be accurately located through the risk warning, thereby improving the efficiency of taking measures.

[0103] In the disclosed embodiment, a simulation model of the target tailings dam is first established, and then the time series monitoring data is matched with the simulation data of the simulation model, and the simulation data with the largest matching value is used as the target simulation data, and finally the safety factor of the target simulation data is obtained as the safety factor of the target tailings dam. This method combines advanced numerical simulation technology and real-time monitoring data, significantly improving the accuracy and reliability of tailings dam safety assessment.

[0104] Corresponding to the tailings dam deformation warning methods provided in the above-mentioned embodiments, an embodiment of the present disclosure also provides a tailings dam deformation warning device. Since the tailings dam deformation warning device provided in the embodiment of the present disclosure corresponds to the tailings dam deformation warning methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned tailings dam deformation warning methods are also applicable to the tailings dam deformation warning device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0105] Figure 5 This is a schematic diagram of a tailings dam deformation warning device according to an embodiment of the present disclosure. As shown in FIG5 , the tailings dam deformation warning device 500 includes: an acquisition module 510 , a calculation module 520 , an identification module 530 , a generation module 540 and a warning module 550 .

[0106] The acquisition module 510 is used to acquire the time-series monitoring data of the target tailings dam at multiple target monitoring nodes, and acquire the first time-series monitoring values ​​of the target tailings dam by multiple monitoring means.

[0107] The calculation module 520 is used to determine the maximum correlation degree of each target monitoring node based on the time series monitoring data and the first time series monitoring value.

[0108] The identification module 530 is used to identify the state of the target tailings dam in response to the maximum correlation degree of any target monitoring node being greater than or equal to the correlation degree threshold, so as to determine the safety factor of the target tailings dam.

[0109] The generation module 540 is used to perform a rolling prediction on the target tailings dam in response to the safety factor being less than or equal to the safety factor threshold, so as to generate a creep curve of the target tailings dam and second time series monitoring values ​​of multiple monitoring means at the time stamp to be predicted.

[0110] The warning module 550 determines the risk level of the target tailings dam at the time stamp to be predicted based on the creep curve, and determines the risk level of the target tailings dam at the time stamp to be predicted based on the second time series monitoring value, and generates a risk warning based on the risk level and the risk occurrence level.

[0111] According to one embodiment of the present disclosure, the risk level of a target tailings dam at a time stamp to be predicted is determined based on a creep curve, including: for any time stamp to be predicted, obtaining the tangent angle of the creep curve at the point corresponding to the time stamp to be predicted; matching the tangent angle with a preset tangent angle-risk level mapping relationship to determine the risk level of the target tailings dam.

[0112] According to one embodiment of the present disclosure, the risk level of the target tailings dam at the time stamp to be predicted is determined based on the second time series monitoring value, including: obtaining the monitoring weight corresponding to each monitoring means; determining the corresponding candidate risk assessment value based on the second time series monitoring value of each monitoring means; and calculating the risk level of the target tailings dam at the time stamp to be predicted based on the candidate risk assessment value and the monitoring weight.

[0113] According to one embodiment of the present disclosure, the risk level of the target tailings dam at the time stamp to be predicted is calculated based on the candidate risk assessment values ​​and the monitoring weights, including: multiplying the candidate risk assessment values ​​of each monitoring means and the corresponding monitoring weights, and adding all the products to calculate a comprehensive risk value; comparing the comprehensive risk value with the risk value-risk level mapping relationship to determine the risk level of the target tailings dam at the time stamp to be predicted.

[0114] According to one embodiment of the present disclosure, based on the time series monitoring data and the first time series monitoring value, the formula for determining the maximum association degree of each target monitoring node is: in, is the maximum correlation, X is the time series monitoring data, and Y is the first time series monitoring value.

[0115] According to one embodiment of the present disclosure, the state of a target tailings dam is identified to determine the safety factor of the target tailings dam, including: establishing a simulation model of the target tailings dam; matching time series monitoring data with simulation data of the simulation model, and using simulation data with the largest matching value as target simulation data; and obtaining the safety factor of the target simulation data as the safety factor of the target tailings dam.

[0116] According to an embodiment of the present disclosure, the method further includes: in response to the safety factor being less than or equal to a safety factor threshold, acquiring a dam body position corresponding to the safety factor.

[0117] Therefore, through the comprehensive monitoring and early warning system disclosed in the present invention, not only can a variety of monitoring means be used for coordinated monitoring, so that various monitoring technologies can complement each other's advantages, all-round deformation monitoring of points, lines and surfaces of tailings dams can be realized, and future deformation trends can be accurately predicted and warned, thereby improving the accuracy and reliability of deformation warnings. It can also provide strong technical support for the safe management and environmental protection of tailings dams, help prevent potential risks, and safeguard public safety and the ecological environment.

[0118] In order to implement the above embodiment, the present disclosure further provides an electronic device 600, Figure 6 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 that is communicatively connected to the processor, the memory 602 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 601 to implement the present disclosure. Figure 1-Figure 4 A tailings dam deformation early warning method according to an embodiment.

[0119] In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the above embodiments. Figure 1-Figure 4 A tailings dam deformation early warning method according to an embodiment.

[0120] In order to implement the above embodiments, the present disclosure also provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the above embodiments. Figure 1-Figure 4 A tailings dam deformation early warning method according to an embodiment.

[0121] 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.

[0122] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure 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, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0123] 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, unless they contradict each other.

[0124] 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.

[0125] 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.

[0126] 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 instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that contains, stores, communicates, propagates or transmits 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 (non-exhaustive list) of computer-readable media 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), an optical fiber 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 tailings dam deformation early warning method, characterized in that: include: Acquire time-series monitoring data of a target tailings dam at a plurality of target monitoring nodes, and acquire first time-series monitoring values ​​of the target tailings dam by a plurality of monitoring means; Based on the time series monitoring data and the first time series monitoring value, the maximum correlation of each target monitoring node is determined using the Pearson correlation coefficient, where the maximum correlation is a value representing the correlation between the monitoring node and the overall deformation monitoring of the target tailings dam; In response to a maximum correlation degree of any target monitoring node being greater than or equal to a correlation degree threshold, performing state identification on the target tailings dam to determine a safety factor of the target tailings dam; In response to the safety factor being less than or equal to a safety factor threshold, performing a rolling prediction on the target tailings dam to generate a creep curve of the target tailings dam and second time series monitoring values ​​of multiple monitoring means at a time stamp to be predicted; Determine the risk level of the target tailings dam at the time stamp to be predicted based on the creep curve, and determine the risk level of the target tailings dam at the time stamp to be predicted based on the second time series monitoring value, and generate a risk warning based on the risk level and the risk occurrence level; The performing state identification on the target tailings dam to determine the safety factor of the target tailings dam includes: Establishing a simulation model of the target tailings dam; Matching the time series monitoring data with the simulation data of the simulation model, and taking the simulation data with the largest matching value as the target simulation data; The safety factor of the target simulation data is obtained as the safety factor of the target tailings dam.

2. The method according to claim 1, characterized in that The step of determining the risk occurrence level of the target tailings dam at the time stamp to be predicted based on the creep curve includes: For any time stamp to be predicted, obtaining the tangent angle of the creep curve at the point corresponding to the time stamp to be predicted; The tangent angle is matched with a preset tangent angle-risk level mapping relationship to determine the risk occurrence level of the target tailings dam.

3. The method according to claim 2, characterized in that The determining, based on the second time series monitoring value, the risk level of the target tailings dam at the time stamp to be predicted includes: Obtain the monitoring weight corresponding to each monitoring method; Determine a corresponding candidate risk assessment value based on the second time series monitoring value of each monitoring means; Based on the candidate risk assessment value and the monitoring weight, the risk level of the target tailings dam at the time stamp to be predicted is calculated.

4. The method according to claim 3, characterized in that The step of calculating the risk level of the target tailings dam at the time stamp to be predicted based on the candidate risk assessment value and the monitoring weight includes: Multiply the candidate risk assessment value of each monitoring method and the corresponding monitoring weight, and add all the products to calculate the comprehensive risk value; The comprehensive risk value is compared with the risk value-risk level mapping relationship to determine the risk level of the target tailings dam at the time stamp to be predicted.

5. The method according to claim 1, characterized in that The formula for determining the maximum degree of association of each target monitoring node based on the time series monitoring data and the first time series monitoring value is: Among them, the is the maximum correlation degree, X is the time series monitoring data, and Y is the first time series monitoring value.

6. The method according to claim 5, characterized in that The method further comprises: In response to the safety factor being less than or equal to the safety factor threshold, a dam body position corresponding to the safety factor is acquired.

7. A tailings dam deformation early warning device, characterized in that: include: An acquisition module, used to acquire time-series monitoring data of a target tailings dam at multiple target monitoring nodes, and to acquire first time-series monitoring values ​​of the target tailings dam by multiple monitoring means; A calculation module, configured to determine the maximum correlation of each target monitoring node using the Pearson correlation coefficient based on the time series monitoring data and the first time series monitoring value, wherein the maximum correlation is a value representing the correlation between the monitoring node and the overall deformation monitoring of the target tailings dam; an identification module, configured to identify the state of the target tailings dam in response to a maximum correlation degree of any target monitoring node being greater than or equal to a correlation degree threshold, so as to determine a safety factor of the target tailings dam; A generating module, configured to perform a rolling prediction on the target tailings dam in response to the safety factor being less than or equal to a safety factor threshold, so as to generate a creep curve of the target tailings dam and second time series monitoring values ​​of multiple monitoring means at a time stamp to be predicted; an early warning module, which determines the risk level of the target tailings dam at the time stamp to be predicted based on the creep curve, and determines the risk level of the target tailings dam at the time stamp to be predicted based on the second time series monitoring value, and generates a risk early warning based on the risk level and the risk level; The identification module is also used to establish a simulation model of the target tailings dam; Matching the time series monitoring data with the simulation data of the simulation model, and taking the simulation data with the largest matching value as the target simulation data; The safety factor of the target simulation data is obtained as the safety factor of the target tailings dam.

8. An electronic device, characterized in that: Including memory and processor; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

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