Tailing pond dam break probability prediction method and device and electronic equipment

Abstract

The invention provides a tailing pond dam break probability prediction method and device and electronic equipment. The method comprises the steps that the permeability coefficient of a target tailing pond is obtained; constructing a first correlation of the rainfall time, the slope state value, the landslide probability increase rate and the slope state coefficient of the target tailings pond, constructing a second correlation of the rainfall intensity and the landslide probability increase rate, and constructing a third correlation of the rainfall intensity, the permeability coefficient and the slope state coefficient; establishing a fourth correlation based on the first correlation, the second correlation and the third correlation; and dam break probability prediction is carried out on the target tailing pond based on the fourth correlation. By establishing the first correlation, the second correlation and the third correlation, multiple variables can be fitted into the fourth correlation, multiple key factors are integrated, comprehensiveness and accuracy of evaluation are ensured, and dam break factor analysis of target tailings in a complex variable environment can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of tailings dam safety monitoring, and in particular to a method, device, and electronic equipment for predicting the probability of tailings dam breach. Background Art

[0002] Tailings ponds are used to store solid waste generated during the mineral processing process. Their main functions include storing tailings, regulating floods, ensuring the reuse of water resources, and protecting the environment. Although there are online safety monitoring systems for tailings ponds that can monitor key parameters such as dam displacement, infiltration line, reservoir water level, dry beach length, rainfall, etc. in real time, since tailings dam breaches occur suddenly, if they are not predicted in advance and prevented in time, they will cause serious loss of life and property. 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 propose a method for predicting the probability of tailings dam breach.

[0005] The second objective of the present disclosure is to provide a device for predicting the probability of tailings dam breach.

[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 method for predicting the probability of tailings dam break, including: obtaining the permeability coefficient of the target tailings pond; constructing a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, and constructing a second correlation between rainfall intensity and landslide probability increase rate, and constructing a third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient; establishing a fourth correlation between the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation; and predicting the probability of dam break of the target tailings pond based on the fourth correlation.

[0010] According to one embodiment of the present disclosure, the first correlation relationship among the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond is constructed, including: multiplying the rainfall time and the landslide probability increase rate, and taking the product as the first calculation factor; adding the first calculation factor and the slope state coefficient, and taking the sum as the second calculation factor; taking the slope state value equal to the second calculation factor as the first correlation relationship.

[0011] According to one embodiment of the present disclosure, the formula of the first correlation is: Among them, the is the landslide probability increase rate, is the slope state coefficient, is the slope state value, and t is the rainfall time.

[0012] According to one embodiment of the present disclosure, constructing the second correlation between rainfall intensity and landslide probability increase rate includes: determining a first constant coefficient based on the permeability coefficient; calculating a third calculation factor based on the first constant coefficient and the rainfall intensity; and taking the landslide probability increase rate equal to the third calculation factor as the second correlation.

[0013] According to one embodiment of the present disclosure, the formula of the second correlation is: Among them, the is the landslide probability increase rate, , , is the first constant coefficient, and q is the rainfall intensity.

[0014] According to one embodiment of the present disclosure, constructing the third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient includes: determining a second constant coefficient based on the permeability coefficient, and obtaining a preset landslide probability threshold, when the slope state value is less than the landslide probability threshold, the slope state coefficient is in a discrete state; calculating a fourth calculation factor based on the second constant coefficient and the rainfall intensity; taking a rainfall time threshold equal to the fourth calculation factor as the third correlation, wherein the rainfall time threshold is the time required for the slope state value to rise to the landslide probability threshold.

[0015] According to one embodiment of the present disclosure, Among them, the is the rainfall time threshold, , , is the second constant coefficient, and q is the rainfall intensity.

[0016] According to one embodiment of the present disclosure, the prediction of the dam break probability of the target tailings pond based on the fourth correlation includes: for any target timestamp to be predicted, calculating the target slope state value at the target timestamp based on the fourth correlation; calculating the exponential function value of the target slope state value; and calculating the dam break probability of the target tailings pond at the target timestamp based on the exponential function value.

[0017] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a tailings dam break probability prediction device, including: an acquisition module, used to obtain the permeability coefficient of the target tailings pond; a construction module, used to construct a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, and to construct a second correlation between rainfall intensity and landslide probability increase rate, and to construct a third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient; a generation module, used to establish a fourth correlation between the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation; a prediction module, used to predict the dam break probability of the target tailings pond based on the fourth correlation.

[0018] 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 break probability prediction method as described in the first aspect embodiment of the present disclosure.

[0019] 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 break probability prediction method as described in the first aspect embodiment of the present disclosure.

[0020] 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 break probability prediction method as described in the first aspect of the present disclosure.

[0021] Therefore, by establishing the first correlation, the second correlation, and the third correlation, the rainfall time, slope state value, landslide probability increase rate and slope state coefficient can be correlated, as well as the rainfall intensity and landslide probability increase rate, and the rainfall intensity, permeability coefficient and slope state coefficient can be correlated. Further, multiple variables are fitted into the fourth correlation, which not only integrates multiple key factors, but also ensures the comprehensiveness and accuracy of the evaluation, and can realize the analysis of dam break factors of the target tailings in a complex and variable environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of a method for predicting the probability of a tailings dam breach according to an embodiment of the present disclosure; Figure 2 is a schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure; Figure 3 is a schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure; Figure 4 is a schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure; Figure 5 is a schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure; Figure 6 is a schematic diagram of the relationship between the target slope state value and the dam break probability in one embodiment of the present disclosure; Figure 7 It is a schematic diagram of a tailings dam breach probability prediction device according to one embodiment of the present disclosure; Figure 8 is a schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Figure 1 is a schematic diagram of a method for predicting the probability of a tailings dam breach according to an embodiment of the present disclosure, such as Figure 1 As shown, the tailings dam failure probability prediction method includes the following steps: S101, obtaining the permeability coefficient of the target tailings pond.

[0027] The tailings dam break probability prediction method of the embodiment of the present application can be applied to the tailings dam break prediction scenario. The executor of the tailings dam break probability prediction of the embodiment of the present application can be the tailings dam break probability prediction device of the embodiment of the present application, and the tailings dam break probability prediction device can be set on an electronic device.

[0028] It should be noted that the permeability coefficient of a tailings dam refers to the ability of water to pass through a medium such as soil or rock under unit hydraulic gradient conditions. It is usually expressed in meters per second (m / s) or centimeters per second (cm / s). In general, the permeability coefficient of a target tailings pond will fluctuate, but it can be considered as a constant value overall.

[0029] In the embodiment of the present disclosure, there are many methods for obtaining the permeability coefficient of the target tailings pond, which are not limited here. For example, the permeability coefficient of the target tailings pond can be obtained through laboratory testing or through on-site sampling.

[0030] S102, constructing a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, constructing a second correlation between rainfall intensity and landslide probability increase rate, and constructing a third correlation between rainfall intensity, permeability coefficient and slope state coefficient.

[0031] It should be noted that the Slope Condition Index (SCI) is a comprehensive indicator used to evaluate the current condition of a slope and its potential risks. It combines multiple factors, including but not limited to geological conditions, hydrological characteristics, structural integrity, deformation characteristics, etc., to provide a quantitative method to evaluate the safety and stability of a slope. There may be multiple factors that affect the slope condition value, so the slope condition value is a variable.

[0032] The rate of increase in landslide probability refers to the rate at which the probability of a landslide occurring in a certain area or slope changes over time within a specific period of time. This parameter is crucial for assessing and predicting potential landslide risks, especially in geological disaster early warning systems. It can help decision makers take preventive measures in a timely manner to reduce the loss of life and property. The rate of increase in landslide probability is a variable that changes with factors such as rainfall.

[0033] Slope Condition Coefficient (SCC) is a comprehensive indicator used to quantify the current condition of a slope and its potential risks. It integrates multiple factors that affect slope stability to provide a numerical evaluation result to help engineers and technicians better understand and manage the safety of slopes. The slope condition coefficient is usually affected by geometric characteristics, material characteristics, hydrological conditions, etc., and is a variable.

[0034] In the embodiment of the present disclosure, the first correlation, the second correlation and the third correlation may be embodied in various forms. For example, they may be embodied in the form of a function or in the form of a mapping table, and no limitation is made here.

[0035] There are many methods for constructing the first correlation, the second correlation and the third correlation, which are not limited here.

[0036] In a possible implementation, the operating relationship between various parameters can be determined by analyzing the historical operating data of the target tailings, thereby establishing the first correlation, the second correlation, and the third correlation.

[0037] In another possible implementation manner, a simulation model of the target tailings may be established, and the first correlation, the second correlation and the third correlation of each parameter may be obtained through simulation.

[0038] In another possible implementation, each parameter can also be processed by a logistic regression model to generate a first correlation, a second correlation, and a third correlation of each parameter. It should be noted that the logistic regression model (sRegression, LR) is a generalized linear regression analysis model. Unlike the general linear regression model, the logistic regression model limits the output value to the interval [0, 1] through the Sigmoid function.

[0039] S103, establishing a fourth correlation among the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation.

[0040] In the embodiment of the present disclosure, after the first correlation, the second correlation, and the third correlation are obtained, there may be multiple methods for establishing the fourth correlation, which are not limited here.

[0041] For example, the first correlation, the second correlation, and the third correlation can be calculated by establishing an algorithm to establish the fourth correlation; in another possible implementation method, the first correlation, the second correlation, and the third correlation can be processed by establishing a model to generate the fourth correlation.

[0042] Therefore, the fourth correlation is established through the first correlation, the second correlation, and the third correlation, and the rainfall time, slope state value, landslide probability increase rate, slope state coefficient, rainfall intensity and landslide probability increase rate that affect the target tailings dam break can be fitted into one correlation relationship, which can realize the analysis of the dam break factors of the target tailings in a complex and variable environment.

[0043] S104, predicting the dam break probability of the target tailings pond based on the fourth correlation.

[0044] In the disclosed embodiment, the permeability coefficient of the target tailings dam is first obtained, and then the first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings dam is constructed, and the second correlation between the rainfall intensity and the landslide probability increase rate is constructed, and the third correlation between the rainfall intensity, permeability coefficient and slope state coefficient is constructed, and then the fourth correlation between the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings dam is established based on the first correlation, the second correlation and the third correlation, and finally the target tailings dam is predicted for the probability of dam break based on the fourth correlation. Thus, by establishing the first correlation, the second correlation and the third correlation, the rainfall time, the slope state value, the landslide probability increase rate and the slope state coefficient can be associated, and the rainfall intensity and the landslide probability increase rate can be associated, and the rainfall intensity, permeability coefficient and the slope state coefficient can be associated, and multiple variables can be further fitted into the fourth correlation, which not only integrates multiple key factors, but also ensures the comprehensiveness and accuracy of the evaluation, and can realize the analysis of the dam break factors of the target tailings in a complex and variable environment.

[0045] In the above embodiment, the first correlation relationship among the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings dam is constructed, and the first correlation relationship among the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings dam is constructed. Figure 2 To explain further, Figure 2 Schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure, the method comprising: S201, multiplying the rainfall time and the landslide probability increase rate, and using the product as the first calculation factor.

[0046] S202: Add the first calculation factor and the slope state coefficient, and use the sum as the second calculation factor.

[0047] S203: taking the slope state value equal to the second calculation factor as the first correlation relationship.

[0048] In the embodiment of the present disclosure, the formula of the first correlation is:

[0049] in, is the rate of increase of landslide probability, is the slope state coefficient, is the slope state value, and t is the rainfall time.

[0050] In the above embodiment, the second correlation between rainfall intensity and landslide probability increase rate is constructed, and the second correlation between rainfall intensity and landslide probability increase rate can also be constructed by Figure 3 To explain further, Figure 3 Schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure, the method comprising: S301, determining a first constant coefficient based on the permeability coefficient.

[0051] It should be noted that there may be multiple first constant coefficients, which need to be determined according to the actual fitting situation.

[0052] It is found through experiments that the first constant coefficient and the permeability coefficient in the embodiment of the present disclosure are distributed in the form of a power function. In a possible implementation method, the selected fitting formula is as follows: k- Fitting formula expression:

[0053] k- Fitting formula expression:

[0054] k- Fitting formula expression:

[0055] in, , , is the first constant coefficient, k is the permeability coefficient, , and is the dependent variable related to the target tailings pond, which can be determined according to the actual situation, and c is a constant coefficient.

[0056] S302: Calculate a third calculation factor based on the first constant coefficient and rainfall intensity.

[0057] S303, taking the landslide probability increase rate equal to the third calculation factor as the second correlation relationship.

[0058] In the embodiment of the present disclosure, the formula of the second correlation is:

[0059] in, is the rate of increase of landslide probability, , , is the first constant coefficient, and q is the rainfall intensity.

[0060] In the above embodiment, the third correlation relationship among rainfall intensity, permeability coefficient and slope state coefficient is constructed, and the third correlation relationship among rainfall intensity, permeability coefficient and slope state coefficient can also be constructed by Figure 4 To explain further, Figure 4 Schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure, the method comprising: S401, determining a second constant coefficient based on the permeability coefficient, and obtaining a preset landslide probability threshold. When the slope state value is less than the candidate slope state value corresponding to the landslide probability threshold, the slope state coefficient is in a discrete state.

[0061] It should be noted that there may be multiple second constant coefficients, which need to be determined according to the actual fitting situation.

[0062] It is found through experiments that the second constant coefficient and the permeability coefficient in the embodiment of the present disclosure are distributed in the form of a power function. In a possible implementation method, the selected fitting formula is as follows: k- Fitting formula expression:

[0063] k- Fitting formula expression:

[0064] k- Fitting formula expression:

[0065] in, , , is the second constant coefficient, k is the permeability coefficient, , , , and It is a dependent variable related to the target tailings pond and can be determined according to actual conditions.

[0066] The inventors found that when fitting the slope state coefficient, when the slope state coefficient is small, the slope state coefficient presents scatter points, and the overall fitting performance is not obvious, but when the slope state coefficient reaches a certain value, the overall fitting presents a power function form.

[0067] Therefore, in the embodiment of the present disclosure, the inventor introduces a landslide probability threshold, and when the slope state value is less than the candidate slope state value corresponding to the landslide probability threshold, the slope state coefficient is in a discrete state. Therefore, by setting the landslide probability threshold, it is possible to distinguish whether the slope state value is in a discrete state or in a power function state, thereby achieving accurate prediction of the slope state value.

[0068] S402, calculating a fourth calculation factor based on the second constant coefficient and rainfall intensity.

[0069] S403: The rainfall time threshold is equal to the fourth calculation factor as a third correlation relationship, wherein the rainfall time threshold is the time required for the slope state value to rise to the landslide probability threshold.

[0070] The rainfall time threshold is the slope state value Z from The rainfall time required to reach the landslide probability threshold.

[0071] In the embodiment of the present disclosure, the formula of the third correlation is:

[0072] in, is the rainfall time threshold, , , is the second constant coefficient, and q is the rainfall intensity.

[0073] In the embodiment of the present disclosure, the formula for establishing the fourth correlation of the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation is:

[0074] Furthermore, the above formula can be converted into the following formula:

[0075] From the above formula, we can see that when the rainfall intensity is close to 0, a is a constant. tends to infinity, and a and Substituting into the above formula, we get that Z tends to infinitesimal, and the landslide probability is close to 0; when the rainfall intensity tends to infinity, a is a constant, is a constant. Substituting it into the above formula, we can see that z is only related to the rainfall time, that is, when the rainwater infiltration per unit time is a constant, the landslide probability is only affected by the rainfall time. The goodness of fit considering the rainfall intensity is shown in the table below.

[0076] Goodness of fit of landslide probability formula (considering rainfall intensity q)

[0077] Furthermore, the above formula can be converted into the following formula:

[0078] in, , , , and is the dependent variable related to the target tailings pond, , and is the dependent variable related to the target tailings pond, b is the slope state coefficient, Z is the slope state value, is the rainfall time, d is the candidate slope state value corresponding to the landslide probability threshold, and c is the constant coefficient.

[0079] In the above embodiment, the probability of dam failure of the target tailings pond is predicted based on the fourth correlation. Figure 5 To explain further, Figure 5 Schematic diagram of another method for predicting the probability of tailings dam failure according to an embodiment of the present disclosure, the method comprising: S501 , for any target timestamp to be predicted, calculating a target slope state value at the target timestamp based on a fourth correlation relationship.

[0080] S502, calculating the exponential function value of the target slope state value.

[0081] S503, calculating the probability of dam breach of the target tailings pond at the target timestamp based on the exponential function value.

[0082] In the embodiment of the present disclosure, the formula for calculating the probability of dam failure of the target tailings pond at the target timestamp based on the exponential function value may be as follows:

[0083] in, is the dam break probability, and Z is the target slope state value. In a possible implementation, the relationship between the target slope state value and the dam break probability can be expressed as Figure 6 As shown, Figure 6 It is a schematic diagram of the relationship between the target slope state value and the dam break probability according to an embodiment of the present disclosure.

[0084] Corresponding to the tailings dam break probability prediction methods provided in the above-mentioned embodiments, an embodiment of the present disclosure further provides a tailings dam break probability prediction device. Since the tailings dam break probability prediction device provided in the embodiment of the present disclosure corresponds to the tailings dam break probability prediction methods provided in the above-mentioned embodiments, the implementation methods of the above-mentioned tailings dam break probability prediction methods are also applicable to the tailings dam break probability prediction device provided in the embodiment of the present disclosure, and will not be described in detail in the following embodiments.

[0085] Figure 7 It is a schematic diagram of a tailings dam break probability prediction device according to an embodiment of the present disclosure. As shown in FIG7 , the tailings dam break probability prediction device 700 includes: an acquisition module 710 , a construction module 720 , a generation module 730 and a prediction module 740 .

[0086] The acquisition module 710 is used to obtain the permeability coefficient of the target tailings pond.

[0087] Construction module 720 is used to construct a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, a second correlation between rainfall intensity and landslide probability increase rate, and a third correlation between rainfall intensity, permeability coefficient and slope state coefficient.

[0088] The generation module 730 is used to establish a fourth correlation between the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation.

[0089] The prediction module 740 is used to predict the dam break probability of the target tailings pond based on the fourth correlation relationship.

[0090] According to one embodiment of the present disclosure, the first correlation relationship among the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond is constructed, including: multiplying the rainfall time and the landslide probability increase rate, and taking the product as the first calculation factor; adding the first calculation factor and the slope state coefficient, and taking the sum as the second calculation factor; taking the slope state value equal to the second calculation factor as the first correlation relationship.

[0091] According to one embodiment of the present disclosure, the formula of the first correlation is: Among them, the is the landslide probability increase rate, is the slope state coefficient, is the slope state value, and t is the rainfall time.

[0092] According to one embodiment of the present disclosure, constructing the second correlation between rainfall intensity and landslide probability increase rate includes: determining a first constant coefficient based on the permeability coefficient; calculating a third calculation factor based on the first constant coefficient and the rainfall intensity; and taking the landslide probability increase rate equal to the third calculation factor as the second correlation.

[0093] According to one embodiment of the present disclosure, the formula of the second correlation is: Among them, the is the landslide probability increase rate, , , is the first constant coefficient, and q is the rainfall intensity.

[0094] According to one embodiment of the present disclosure, constructing the third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient includes: determining a second constant coefficient based on the permeability coefficient, and obtaining a preset landslide probability threshold, when the slope state value is less than the landslide probability threshold, the slope state coefficient is in a discrete state; calculating a fourth calculation factor based on the second constant coefficient and the rainfall intensity; taking a rainfall time threshold equal to the fourth calculation factor as the third correlation, wherein the rainfall time threshold is the time required for the slope state value to rise to the landslide probability threshold.

[0095] According to one embodiment of the present disclosure, Among them, the is the rainfall time threshold, , , is the second constant coefficient, and q is the rainfall intensity.

[0096] According to one embodiment of the present disclosure, the prediction of the dam break probability of the target tailings pond based on the fourth correlation includes: for any target timestamp to be predicted, calculating the target slope state value at the target timestamp based on the fourth correlation; calculating the exponential function value of the target slope state value; and calculating the dam break probability of the target tailings pond at the target timestamp based on the exponential function value.

[0097] Therefore, by establishing the first correlation, the second correlation, and the third correlation, the rainfall time, slope state value, landslide probability increase rate and slope state coefficient can be correlated, as well as the rainfall intensity and landslide probability increase rate, and the rainfall intensity, permeability coefficient and slope state coefficient can be correlated. Further, multiple variables are fitted into the fourth correlation, which not only integrates multiple key factors, but also ensures the comprehensiveness and accuracy of the evaluation, and can realize the analysis of dam break factors of the target tailings in a complex and variable environment.

[0098] In order to implement the above embodiment, the present disclosure further provides an electronic device 800, Figure 8 is a schematic diagram of an electronic device according to an embodiment of the present disclosure, such as Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 802 that is communicatively connected to the processor, the memory 802 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 801 to implement the present disclosure. Figure 1-Figure 6 A method for predicting the probability of tailings dam breach according to an embodiment.

[0099] 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 6 A method for predicting the probability of tailings dam breach according to an embodiment.

[0100] 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 6 A method for predicting the probability of tailings dam breach according to an embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 method for predicting the probability of tailings dam breach, characterized in that: include: Obtain the permeability coefficient of the target tailings pond; Constructing a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, constructing a second correlation between the rainfall intensity and the landslide probability increase rate, and constructing a third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient; Based on the first correlation, the second correlation, and the third correlation, a fourth correlation among the rainfall time, rainfall intensity, permeability coefficient, and slope state value of the target tailings pond is established; The dam break probability of the target tailings pond is predicted based on the fourth correlation.

2. The method according to claim 1, characterized in that The first correlation relationship among the rainfall time, the slope state value, the landslide probability increase rate and the slope state coefficient of the target tailings pond is constructed, including: Multiplying the rainfall time and the landslide probability increase rate, and using the product as the first calculation factor; Adding the first calculation factor and the slope state coefficient, and using the sum as a second calculation factor; The slope state value is equal to the second calculation factor as the first correlation relationship.

3. The method according to claim 2, characterized in that The formula of the first correlation is: Among them, the is the landslide probability increase rate, is the slope state coefficient, is the slope state value, and t is the rainfall time.

4. The method according to claim 1, characterized in that: The second correlation between rainfall intensity and landslide probability increase rate is constructed, including: determining a first constant coefficient based on the permeability coefficient; Calculate a third calculation factor based on the first constant coefficient and the rainfall intensity; The landslide probability increase rate is equal to the third calculation factor as the second correlation relationship.

5. The method according to claim 4, characterized in that The formula of the second correlation is: Among them, the is the landslide probability increase rate, , , is the first constant coefficient, and q is the rainfall intensity.

6. The method according to claim 1, characterized in that The constructing of the third correlation relationship among the rainfall intensity, the permeability coefficient and the slope state coefficient comprises: Determine a second constant coefficient based on the permeability coefficient, and obtain a preset landslide probability threshold, when the slope state value is less than the candidate slope state value corresponding to the landslide probability threshold, the slope state coefficient is a discrete state; Calculate a fourth calculation factor based on the second constant coefficient and the rainfall intensity; The rainfall time threshold is equal to the fourth calculation factor as the third correlation relationship, wherein the rainfall time threshold is the time required for the slope state value to rise to the landslide probability threshold.

7. The method according to claim 6, characterized in that The formula of the third correlation is: Among them, the is the rainfall time threshold, , , is the second constant coefficient, and q is the rainfall intensity.

8. The method according to any one of claims 1 to 7, characterized in that The predicting of the dam break probability of the target tailings pond based on the fourth correlation includes: For any target timestamp to be predicted, calculating a target slope state value at the target timestamp based on the fourth correlation relationship; Calculating an exponential function value of the target slope state value; The probability of dam breach of the target tailings pond at the target timestamp is calculated based on the exponential function value.

9. A tailings dam breach probability prediction device, characterized in that: include: An acquisition module, used to obtain the permeability coefficient of the target tailings pond; A construction module is used to construct a first correlation between the rainfall time, slope state value, landslide probability increase rate and slope state coefficient of the target tailings pond, a second correlation between rainfall intensity and landslide probability increase rate, and a third correlation between the rainfall intensity, the permeability coefficient and the slope state coefficient; A generating module, used for establishing a fourth correlation between the rainfall time, rainfall intensity, permeability coefficient and slope state value of the target tailings pond based on the first correlation, the second correlation and the third correlation; A prediction module is used to predict the probability of dam break of the target tailings pond based on the fourth correlation.

10. 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 8.

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