Geological disaster identification method and system based on multi-source data fusion

Abstract

The invention provides a geological disaster recognition method and system based on multi-source data fusion, and relates to the technical field of reservoir geological risks, reservoir data are collected, correlation analysis is performed on the data, iteration calculation is performed through multiple iterations and a set learning rate, and therefore a geological disaster probability occurrence value used for reflecting the reservoir geological disaster probability is generated; according to the method, the coordinate height, the gradient, the side slope depth and the underground water level depth of a reservoir side slope are collected, correlation analysis processing is carried out on data of the coordinate height, the gradient, the side slope depth and the underground water level depth of each day in combination with collected 360-time data, and an iteration probability regression index is generated through data probability iteration; and the minimum value of the iteration probability regression index is obtained, so that the geological disaster probability occurrence value for reflecting the reservoir geological disaster probability is generated, the geological disaster probability is automatically output, and workers are helped to better grasp the reservoir geological disaster risk.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir geological risk, and in particular to a geological disaster identification method and system based on multi-source data fusion. Background Art

[0002] Reservoir geological risk refers to the potential threats to the reservoir and its surrounding environment, infrastructure and human activities caused by geological factors. These risks may affect the safe operation of the reservoir, leading to geological disasters such as dam breach, debris flow, landslide, and even cause serious impacts on downstream areas.

[0003] In general reservoir risk assessment, only the coordinate height, inclination, slope depth, and groundwater level depth data are monitored. Emergency warnings are only issued when the relevant data exceeds the threshold. Since geological disasters are often not the result of a single factor, and this monitoring method does not process the data comprehensively, the errors in emergency warnings are large and emergency warnings cannot be issued accurately.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a geological disaster identification method and system based on multi-source data fusion to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The multi-source data fusion geological disaster identification method is used to analyze the probability of occurrence of geological disasters on reservoir slopes, and is characterized in that the specific steps include:

[0008] S1. Collecting initial data of the reservoir, wherein the initial data includes initial coordinate height of the slope, initial slope inclination, initial depth of the slope, and initial groundwater level depth of the slope;

[0009] S2. Collect reservoir data once a day, analyze and process it with the initial reservoir data to obtain reservoir deviation data, and number the reservoir data according to the number of days. The reservoir deviation data includes the coordinate height deviation of the highest point of the slope, the slope inclination deviation, the slope depth deviation, and the slope groundwater level depth deviation. The number of days for collection is 360 days.

[0010] S3, respectively performing correlation analysis on the coordinate height deviation of the highest point of the slope, the slope inclination deviation, the slope depth deviation, and the slope groundwater level depth deviation, to generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrating the normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data of each day into classification groups;

[0011] S4. For all the classification groups, the geological disaster probability is set respectively, and the disaster occurrence probability prediction value is set, and the correlation analysis of the geological disaster probability is performed to generate the initial probability;

[0012] S5. Establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index;

[0013] S6. Set the learning rate, perform correlation analysis on the k-th iteration probability regression index, generate a geological disaster probability occurrence value, and the geological disaster probability occurrence value is used to reflect the probability of geological disasters. Input the reservoir data collected in real time into the iteration model and output the geological disaster probability occurrence value.

[0014] Furthermore, the initial coordinate height A0 of the slope is determined by satellite GPS positioning, the initial inclination B0 of the slope is measured by an inclinometer, the initial depth C0 of the slope is measured by a deep displacement sensor, and the initial groundwater level D0 of the slope is measured by a water level sensor.

[0015] Furthermore, the coordinate height deviation of the highest point of the slope on the i-th day is expressed as A i The slope inclination deviation on the i-th day is expressed as B i The slope depth deviation on the i-th day is expressed as C i The groundwater level deviation of the slope on the ith day is expressed as D i Represents, where the subscript i is used as the index of day i.

[0016] Furthermore, the slope highest point coordinate height deviation A i , Slope inclination deviation B i , Slope depth deviation C i , slope groundwater level depth deviation D i Perform correlation analysis respectively to generate normalized coordinate height A′ i , normalized slope B′ i , normalized slope depth C′ i , Normalized groundwater level depth D′ i , the formula based on is:

[0017]

[0018] Among them, max{A i} and min{A i} respectively represent the maximum and minimum values ​​of the coordinate height deviation of the highest point of all slopes, max{B i} and min{B i} are used to represent the total slope inclination deviation B i The maximum and minimum values ​​of max{C i} and min{C i} are used to represent the total slope depth deviation C i The maximum and minimum values ​​of max{D i} and min{D i} are used to represent the groundwater level deviation D of the entire slope. i The maximum and minimum values ​​of the normalized coordinate height A′ i , normalized slope B′ i , normalized slope depth C′ i , Normalized groundwater level depth D′ i Both are used to ensure data consistency, the slope highest point coordinate height deviation A i It is used to reflect the difference between the coordinate height of the highest point on the i-th day and the initial coordinate height of the slope, and the slope inclination deviation B i It is used to reflect the deviation value of the slope inclination on the i-th day from the initial slope inclination and the slope depth deviation C i It is used to reflect the deviation value between the slope depth on the ith day and the initial slope depth, and the slope groundwater level depth deviation D i It is used to reflect the deviation between the depth of the slope groundwater level on the ith day and the initial slope groundwater level.

[0019] Furthermore, in S3, the normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth classification group of the i-th day are set as T i , for all classification groups T i Set the geological disaster probability y i , for the probability of geological disasters y i Perform correlation analysis to generate the initial probability F0, based on the formula:

[0020]

[0021] Perform K iterations, where the output probability of the k-1th iteration is F k-1 , set the residual of the kth iteration to The formula is:

[0022]

[0023] Among them, the residual of the kth iteration Used to reflect the difference between the output probability and the geological disaster probability.

[0024] Furthermore, the kth iteration probability regression index is defined as h k , the formula based on is:

[0025]

[0026] in, It is used to reflect the sum of squares of the differences between the residuals of all samples and the probability of geological disasters. Arg in is used to find the target formula The minimum value in h is obtained by computer software XGBoost k .

[0027] Furthermore, the learning rate μ is set to 0.5, and the correlation analysis is performed on the k-th iteration probability regression index to generate the geological disaster probability occurrence value s. The formula is as follows:

[0028]

[0029] Among them, the value of K is not less than 10, and the geological disaster probability occurrence value s is used to reflect the predicted probability of geological disasters, and its value range is [0,1].

[0030] Furthermore, when the geological disaster probability occurrence value s is 0, it means that a geological disaster will not occur, and when the geological disaster probability occurrence value s is 1, it means that a geological disaster will occur.

[0031] The present invention also provides a geological disaster identification system of multi-source data fusion, which is used to execute a geological disaster identification method of multi-source data fusion, comprising:

[0032] Initial data collection module, used to collect initial reservoir data;

[0033] The reservoir data collection module is used to collect reservoir data once a day and number the reservoir data according to the number of days;

[0034] The normalization module is used to perform correlation analysis on the deviation of the coordinate height of the highest point of the slope, the deviation of the slope inclination, the deviation of the slope depth, and the deviation of the groundwater level depth of the slope, and generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrate the daily normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data into classification groups;

[0035] The evaluation module is used to set the geological hazard probability y for all classification groups. i, conduct correlation analysis on the probability of geological disasters and generate initial probability;

[0036] The iteration module is used to establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index;

[0037] The output module performs correlation analysis on the k-th iteration probability regression index to generate a geological disaster probability occurrence value, inputs the real-time collected reservoir data into the iteration model, and outputs the geological disaster probability occurrence value.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention is based on;

[0040] The present invention collects the coordinate height, inclination, slope depth and groundwater level of the reservoir slope, and performs correlation analysis on the daily coordinate height, inclination, slope depth and groundwater level data in combination with the collected data 360 times. It generates an iterative probability regression index through data probability iteration, and obtains the minimum value of the iterative probability regression index, thereby generating a geological disaster probability occurrence value for reflecting the probability of geological disasters in the reservoir, automatically outputting the geological disaster probability, and helping staff to better grasp the geological disaster risks of the reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0042] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0044] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] Example:

[0046] See also Figure 1 , the present invention provides a technical solution:

[0047] The multi-source data fusion geological disaster identification method is used to analyze the probability of geological disasters on reservoir slopes. With the help of new generation information technologies such as the Internet of Things, cloud computing, big data, and artificial intelligence, the geological disaster-related data is edge-calculated. The reservoir data is collected through the data collection gateway of the Internet of Things, and the data is correlated and processed to output the geological disaster risk assessment value. A database is established through data collection, and the data in the database is analyzed to generate the geological disaster probability occurrence value and evaluate the probability of reservoir geological disasters. The specific steps include:

[0048] Step 1, collecting initial data of the reservoir, the initial data includes the initial coordinate height of the slope, the initial inclination of the slope, the initial depth of the slope, and the initial groundwater level depth of the slope; the initial coordinate height A0 of the slope is determined by satellite GPS positioning, the initial inclination B0 of the slope is measured by an inclinometer, the initial depth C0 of the slope is measured by a deep displacement sensor, and the initial groundwater level depth D0 of the slope is measured by a water level sensor.

[0049] Step 2: Collect reservoir data once a day, analyze and process it with the initial reservoir data to obtain reservoir deviation data, and number the reservoir data according to the number of days. The reservoir deviation data includes the coordinate height deviation of the highest point of the slope, the slope inclination deviation, the slope depth deviation, and the slope groundwater level depth deviation. The number of days for collection is 360 days; the coordinate height deviation of the highest point of the slope on the i-th day is represented by A i The slope inclination deviation on the i-th day is expressed as B i The slope depth deviation on the i-th day is expressed as C iThe groundwater level deviation of the slope on the ith day is expressed as D i Represents, where the subscript i is used as the index of day i.

[0050] Step 3, respectively, performing correlation analysis on the slope highest point coordinate height deviation, slope inclination deviation, slope depth deviation, and slope groundwater level depth deviation to generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrating the daily normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data into classification groups;

[0051] Coordinate height deviation of the highest point of the slope A i , Slope inclination deviation B i , Slope depth deviation C i , slope groundwater level depth deviation D i Perform correlation analysis respectively to generate normalized coordinate height A′ i , normalized slope B′ i , normalized slope depth C′ i , Normalized groundwater level depth D′ i , the formula based on is:

[0052]

[0053] Among them, max{A i} and min{A i} respectively represent the maximum and minimum values ​​of the coordinate height deviation of the highest point of all slopes, max{B i} and min{B i} are used to represent the total slope inclination deviation B i The maximum and minimum values ​​of max{C i} and min{C i} are used to represent the total slope depth deviation C i The maximum and minimum values ​​of max{D i} and min{D i} are used to represent the groundwater level deviation D of the entire slope. i The maximum and minimum values ​​of the normalized coordinate height A′ i , normalized slope B′ i , normalized slope depth C′ i , Normalized groundwater level depth D′ i Both are used to ensure data consistency, the slope highest point coordinate height deviation A i It is used to reflect the difference between the coordinate height of the highest point on the i-th day and the initial coordinate height of the slope, and the slope inclination deviation b iIt is used to reflect the deviation value of the slope inclination on the i-th day from the initial slope inclination and the slope depth deviation C i It is used to reflect the deviation value of the slope depth on the i-th day from the initial slope depth and the slope groundwater level depth deviation d i It is used to reflect the deviation between the depth of the slope groundwater level on the ith day and the initial slope groundwater level.

[0054] Step 4: For all classification groups, the probability of geological disasters is set respectively, and the predicted value of the probability of disaster occurrence is set, and the correlation analysis of the probability of geological disasters is performed to generate the initial probability;

[0055] The normalized coordinate height, normalized slope, normalized slope depth, and normalized groundwater level depth classification group of the i-th day are set as T i , for all classification groups T i Set the geological disaster probability y i The method of obtaining the probability of geological disasters is as follows: Geological experts calculate the slope height deviation A according to the coordinate height deviation of the highest point of the slope. i , Slope inclination deviation B i , Slope depth deviation C i , slope groundwater level depth deviation D i The numerical value sets the probability of the corresponding disaster occurring, and the average value of the probability of the corresponding disaster occurring is taken, and the average value is the geological disaster probability y i , for the probability of geological disasters y i Perform correlation analysis to generate the initial probability F0, based on the formula:

[0056]

[0057] Perform K iterations, where the output probability of the k-1th iteration is F k-1 , set the residual of the kth iteration to The formula is:

[0058]

[0059] Among them, the residual of the kth iteration Used to reflect the difference between the output probability and the geological disaster probability.

[0060] Step 5: Establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index;

[0061] Define the kth iteration probability regression index as h k , the formula based on is:

[0062]

[0063] in, It is used to reflect the sum of squares of the differences between the residuals of all samples and the probability of geological disasters. Arg in is used to find the target formula The minimum value in h is obtained by computer software XGBoost k .

[0064] Step 6: Set the learning rate, perform correlation analysis on the k-th iteration probability regression index, generate a geological disaster probability occurrence value, which is used to reflect the probability of geological disasters. Input the reservoir data collected in real time into the iteration model and output the geological disaster probability occurrence value.

[0065] The learning rate μ is set to 0.5, and the correlation analysis is performed on the k-th iteration probability regression index to generate the geological disaster probability occurrence value s. The formula is as follows:

[0066]

[0067] Among them, the value of K is not less than 10, and the geological disaster probability occurrence value s is used to reflect the predicted probability of geological disasters, and its value range is [0,1].

[0068] Input the latest obtained slope highest point coordinate height deviation, slope inclination deviation, slope depth deviation, slope groundwater level depth deviation, and refer to the slope highest point coordinate height deviation A according to the slope highest point coordinate height deviation A. i , Slope inclination deviation B i , Slope depth deviation C i , slope groundwater level depth deviation D i The numerical values ​​are set for the probabilities of the corresponding disasters that may occur, and the probabilities of the corresponding four parameters that may cause the disasters to occur are obtained. The probabilities of the four parameters that may cause the disasters to occur are input into the iterative model, and it is iterated to output the probability value s of the geological disaster.

[0069] When the geological disaster probability occurrence value s is 0, it means that a geological disaster will not occur. When the geological disaster probability occurrence value s is 1, it means that a geological disaster will occur. The larger the s value is, the higher the probability of a geological disaster. The geological disaster probability occurrence value s indicates the predicted probability of a geological disaster.

[0070] Reference Figure 2 The present invention also provides a geological disaster identification system of multi-source data fusion, which is used to execute a geological disaster identification method of multi-source data fusion, including:

[0071] Initial data collection module, used to collect initial reservoir data;

[0072] The reservoir data collection module is used to collect reservoir data once a day and number the reservoir data according to the number of days;

[0073] The normalization module is used to perform correlation analysis on the deviation of the coordinate height of the highest point of the slope, the deviation of the slope inclination, the deviation of the slope depth, and the deviation of the groundwater level depth of the slope, and generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrate the daily normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data into classification groups;

[0074] The evaluation module is used to set the geological hazard probability y for all classification groups. i , conduct correlation analysis on the probability of geological disasters and generate initial probability;

[0075] The iteration module is used to establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index;

[0076] The output module performs correlation analysis on the k-th iteration probability regression index to generate a geological disaster probability occurrence value, inputs the real-time collected reservoir data into the iteration model, and outputs the geological disaster probability occurrence value.

[0077] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0078] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0079] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A multi-source data fusion geological disaster identification method is used to analyze the probability of occurrence of geological disasters on reservoir slopes, which is characterized by: The specific steps include: S1. Collecting initial data of the reservoir, wherein the initial data includes initial coordinate height of the slope, initial slope inclination, initial depth of the slope, and initial groundwater level depth of the slope; S2. Collect reservoir data once a day, analyze and process it with the initial reservoir data to obtain reservoir deviation data, and number the reservoir data according to the number of days. The reservoir deviation data includes the coordinate height deviation of the highest point of the slope, the slope inclination deviation, the slope depth deviation, and the slope groundwater level depth deviation. The number of days for collection is 360 days. S3, respectively performing correlation analysis on the coordinate height deviation of the highest point of the slope, the slope inclination deviation, the slope depth deviation, and the slope groundwater level depth deviation, to generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrating the normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data of each day into classification groups; S4. For all the classification groups, the geological disaster probability is set respectively, and the disaster occurrence probability prediction value is set, and the correlation analysis of the geological disaster probability is performed to generate the initial probability; S5. Establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index; S6. Set the learning rate, perform correlation analysis on the k-th iteration probability regression index, generate a geological disaster probability occurrence value, and the geological disaster probability occurrence value is used to reflect the probability of geological disasters. Input the reservoir data collected in real time into the iteration model and output the geological disaster probability occurrence value.

2. The method for identifying geological hazards by multi-source data fusion according to claim 1, characterized in that: The initial coordinate height A0 of the slope is determined by satellite GPS positioning, the initial inclination B0 of the slope is measured by an inclinometer, the initial depth C0 of the slope is measured by a deep displacement sensor, and the initial groundwater level D0 of the slope is measured by a water level sensor.

3. The method for identifying geological hazards by multi-source data fusion according to claim 2 is characterized in that: The coordinate height deviation of the highest point of the slope on the i-th day is expressed as A i The slope inclination deviation on the i-th day is expressed as B i The slope depth deviation on the i-th day is expressed as C i The groundwater level deviation of the slope on the ith day is expressed as D i Represents, where the subscript i is used as the index of day i.

4. The method for identifying geological hazards by multi-source data fusion according to claim 3 is characterized in that: Coordinate height deviation of the highest point of the slope A i , Slope inclination deviation B i , Slope depth deviation C i , slope groundwater level depth deviation D i Perform correlation analysis respectively to generate normalized coordinate height A ′ i , normalized slope B i ′ , Normalized slope depth C i ′ , Normalized groundwater level depth D i ′ , the formula based on is: Among them, max{A i } and min{A i } respectively represent the maximum and minimum values ​​of the coordinate height deviation of the highest point of all slopes, max{B i } and min{B i } are used to represent the total slope inclination deviation B i The maximum and minimum values ​​of max{C i } and min{C i } are used to represent the total slope depth deviation C i The maximum and minimum values ​​of max{D i } and min{D i } are used to represent the groundwater level deviation D of the entire slope. i The maximum and minimum values ​​of the normalized coordinate height A ′ i , normalized slope B i ′ , Normalized slope depth C i ′ , Normalized groundwater level depth D i ′ Both are used to ensure data consistency, the slope highest point coordinate height deviation A i It is used to reflect the difference between the coordinate height of the highest point of the slope on the i-th day and the initial coordinate height of the slope, and the slope inclination deviation B i It is used to reflect the deviation value of the slope inclination on the i-th day from the initial slope inclination and the slope depth deviation C i It is used to reflect the deviation value between the slope depth on the ith day and the initial slope depth, and the slope groundwater level depth deviation D i It is used to reflect the deviation between the depth of the slope groundwater level on the ith day and the initial slope groundwater level.

5. The method for identifying geological hazards by multi-source data fusion according to claim 4 is characterized in that: In S3, the normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth classification group of the i-th day are set as T i , for all classification groups T i Set the geological disaster probability y i , for the probability of geological disasters y i Perform correlation analysis to generate the initial probability F0, based on the formula: Perform K iterations, where the output probability of the k-1th iteration is F k-1 , set the residual of the kth iteration to r i (k) , the formula based on is: r i (k) =y i -F k-1 Among them, the residual r of the kth iteration i (k) Used to reflect the difference between the output probability and the geological disaster probability.

6. The method for identifying geological hazards by multi-source data fusion according to claim 5 is characterized by: Define the kth iteration probability regression index as h k , the formula based on is: in, It is used to reflect the sum of squares of the differences between the residuals of all samples and the probability of geological disasters. Arg in is used to find the target formula The minimum value in h is obtained by computer software XGBoost k .

7. The method for identifying geological hazards by multi-source data fusion according to claim 6 is characterized by: The learning rate μ is set to 0.5, and the correlation analysis is performed on the k-th iteration probability regression index to generate the geological disaster probability occurrence value s. The formula is as follows: Among them, the value of K is not less than 10, and the geological disaster probability occurrence value s is used to reflect the predicted probability of geological disasters, and its value range is [0,1].

8. The method for identifying geological hazards by multi-source data fusion according to claim 7 is characterized by: When the geological disaster probability value s is 0, it means that no geological disaster will occur. When the geological disaster probability value s is 1, it means that a geological disaster will occur.

9. A geological disaster identification system based on multi-source data fusion, used to execute the geological disaster identification method based on multi-source data fusion according to claim 1, characterized in that: include: Initial data collection module, used to collect initial reservoir data; The reservoir data collection module is used to collect reservoir data once a day and number the reservoir data according to the number of days; The normalization module is used to perform correlation analysis on the deviation of the coordinate height of the highest point of the slope, the deviation of the slope inclination, the deviation of the slope depth, and the deviation of the groundwater level depth of the slope, and generate normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth, and integrate the daily normalized coordinate height, normalized inclination, normalized slope depth, and normalized groundwater level depth data into classification groups; The evaluation module is used to set the geological hazard probability y for all classification groups. i , conduct correlation analysis on the probability of geological disasters and generate initial probability; The iteration module is used to establish an iterative model, perform K iterations on the initial probability, output the residual of the kth iteration, and define the kth iteration probability regression index; The output module performs correlation analysis on the k-th iteration probability regression index to generate a geological disaster probability occurrence value, inputs the real-time collected reservoir data into the iteration model, and outputs the geological disaster probability occurrence value.

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