Geological hazard risk prediction method, system, equipment and medium based on dynamic threshold

Abstract

The present invention discloses a method, system, device and medium for predicting geological disaster risks based on dynamic thresholds, which relates to the field of data processing technology, including obtaining a geological area to be predicted and geological information, dividing the geological area to be predicted into multiple sub-prediction areas, obtaining a first comparison area and a second comparison area, obtaining a first moment and a second moment, and obtaining a comparison threshold; obtaining first settlement data, obtaining second settlement data, obtaining a first calculated settlement factor, and obtaining a first comprehensive settlement factor; obtaining third settlement data, obtaining fourth settlement data, obtaining a second calculated settlement factor, and obtaining a second comprehensive settlement factor; obtaining a prediction result according to the first comprehensive settlement factor, the second comprehensive settlement factor and the comparison threshold. The present invention has the advantages of adaptive adjustment of the comparison threshold, accurate prediction, and stable and reliable prediction.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, system, equipment and medium for predicting geological disaster risks based on dynamic thresholds. Background Art

[0002] Geological disasters, such as ground collapse, landslides, mud-rock flows, etc., are common disaster phenomena in nature, posing a serious threat to human life and property. In order to effectively prevent and reduce the losses caused by geological disasters, accurate and timely geological disaster risk prediction is particularly important.

[0003] Existing geological disaster risk prediction methods mostly rely on geological surveys, historical data analysis and other means, but these methods often have problems such as low prediction accuracy and poor prediction timeliness. In particular, existing methods often ignore the differences and dynamics within the geological region. The geological region usually contains multiple sub-regions, which may have significant differences in geological conditions, rock formation data, etc. At the same time, geological changes are a dynamic process, and the speed and magnitude of changes in different time periods may also be different. Therefore, simply predicting the entire geological region as a whole is difficult to accurately reflect its internal changes and risk levels. Secondly, different rock formation data have different physical properties and stability, so the threshold for determining anomalies should also change accordingly. Using a fixed threshold for risk judgment ignores the differences in rock formation data and reduces the accuracy and reliability of the prediction.

[0004] Therefore, a new method for geological disaster risk prediction is urgently needed to provide accurate and timely geological disaster risk warning information and provide strong support for the prevention and control of geological disasters. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method, system, equipment and medium for predicting geological disaster risks based on dynamic thresholds.

[0006] A method for predicting geological disaster risks based on dynamic thresholds includes: obtaining a geological area to be predicted and corresponding geological information, dividing the geological area to be predicted into a plurality of sequentially connected sub-prediction areas, obtaining rock formation data of the sub-areas according to the geological information, taking any two sub-prediction areas with the same and adjacent rock formation data as a first comparison area and a second comparison area, obtaining a first moment and a second moment separated by a first time period according to the rock formation data, and obtaining a comparison threshold according to the rock formation data; dividing the first comparison area into a plurality of first calculation areas, obtaining first settlement data corresponding to each first calculation area at a first moment, and obtaining second settlement data corresponding to each first calculation area at a second moment, and obtaining a first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area. The second settlement data obtains the first calculated settlement factor corresponding to the first calculation area, and the first comprehensive settlement factor is obtained based on the second calculation model and the first calculated settlement factors corresponding to each first calculation area; the second comparison area is divided into multiple second calculation areas, and the third settlement data corresponding to each second calculation area is obtained at the first moment, and the fourth settlement data corresponding to each second calculation area is obtained at the second moment, and the second calculated settlement factor corresponding to the second calculation area is obtained based on the first calculation model, the third settlement data corresponding to the second calculation area and the fourth settlement data corresponding to the second calculation area, and the second comprehensive settlement factor is obtained based on the second calculation model and the second calculated settlement factor corresponding to each second calculation area; the prediction result is obtained according to the first comprehensive settlement factor, the second comprehensive settlement factor and the comparison threshold.

[0007] Optionally, obtaining the prediction result according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold includes: obtaining the predicted risk index based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor; and obtaining the prediction result according to the predicted risk index and the comparison threshold.

[0008] Optionally, obtaining the prediction result based on the predicted risk index and the comparison threshold includes: judging whether the predicted risk index exceeds the comparison threshold; if exceeded, determining the geological disaster risk level according to the excess amount, and forming the prediction result accordingly.

[0009] Optionally, the risk index model in obtaining the predicted risk index based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor is expressed as: ;in, To predict the risk index, is the first comprehensive sedimentation factor, It is the second comprehensive sedimentation factor.

[0010] Optionally, the first calculation model in obtaining the first calculation settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area is expressed as: ;in, is the first calculated settlement factor corresponding to the i-th first calculation area, is the area weight corresponding to the i-th first calculation area, is the first settlement data corresponding to the i-th first calculation area, is the second settlement data corresponding to the i-th first calculation area.

[0011] Optionally, the second calculation model in obtaining the first comprehensive sedimentation factor based on the second calculation model and the first calculation sedimentation factors corresponding to each first calculation area is expressed as: ;in, is the first comprehensive sedimentation factor, is the number of the first calculation area, is the first calculated settlement factor corresponding to the i-th first calculation area.

[0012] A geological disaster risk prediction system based on dynamic thresholds is also provided, and the system includes: an acquisition module, which is used to acquire the geological area to be predicted and the corresponding geological information, and divide the geological area to be predicted into a plurality of sequentially connected sub-prediction areas, and acquire rock formation data of the sub-areas according to the geological information, and use any two sub-prediction areas with the same and adjacent rock formation data as the first comparison area and the second comparison area, and acquire the first moment and the second moment of the first time interval according to the rock formation data, and acquire the comparison threshold according to the rock formation data; a first data processing module, which is used to divide the first comparison area into a plurality of first calculation areas, and acquire the first settlement data corresponding to each first calculation area at the first moment, and acquire the second settlement data corresponding to each first calculation area at the second moment, and based on the first calculation model, the first settlement data corresponding to the first calculation area and the first calculation area a second data processing module, for dividing the second comparison area into multiple second calculation areas, obtaining third settlement data corresponding to each second calculation area at a first moment, and obtaining fourth settlement data corresponding to each second calculation area at a second moment, and obtaining the second calculation settlement factor corresponding to the second calculation area based on the first calculation model, the third settlement data corresponding to the second calculation area and the fourth settlement data corresponding to the second calculation area, and obtaining the second calculation settlement factor corresponding to the second calculation area based on the second calculation model, the third settlement data corresponding to the second calculation area and the fourth settlement data corresponding to the second calculation area, and obtaining the second comprehensive settlement factor based on the second calculation model and the second calculation settlement factor corresponding to each second calculation area; a prediction and analysis module, for obtaining prediction results according to the first comprehensive settlement factor, the second comprehensive settlement factor and the comparison threshold.

[0013] Optionally, the prediction and analysis module is also used to: obtain a predicted risk index based on a risk index model, a first comprehensive sedimentation factor and a second comprehensive sedimentation factor; and obtain a prediction result according to the predicted risk index and a comparison threshold.

[0014] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned geological disaster risk prediction method based on dynamic thresholds.

[0015] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned geological disaster risk prediction method based on dynamic threshold is implemented.

[0016] The beneficial effects of the present invention are embodied in:

[0017] In the entire geological disaster risk prediction method based on dynamic thresholds, by dividing the geological area to be predicted into multiple sequentially connected sub-prediction areas, and further subdividing them into calculation areas, the scheme can more carefully consider the differences and dynamics within the geological area, accurately capture and analyze the geological changes in different sub-areas and calculation areas, and effectively improve the accuracy and precision of the prediction; further, the settlement data at different time points are obtained to provide reliable basic data for calculating the settlement factor, further enhancing the timeliness and reliability of the prediction. By constructing the first calculation model and the second calculation model, the scheme can comprehensively consider the settlement conditions of multiple calculation areas. The comprehensive settlement factor is calculated, which reflects the overall settlement trend of the comparison area in the selected time period; further, by introducing the risk index model and comparison threshold, the size of the geological disaster risk can be quantified, providing an intuitive basis for risk judgment. At the same time, the comparison threshold is set according to the physical properties and stability differences of the rock formation data, representing the normal or acceptable settlement range, making the risk judgment more scientific and reasonable. When the predicted risk index exceeds the comparison threshold, it means that the risk of geological disasters is high, and it is necessary to further analyze geological conditions, mining activities and other factors to determine the specific risk level and possible disaster types, and provide strong support for disaster prevention and mitigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0019] Figure 1 It is a schematic diagram of the steps of the geological disaster risk prediction method based on dynamic threshold of the present invention;

[0020] Figure 2 It is a schematic diagram of step S4 in the geological disaster risk prediction method based on dynamic threshold value of the present invention;

[0021] Figure 3 It is a schematic diagram of some steps of S41 in the geological disaster risk prediction method based on dynamic threshold of the present invention;

[0022] Figure 4 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0023] Reference numerals:

[0024] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - I / O interface, 705 - communication component. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] like Figure 1 As shown, a method for predicting geological disaster risks based on dynamic thresholds is provided, including:

[0029] S1. Obtain the geological area to be predicted and the corresponding geological information, and divide the geological area to be predicted into a plurality of sequentially connected sub-prediction areas, and obtain rock formation data of the sub-areas according to the geological information, and use any two sub-prediction areas with the same and adjacent rock formation data as the first comparison area and the second comparison area, and obtain a first moment and a second moment separated by a first time period according to the rock formation data, and obtain a comparison threshold according to the rock formation data;

[0030] S2, dividing the first comparison area into a plurality of first calculation areas, acquiring first settlement data corresponding to each first calculation area at a first moment, acquiring second settlement data corresponding to each first calculation area at a second moment, and acquiring a first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area, and acquiring a first comprehensive settlement factor based on the second calculation model and the first calculated settlement factors corresponding to each first calculation area;

[0031] S3, dividing the second comparison area into a plurality of second calculation areas, obtaining third settlement data corresponding to each second calculation area at a first moment, and obtaining fourth settlement data corresponding to each second calculation area at a second moment, and obtaining a second calculated settlement factor corresponding to the second calculation area based on the first calculation model, the third settlement data corresponding to the second calculation area, and the fourth settlement data corresponding to the second calculation area, and obtaining a second comprehensive settlement factor based on the second calculation model and the second calculated settlement factors corresponding to each second calculation area;

[0032] S4. Obtain prediction results according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and a comparison threshold.

[0033] In this embodiment, it should be noted that, in S1, the geological area to be predicted and its corresponding geological information are first obtained, which provides basic data for subsequent analysis and prediction. The geological information includes geological structure and rock layer distribution records. While obtaining this information, the geological area to be predicted is divided into a plurality of sequentially connected sub-prediction areas. Such division is to consider the differences and dynamics within the geological area in more detail, because different sub-areas may have significant differences in geological conditions, rock layer data, etc. Next, the rock layer data of each sub-area is obtained according to the geological information, which is an important basis for judging geological stability and predicting geological disaster risks. Then, the sub-prediction areas with the same and adjacent rock layer data are found, and they are used as the first comparison area and the second comparison area. This is because, under the same rock layer data, adjacent sub-areas should have similar geological changes within a period of time. Finally, a first moment and a second moment separated by a first time period are determined according to the rock layer data, and a comparison threshold corresponding to the rock layer data is obtained. This comparison threshold is an important basis for subsequent judgment of whether the risk of geological disasters exceeds the normal range, which takes into account the physical properties and stability differences of different rock layer data.

[0034] For example, suppose the geological area to be predicted is a slope in a mining area. Through geological survey and historical data analysis, the geological information of the area, including geological structure map, rock layer distribution map, etc., is obtained. Then, the mountain area is divided into multiple sequentially connected sub-prediction areas, such as different slopes of the slope, rock layer exposure or distribution of mining activities. Then, the rock layer data of each sub-area is obtained according to the geological information, such as the type, thickness, inclination, etc. of the rock layer. When comparing these rock layer data, it is found that two adjacent sub-areas have the same rock layer data, so they are used as the first comparison area and the second comparison area. Finally, according to the physical properties of the rock layer data, a first time period with an interval of one month is determined, and the beginning and end of the month are selected as the first moment and the second moment. At the same time, according to the physical properties and stability of the rock layer data, a comparison threshold is obtained, which is used to judge whether the geological changes of the two comparison areas within one month are abnormal, so as to predict the risk of geological disasters.

[0035] In S2, the first comparison area is first subdivided into multiple first calculation areas. Such subdivision is to more accurately capture and analyze geological changes, because even in the same comparison area, different calculation areas may show different settlement trends due to local geological conditions, mining activities or other factors. Next, the first settlement data corresponding to each first calculation area is obtained at the first moment, which is usually achieved by means of field monitoring equipment such as GPS, radar interferometry (InSAR) or leveling. These data record the ground settlement of the calculation area at that moment. Then, at the second moment, the second settlement data corresponding to each first calculation area is obtained again, so that there is settlement information at two different time points. With these settlement data, the first calculation model is used to calculate the first calculation settlement factor corresponding to each first calculation area. This model can be constructed based on physical principles, statistical methods or machine learning algorithms, and it can evaluate geological stability based on the change amount and change rate of settlement data. Subsequently, based on the second calculation model and the first calculation settlement factors corresponding to each first calculation area, a first comprehensive settlement factor is calculated. The second calculation model may be a weighted average model, a cluster analysis model or other model that can comprehensively consider the settlement of multiple calculation areas. The first comprehensive sedimentation factor reflects the overall sedimentation trend of the first comparison area within the selected time period.

[0036] For example, the first comparison area is an area on the hillside of the mining area, which is subdivided into five first calculation areas. At the first moment (such as the beginning of the month), the ground subsidence data of each calculation area is obtained using GPS monitoring equipment and recorded as the first subsidence data. At the second moment (such as the end of the month), monitoring is carried out again and the second subsidence data is obtained. Then, the first calculation settlement factor of each calculation area is calculated using the first calculation model. Finally, the first comprehensive settlement factor is calculated using the second calculation model. This comprehensive settlement factor will be used for subsequent data processing and assessment of future geological disaster risks.

[0037] In S3, the processing method of the first comparison area is similar to that in S2, except that the object becomes a sub-prediction area with the same rock formation data and adjacent to the first comparison area, that is, the second comparison area. First, the second comparison area is subdivided into multiple second calculation areas. Such subdivision is based on the complexity and local differences of geological conditions, and aims to more accurately evaluate the geological changes in different areas. Then, at the first moment, the third settlement data corresponding to each second calculation area is obtained, which also relies on field monitoring equipment such as GPS, InSAR or leveling to ensure the accuracy and reliability of the data. These data record the ground settlement status of the second calculation area at that moment. Subsequently, at the second moment, monitoring is performed again, and the fourth settlement data corresponding to each second calculation area is obtained. In this way, settlement information at two different time points is obtained, which provides a basis for the subsequent calculation of settlement factors. With these settlement data, the first calculation model that is the same or similar to that in step S2 is used to calculate the second calculation settlement factor corresponding to each second calculation area. Finally, based on the second calculation model and the second calculation settlement factor corresponding to each second calculation area, the second comprehensive settlement factor is calculated. The second calculation model may use weighted average, cluster analysis or other methods to comprehensively consider the settlement conditions of multiple calculation areas, thereby obtaining a comprehensive indicator reflecting the overall settlement trend of the second comparison area within the selected time period.

[0038] For example, assume that the second comparison area is another mining area on the hillside of the mining area, adjacent to the first comparison area and with similar rock formation data. This mining area is subdivided into four second calculation areas, each of which represents a different geological condition or mining activity area. At the first moment (such as the beginning of the month), the ground subsidence data of each calculation area is obtained using InSAR technology and recorded as the third subsidence data. These data show the ground subsidence of each calculation area at that moment. At the second moment (such as the end of the month), InSAR monitoring is performed again and the fourth subsidence data is obtained. Then, the second calculation model is used to calculate the second calculation settlement factor of each second calculation area. Finally, the second calculation model is used to calculate the second comprehensive settlement factor based on the second calculation settlement factor of each second calculation area. This comprehensive settlement factor will be used for subsequent data processing and assessment of future geological disaster risks.

[0039] In S4, the first comprehensive settlement factor and the second comprehensive settlement factor calculated previously, as well as the comparison threshold determined according to the rock formation data, are used to predict the risk of geological disasters. The possibility of geological disasters is evaluated by comparing the overall settlement trend of the two comparison areas in the selected time period with the normal or expected settlement range. Specifically, the first comprehensive settlement factor and the second comprehensive settlement factor are used as inputs and substituted into a risk index model, which can calculate a predicted risk index based on the values ​​of the two comprehensive settlement factors. This risk index reflects the magnitude of the geological disaster risk, and the higher the index, the greater the risk. Subsequently, this predicted risk index is compared with a predetermined comparison threshold. The comparison threshold is set based on the physical properties and stability differences of the rock formation data, and it represents a normal or acceptable settlement range. If the predicted risk index exceeds the comparison threshold, it means that the risk of geological disasters is high.

[0040] For example, suppose that the first comprehensive settlement factor and the second comprehensive settlement factor of the first comparison area and the second comparison area within one month have been calculated through the previous steps. Now, substitute these two comprehensive settlement factors into the risk index model to calculate the predicted risk index. This model may consider multiple factors, such as settlement velocity, settlement amount, rock formation type, etc., to comprehensively evaluate the risk of geological disasters. Assume that the calculated predicted risk index is 0.8, and the comparison threshold determined based on the rock formation data is 0.7. Since the predicted risk index exceeds the comparison threshold, it means that the risk of geological disasters may be high. Therefore, it is necessary to further analyze the geological conditions, mining activities and other factors of the two comparison areas to determine the specific risk level and possible disaster types. For example, it may be found that cracks or signs of landslides appear in the rock formations in a certain area, and corresponding prevention and control measures need to be taken to reduce the risk of disasters. Through such a prediction and analysis process, the risk of geological disasters can be more accurately assessed, providing strong support for disaster prevention and mitigation.

[0041] In summary, in the entire geological disaster risk prediction method based on dynamic threshold, by dividing the geological area to be predicted into multiple sequentially connected sub-prediction areas, and further subdividing them into calculation areas, this scheme can more carefully consider the differences and dynamics within the geological area, accurately capture and analyze the geological changes in different sub-areas and calculation areas, and effectively improve the accuracy and precision of the prediction; further, the settlement data at different time points are obtained, which provides reliable basic data for calculating the settlement factor, further enhancing the timeliness and reliability of the prediction. By constructing the first calculation model and the second calculation model, this scheme can comprehensively consider the settlement conditions of multiple calculation areas. The comprehensive settlement factor is calculated based on the situation, which reflects the overall settlement trend of the comparison area in the selected time period; further, by introducing the risk index model and comparison threshold, the size of the geological disaster risk can be quantified, providing an intuitive basis for risk judgment. At the same time, the comparison threshold is set according to the physical properties and stability differences of the rock formation data, representing the normal or acceptable settlement range, making the risk judgment more scientific and reasonable. When the predicted risk index exceeds the comparison threshold, it means that the risk of geological disasters is high, and it is necessary to further analyze factors such as geological conditions and mining activities to determine the specific risk level and possible disaster types, providing strong support for disaster prevention and mitigation.

[0042] like Figure 2 As shown, in one embodiment, obtaining the prediction result according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold in S4 includes:

[0043] S41, obtaining a predicted risk index based on the risk index model, the first comprehensive sedimentation factor, and the second comprehensive sedimentation factor;

[0044] S42. Obtain prediction results according to the prediction risk index and the comparison threshold.

[0045] In this embodiment, it should be noted that, in S41, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor calculated above need to be used as input data and substituted into the risk index model. This risk index model can consider the use of the first comprehensive sedimentation factor and the second comprehensive sedimentation factor to evaluate the size of the geological disaster risk, that is, through the calculation of the model, a quantitative prediction risk index can be obtained, which directly reflects the possibility and severity of the occurrence of geological disasters. The higher the index, the greater the risk of geological disasters, and the more urgent and strict the prevention and control measures that need to be taken.

[0046] For example, in the geological disaster risk prediction of a hillside in a mining area, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor of the first comparison area and the second comparison area within one month are substituted into the risk index model, and a specific predicted risk index is calculated, such as 0.85. This index represents the geological disaster risk level of the area in the current time period.

[0047] In S42, the calculated predicted risk index is first compared with a pre-determined comparison threshold. The comparison threshold is set based on the physical properties and stability differences of the rock formation data. It represents a normal or acceptable settlement range and is also the basis for judging whether the geological disaster risk exceeds the standard. If the predicted risk index exceeds the comparison threshold, it means that the geological disaster risk is high, and further analysis of factors such as geological conditions or mining activities is required to determine the specific risk level and possible disaster type.

[0048] For example, in the previous prediction of geological disaster risk in the mining area, the comparison threshold was assumed to be 0.7, and the calculated prediction risk index was 0.85, which obviously exceeded the comparison threshold. This means that the risk of geological disasters in this area is high, and it may be necessary to further analyze geological conditions such as rock cracks and signs of landslides, as well as factors such as the distribution and intensity of mining activities, to determine the specific risk level and take corresponding prevention and control measures. Through such a prediction and analysis process, the risk of geological disasters can be assessed more accurately, providing strong support for disaster prevention and mitigation.

[0049] like Figure 3 As shown, in one embodiment, obtaining the prediction result according to the prediction risk index and the comparison threshold in S42 includes:

[0050] S421, determining whether the predicted risk index exceeds a comparison threshold;

[0051] S422. If exceeded, the geological disaster risk level is determined based on the excess amount and used to form a prediction result.

[0052] In this embodiment, it should be noted that, in S421, it is determined whether the predicted risk index exceeds the set comparison threshold. This judgment process is based on the predicted risk index obtained by the risk index model based on the first comprehensive settlement factor and the second comprehensive settlement factor calculated in the previous step, and is compared with the comparison threshold pre-set according to the physical properties and stability differences of the rock formation data. If the value of the predicted risk index is higher than the value of the comparison threshold, it indicates that the settlement trend or geological changes in the current geological area during the selected time period exceed the normal or acceptable range, and there is a high risk of geological disasters. For example, in a certain prediction, if the calculated predicted risk index is 0.9, and the comparison threshold is 0.75, then the system or analyst will judge that the predicted risk index exceeds the comparison threshold and enter the next step of risk level assessment.

[0053] In S422, after confirming that the predicted risk index exceeds the comparison threshold, the specific geological disaster risk level is determined according to the amount exceeded, and the final prediction result is formed accordingly. This step needs to convert the degree of exceeding the comparison threshold into a specific risk level, such as low risk, medium risk, high risk or extremely high risk, based on certain standards or rules. These levels can be set according to actual conditions and needs, and usually consider factors such as the severity of the consequences that may be caused by geological disasters, the probability of occurrence, and the scope of impact. Continuing the above example, if the predicted risk index of 0.9 exceeds the comparison threshold of 0.75, and the excess is 0.15, according to the preset risk level classification standard, this excess may correspond to a "high risk" level. Therefore, the final prediction result will be that the geological area has a high risk of geological disasters in the current time period, and prevention and control measures need to be taken immediately to reduce the possibility and impact of disasters.

[0054] In one embodiment, the risk index model in obtaining the predicted risk index based on the risk index model, the first comprehensive sedimentation factor, and the second comprehensive sedimentation factor in S41 is expressed as:

[0055] ;in,

[0056] To predict the risk index, is the first comprehensive sedimentation factor, It is the second comprehensive sedimentation factor.

[0057] In this embodiment, it should be noted that in the numerator, the difference operation The first comprehensive sedimentation factor is calculated and the second comprehensive sedimentation factor The absolute difference between the two comparison areas reflects the difference in the settlement trend of the two comparison areas in the selected time period. The use of absolute values ​​ensures that the difference is always positive, which is convenient for subsequent logarithmic operations. The natural logarithm of the difference is taken after adding 1. Logarithmic operations can enhance the sensitivity of the model to differences in settlement changes, especially when the difference is large. The purpose of adding 1 is to avoid taking the logarithm of 0 in the operation and to ensure that the logarithmic part is defined even when the difference is 0. Exponential operation It is the negative exponent of the difference. The exponential operation maps the difference to a range between (0, 1). When the difference increases, the exponent value decreases rapidly. This helps the model to smooth the growth of the risk index when the difference is large, avoiding excessive impact of extreme values ​​on the results.

[0058] In the denominator, Ensure that the risk index is always within a limited range. When the difference is large, the exponential part in the denominator approaches 0, making the risk index close to the value of the logarithmic part. When the difference is small, the denominator approaches 2, making the risk index smaller.

[0059] Overall operation By combining the results of logarithmic and exponential operations, we obtain a risk index that is both sensitive to settlement differences and within a reasonable range. This index can intuitively reflect the difference in settlement trends between two contrasting areas, as well as the geological hazard risk represented by this difference.

[0060] For example, suppose =2.8766, the second comprehensive sedimentation factor of the second comparison area =2.4, and finally put it into the expression to get ≈0.2401. In this example, the calculated risk index R = 0.2401 indicates that the geological hazard risk represented by the settlement difference between the two contrasting areas during the selected time period is relatively low.

[0061] In one embodiment, the first calculation model in S2 in which the first calculation settlement factor corresponding to the first calculation area is obtained based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area is expressed as:

[0062] ;in,

[0063] is the first calculated settlement factor corresponding to the i-th first calculation area, is the area weight corresponding to the i-th first calculation area, is the first settlement data corresponding to the i-th first calculation area, is the second settlement data corresponding to the i-th first calculation area.

[0064] In this embodiment, it should be noted that the sedimentation data difference This part calculates the change in settlement data of the first calculation area of ​​the ith time at two different time points (the first moment and the second moment); settlement data is usually obtained through field monitoring equipment such as GPS, InSAR or leveling, reflecting the settlement of the ground at a specific time point; by calculating the difference in settlement data at two time points, the change in settlement of the area within the selected time period can be obtained, which is an important indicator for evaluating geological stability. Regional weight It is set according to the geological conditions, mining activities or rock formation data of the first calculation area, and is generally set to 1. Different calculation areas may contribute differently to the overall settlement trend due to differences in geological conditions, mining intensity or other factors. By introducing regional weights, the relative importance of different calculation areas in the overall settlement can be more accurately reflected. The first calculation settlement factor It is the product of the difference in settlement data and the regional weight. It comprehensively considers the settlement change and regional weight, and reflects the settlement trend and geological stability of the i-th first calculation area in the selected time period.

[0065] For example, suppose there is a first comparison area of ​​a mining area slope, which is subdivided into 5 first calculation areas. At the first moment (such as the beginning of the month), the ground subsidence data of each calculation area is obtained using GPS monitoring equipment and recorded as the first subsidence data. At the second time (e.g., the end of the month), monitoring was performed again and the second settlement data was obtained. At the same time, regional weights are set according to the geological conditions and mining activities in each calculation area. . , , , and They are 5, 3, 2, 4, 1 respectively; , , , and They are 7, 5, 4, 6, and 3 respectively. Regional weight , , , and They are 0.8, 1.0, 0.9, 1.2, and 0.7 respectively. Finally, we get , , , and They are 1.6, 2.0, 1.8, 2.4 and 1.4 respectively.

[0066] It should also be noted that, similar to the present embodiment, the first calculation model can also be used to calculate the second calculation sedimentation factor.

[0067] In one embodiment, the second calculation model in S2 for obtaining the first comprehensive sedimentation factor based on the second calculation model and the first calculation sedimentation factors corresponding to each first calculation area is expressed as:

[0068] ;in,

[0069] is the first comprehensive sedimentation factor, is the number of the first calculation area, is the first calculated settlement factor corresponding to the i-th first calculation area.

[0070] In this embodiment, it should be noted that: This section is the first calculation settlement factor for all first calculation areas and its logarithmically transformed value are summed; the summation operation can comprehensively consider the settlement of all calculation areas and ensure that the first comprehensive settlement factor It reflects the overall subsidence trend of the entire first comparison area. The introduction of is to enhance the sensitivity of the model to the amount of settlement changes, especially when the amount of settlement changes is large; at the same time, the contribution of this sensitivity is relatively smooth, avoiding extreme values ​​from having too much impact on the overall results; at the same time, the logarithmic transformation can relatively amplify smaller settlement changes, so that these changes can also be reflected in the comprehensive settlement factor. Among them, This ensures that the sign of the settlement change will not affect the result of the logarithmic transformation, because the domain of the logarithmic function is positive. By taking the absolute value, the model can be simplified in processing settlement data without considering the direction of settlement (upward or downward).

[0071] The first comprehensive sedimentation factor It represents the average settlement trend of all the first calculation areas. Through the average operation, the influence of different numbers of calculation areas on the comprehensive settlement factor can be eliminated, making the results more comparable.

[0072] For example, after the data in the above implementation is input, the calculation results are 14.383=2.8766.

[0073] It should also be noted that, similar to the present embodiment, the second calculation model can also be used to calculate the second comprehensive sedimentation factor.

[0074] A geological disaster risk prediction system based on dynamic thresholds is also provided, the system comprising:

[0075] An acquisition module, used to acquire the geological area to be predicted and the corresponding geological information, and divide the geological area to be predicted into a plurality of sequentially connected sub-prediction areas, and acquire rock formation data of the sub-areas according to the geological information, and use any two sub-prediction areas with the same and adjacent rock formation data as the first comparison area and the second comparison area, and acquire a first moment and a second moment separated by a first time period according to the rock formation data, and acquire a comparison threshold according to the rock formation data;

[0076] A first data processing module is used to divide the first comparison area into a plurality of first calculation areas, obtain first settlement data corresponding to each first calculation area at a first moment, obtain second settlement data corresponding to each first calculation area at a second moment, obtain a first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area, and obtain a first comprehensive settlement factor based on the second calculation model and the first calculated settlement factors corresponding to each first calculation area;

[0077] A second data processing module is used to divide the second comparison area into a plurality of second calculation areas, obtain third settlement data corresponding to each second calculation area at a first moment, obtain fourth settlement data corresponding to each second calculation area at a second moment, obtain a second calculated settlement factor corresponding to the second calculation area based on the first calculation model, the third settlement data corresponding to the second calculation area, and the fourth settlement data corresponding to the second calculation area, and obtain a second comprehensive settlement factor based on the second calculation model and the second calculated settlement factors corresponding to each second calculation area;

[0078] The prediction and analysis module is used to obtain prediction results based on the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold.

[0079] In one embodiment, the prediction analysis module is also used to: obtain a predicted risk index based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor; and obtain a prediction result according to the predicted risk index and a comparison threshold.

[0080] In one embodiment, the prediction analysis module is also used to: determine whether the predicted risk index exceeds a comparison threshold; if exceeded, determine the geological disaster risk level based on the excess amount, and form a prediction result based on this.

[0081] In this embodiment, it should be noted that regarding the above-mentioned geological hazard risk prediction system based on dynamic thresholds, the specific method of performing operations has been described in detail in the implementation of the geological hazard risk prediction method based on dynamic thresholds, and will not be elaborated here.

[0082] Figure 4 is a block diagram of an electronic device for a method for predicting geological disaster risks based on a dynamic threshold according to an exemplary embodiment. Figure 4 As shown, the electronic device 700 may include: a processor 701 and a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an I / O interface 704, and a communication component 705.

[0083] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned method for predicting geological disaster risks based on dynamic thresholds. The memory 702 is used to store various types of data to support the operation of the electronic device 700, and these data may include, for example, instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, which is used to receive external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0084] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned dynamic threshold-based geological disaster risk prediction method.

[0085] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for predicting geological hazards risk based on dynamic thresholds are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned method for predicting geological hazards risk based on dynamic thresholds.

[0086] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned dynamic threshold-based geological hazard risk prediction method when executed by the programmable device.

[0087] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0088] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0089] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting geological disaster risks based on dynamic thresholds, characterized in that: include: Acquire a geological region to be predicted and corresponding geological information, divide the geological region to be predicted into a plurality of sequentially connected sub-prediction regions, acquire rock formation data of the sub-regions according to the geological information, and use any two sub-prediction regions with the same and adjacent rock formation data as a first comparison region and a second comparison region, acquire a first moment and a second moment separated by a first time period according to the rock formation data, and acquire a comparison threshold according to the rock formation data; Divide the first comparison area into a plurality of first calculation areas, obtain first settlement data corresponding to each first calculation area at a first moment, obtain second settlement data corresponding to each first calculation area at a second moment, obtain a first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area, and obtain a first comprehensive settlement factor based on the second calculation model and the first calculated settlement factors corresponding to each first calculation area; The first calculation model in obtaining the first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area and the second settlement data corresponding to the first calculation area is expressed as: ;in, is the first calculated settlement factor corresponding to the i-th first calculation area, is the area weight corresponding to the i-th first calculation area, is the first settlement data corresponding to the i-th first calculation area, is the second settlement data corresponding to the i-th first calculation area; The second calculation model in obtaining the first comprehensive sedimentation factor based on the second calculation model and the first calculation sedimentation factors corresponding to each first calculation area is expressed as: ;in, is the first comprehensive sedimentation factor, is the number of the first calculation area, is the first calculated settlement factor corresponding to the i-th first calculation area; Divide the second comparison area into a plurality of second calculation areas, obtain third settlement data corresponding to each second calculation area at a first moment, obtain fourth settlement data corresponding to each second calculation area at a second moment, obtain a second calculated settlement factor corresponding to the second calculation area based on the first calculation model, the third settlement data corresponding to the second calculation area, and the fourth settlement data corresponding to the second calculation area, and obtain a second comprehensive settlement factor based on the second calculation model and the second calculated settlement factors corresponding to each second calculation area; The prediction result is obtained according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold; the prediction result is obtained according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold, including: obtaining a predicted risk index based on a risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor; obtaining a prediction result according to the predicted risk index and the comparison threshold; the risk index model in the prediction risk index obtained based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor is expressed as: ;in, To predict the risk index, is the first comprehensive sedimentation factor, It is the second comprehensive sedimentation factor.

2. The method for predicting geological disaster risks based on dynamic threshold according to claim 1, characterized in that: The obtaining of prediction results according to the prediction risk index and the comparison threshold comprises: Determine whether the predicted risk index exceeds the comparison threshold; If it exceeds the limit, the geological disaster risk level will be determined based on the amount of excess and used to form the prediction result.

3. A geological disaster risk prediction system based on dynamic threshold, characterized in that: The system comprises: An acquisition module, used to acquire the geological area to be predicted and the corresponding geological information, and divide the geological area to be predicted into a plurality of sequentially connected sub-prediction areas, and acquire rock formation data of the sub-areas according to the geological information, and use any two sub-prediction areas with the same and adjacent rock formation data as the first comparison area and the second comparison area, and acquire a first moment and a second moment separated by a first time period according to the rock formation data, and acquire a comparison threshold according to the rock formation data; A first data processing module is used to divide the first comparison area into a plurality of first calculation areas, obtain first settlement data corresponding to each first calculation area at a first moment, obtain second settlement data corresponding to each first calculation area at a second moment, obtain a first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area, and the second settlement data corresponding to the first calculation area, and obtain a first comprehensive settlement factor based on the second calculation model and the first calculated settlement factors corresponding to each first calculation area; The first calculation model in obtaining the first calculated settlement factor corresponding to the first calculation area based on the first calculation model, the first settlement data corresponding to the first calculation area and the second settlement data corresponding to the first calculation area is expressed as: ;in, is the first calculated settlement factor corresponding to the i-th first calculation area, is the area weight corresponding to the i-th first calculation area, is the first settlement data corresponding to the i-th first calculation area, is the second settlement data corresponding to the i-th first calculation area; The second calculation model in obtaining the first comprehensive sedimentation factor based on the second calculation model and the first calculation sedimentation factors corresponding to each first calculation area is expressed as: ;in, is the first comprehensive sedimentation factor, is the number of the first calculation area, is the first calculated settlement factor corresponding to the i-th first calculation area; A second data processing module is used to divide the second comparison area into a plurality of second calculation areas, obtain third settlement data corresponding to each second calculation area at a first moment, obtain fourth settlement data corresponding to each second calculation area at a second moment, obtain a second calculated settlement factor corresponding to the second calculation area based on the first calculation model, the third settlement data corresponding to the second calculation area, and the fourth settlement data corresponding to the second calculation area, and obtain a second comprehensive settlement factor based on the second calculation model and the second calculated settlement factors corresponding to each second calculation area; The prediction analysis module is used to obtain the prediction result according to the first comprehensive sedimentation factor, the second comprehensive sedimentation factor and the comparison threshold; the prediction analysis module is also used to: obtain the predicted risk index based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor; obtain the prediction result according to the predicted risk index and the comparison threshold; the risk index model in obtaining the predicted risk index based on the risk index model, the first comprehensive sedimentation factor and the second comprehensive sedimentation factor is expressed as: ;in, To predict the risk index, is the first comprehensive sedimentation factor, It is the second comprehensive sedimentation factor.

4. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the computer program in the memory to implement the geological disaster risk prediction method based on dynamic threshold as described in claim 1 or claim 2.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for predicting geological disaster risks based on dynamic thresholds described in claim 1 or claim 2 is implemented.

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