A method and system for monitoring geological disasters in scenic areas based on big data

Abstract

The present invention provides a scenic area geological disaster monitoring method and system based on big data, relating to the field of geological disaster early warning technology. The purpose is to establish a more reliable correspondence between data types and geological disaster types, thereby achieving more accurate geological disaster monitoring. The method includes setting up multiple geological disaster monitoring data sets and multiple geological disaster types to be predicted; establishing a mapping relationship table corresponding to each geological disaster type based on the scope of the geological disaster monitoring data obtained from the scenic area's big data and the degree of influence on the occurrence trend of the geological disaster type obtained from big data from multiple regions; and performing predictions for each geological disaster type to be predicted using a disaster prediction model. The present invention has the advantages of being more targeted and flexible, and providing more accurate and reliable geological disaster-related analysis.

Description

Technical Field

[0001] The present invention relates to the field of geological disaster early warning technology, and in particular to a scenic area geological disaster monitoring method and system based on big data. Background Art

[0002] Geological disaster monitoring is an important means to ensure outdoor safety and reduce accident risks today.

[0003] Scenic areas are particularly densely populated, creating an even greater need for geological disaster monitoring. However, geological disaster monitoring in scenic areas faces several challenges. Geological disaster monitoring typically requires the collection of multiple data types, and different types of geological disasters require different data selections. Determining the type of data required for a given geological disaster requires capturing relationships between large amounts of data. However, capturing data relationships in a specific scenic area is limited due to the limited size of the scenic area, which can result in insufficient data and inaccurate data analysis results. On the other hand, large-scale data collection beyond the scenic area can address data volume limitations, but the targeting of the scenic area's unique characteristics will be weakened, similarly reducing the reliability of data analysis. All of these deficiencies can impact the accuracy of disaster predictions.

[0004] Therefore, it is necessary to optimize the geological disaster monitoring in scenic areas, establish a more reliable correspondence between data types and geological disaster types, and achieve more accurate geological disaster monitoring. Summary of the Invention

[0005] The purpose of the present invention is to provide a scenic area geological disaster monitoring method and system based on big data, which can establish a more reliable correspondence between data types and geological disaster types and achieve more accurate geological disaster monitoring.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for monitoring geological disasters in scenic areas based on big data, comprising the following steps:

[0008] Set up a variety of geological disaster monitoring data and a variety of geological disaster types to be predicted;

[0009] According to the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of geological disaster types obtained based on the big data of multiple regions, the influencing factors are obtained, and a mapping relationship table corresponding to multiple geological disaster monitoring data and one geological disaster type is established;

[0010] The disaster prediction model is used to predict each type of geological disaster to be predicted.

[0011] Preferably, the method for establishing a mapping relationship between multiple geological disaster monitoring data and one geological disaster type includes the following steps:

[0012] Obtaining the influence factors of each type of geological disaster monitoring data on the geological disaster type respectively, wherein the influence factors include an occurrence trend influence factor and an action range influence factor, and a larger value of the occurrence trend influence factor represents a greater degree of influence on the occurrence trend, and a larger value of the action range influence factor represents a greater degree of influence on the action range;

[0013] The mapping relationship is determined based on the influencing factors of each type of geological disaster monitoring data and the geological disaster type.

[0014] Preferably, the impact factor is the product of the occurrence trend impact factor and the scope impact factor:

[0015] ;

[0016] in, Representative The geological disaster monitoring data mentioned above is The influencing factors of the above-mentioned geological disaster types, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of action of the above-mentioned geological disaster types.

[0017] Preferably, the method for obtaining the occurrence trend influencing factor is:

[0018] ;

[0019] ;

[0020] in, For the first The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is From multiple regions The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is is the total number of types of geological disaster monitoring data, is the natural logarithm function, To find the function of the Pearson correlation coefficient of two parameters, For the first The time series data sequence of the geological disaster monitoring data, For the first The time series data of the geological disaster types mentioned above, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of the geological disaster type;

[0021] The method for obtaining the scope influence factor is:

[0022] ;

[0023] in, is a natural constant, is the total area of ​​the scenic area, For the The area within the scenic area where the geological disaster monitoring data mentioned above is effective.

[0024] Preferably, the method for determining the mapping relationship table based on the influencing factors of the geological disaster types according to each of the geological disaster monitoring data is:

[0025] Preset impact factor threshold;

[0026] Jordi The geological disaster monitoring data mentioned above is If the impact factor of the geological disaster type is greater than the impact factor threshold, the The geological disaster monitoring data is put into the A mapping relationship table of the geological disaster types.

[0027] Preferably, the disaster prediction model includes an input layer, a data cleaning layer, a semantic matching layer, a feature extraction layer and a recognition layer;

[0028] The input layer is used to receive the geological disaster type to be predicted, multiple geological disaster monitoring data for N consecutive days and the pre-stored mapping relationship table;

[0029] The function of the data cleaning layer is: if any geological disaster occurs in N consecutive days, the multiple geological disaster monitoring data of the corresponding date will be cleared and the cleared data will be supplemented by the Kalman filter;

[0030] The function of the semantic matching layer is:

[0031] The name of each input geological disaster monitoring data is converted into a word vector by using the Word2Vec function;

[0032] Obtaining the word vector of the geological disaster monitoring data required for prediction according to the type of geological disaster to be predicted through the mapping relationship table;

[0033] Performing semantic matching through word vectors to select the geological disaster monitoring data required for prediction from the input geological disaster monitoring data;

[0034] The feature extraction layer is used to extract features based on the geological disaster monitoring data required for the prediction to obtain multiple feature values;

[0035] The recognition layer is used to perform nonlinearization on the characteristic value and output the prediction result of the geological disaster based on the activation function.

[0036] Preferably, the method for extracting features from the geological disaster monitoring data required for the prediction is:

[0037] ;

[0038] ;

[0039] ;

[0040] in, for and The eigenvalue array formed, and Prediction The first of the geological disaster monitoring data required for the geological disaster type The first characteristic value and the second characteristic value of the geological disaster monitoring data are To find the standard deviation function, For N consecutive days The first of the geological disaster monitoring data required for the geological disaster type An array of geological disaster monitoring data, and They are Middle and elements.

[0041] Preferably, the activation function is:

[0042] ;

[0043] ;

[0044] in, For the The output of the activation function of the identification layer of the geological disaster type, Represents the input parameters of the activation function after nonlinearization, Represents the geological disaster monitoring data required for prediction The geological disaster monitoring data The nonlinear output of the eigenvalue, , The total amount of geological disaster monitoring data required for prediction, is a natural constant, and Representing the Hedi The geological disaster monitoring data mentioned above is The influencing factors of the geological disaster types, 、 and are the first training weight, the first training bias, and the second training weight respectively.

[0045] Preferably, the first training weight, the first training bias and the second training weight are determined by minimizing the Loss function of the geological disaster type Sure:

[0046] ;

[0047] ;

[0048] ;

[0049] ;

[0050] in, is the judgment threshold, To find the function of the parameter that minimizes the function value, is the total number of training data sets, is the intermediate parameter, Representative Calculated from the training data The value of Representative Calculated from the training data The value of is the truth judgment function, Representative The first set of training data The true value corresponding to the prediction result of the geological disaster type.

[0051] The present invention also provides a scenic area geological disaster monitoring system based on big data, which is applied to the above-mentioned scenic area geological disaster monitoring method based on big data, including:

[0052] Initialization module, used to set various geological disaster monitoring data and various geological disaster types to be predicted;

[0053] A mapping establishment module is used to establish a mapping relationship table corresponding to a plurality of geological disaster monitoring data to a geological disaster type based on the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of the geological disaster type obtained based on the big data of multiple regions;

[0054] The prediction module is used to predict each type of geological disaster to be predicted through the disaster prediction model.

[0055] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0056] The present invention can collect geological disasters and related monitoring data from a large number of regions, and establish a mapping relationship between the geological disasters to be predicted and the corresponding monitoring data in combination with the regional characteristics of the target scenic area. While ensuring the amount of data, it also ensures that the data characteristics are consistent with the characteristics of the scenic area itself, which helps to improve the accuracy of geological disaster monitoring for specific scenic areas.

[0057] When establishing a mapping relationship table, the present invention establishes a data trend relationship through a large amount of regional data, and establishes a local data influence relationship through the area characteristics of the scenic area. The combination of the two can correct the relationship establishment of a large amount of data while being simple in calculation and easy to implement, and has a high cost-effectiveness.

[0058] When predicting geological disasters through the model, the present invention assigns different weights to different types of monitoring data based on the characteristics of the scenic area itself and for different types of geological disasters, thereby improving the pertinence of geological disaster monitoring and thus improving monitoring reliability.

[0059] When training the model, the output model established and the loss function adopted by the present invention can further optimize the specific coefficients of the weights, which helps to further improve the accuracy of the prediction;

[0060] The present invention is reasonably designed, easy to implement, highly targeted to local areas, and easy to promote and implement in various scenic spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1A flow chart of the method for monitoring geological disasters in scenic areas based on big data provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only 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 herein can be arranged and designed in various different configurations.

[0063] Example 1

[0064] This embodiment provides a method for monitoring geological disasters in scenic areas based on big data. Figure 1 , including the following steps:

[0065] Set up a variety of geological disaster monitoring data and a variety of geological disaster types to be predicted;

[0066] According to the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of geological disaster types obtained based on the big data of multiple regions, the influencing factors are obtained, and a mapping relationship table corresponding to multiple geological disaster monitoring data and one geological disaster type is established;

[0067] The disaster prediction model is used to predict each type of geological disaster to be predicted.

[0068] Based on the technical solution of this embodiment, it is possible to collect geological disaster and related monitoring data from multiple regions and, combined with the specific regional characteristics of the target scenic area, establish a mapping relationship between the type of geological disaster to be predicted and the corresponding geological disaster monitoring data. This process not only ensures a sufficient amount of sample data for confirming the relationship, but also ensures that the data characteristics are highly consistent with the characteristics of the scenic area itself, thereby significantly improving the accuracy of geological disaster monitoring for specific scenic areas, helping to more effectively identify potential geological disaster risks in the subsequent identification process, and thus providing timely warnings and decision-making support.

[0069] In this embodiment, the method for establishing a mapping relationship between multiple geological disaster monitoring data and one geological disaster type includes the following steps:

[0070] Obtaining the influence factors of each type of geological disaster monitoring data on the geological disaster type respectively, wherein the influence factors include an occurrence trend influence factor and an action range influence factor, and a larger value of the occurrence trend influence factor represents a greater degree of influence on the occurrence trend, and a larger value of the action range influence factor represents a greater degree of influence on the action range;

[0071] The mapping relationship is determined based on the influencing factors of each type of geological disaster monitoring data and the geological disaster type.

[0072] As a preferred solution of this embodiment, the impact factor is the product of the occurrence trend impact factor and the scope impact factor:

[0073] ;

[0074] in, Representative The geological disaster monitoring data mentioned above is The influencing factors of the above-mentioned geological disaster types, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of action of the above-mentioned geological disaster types.

[0075] Specifically, the method for obtaining the occurrence trend influencing factor is:

[0076] ;

[0077] ;

[0078] in, For the first The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is From multiple regions The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is is the total number of types of geological disaster monitoring data, is the natural logarithm function, To find the function of the Pearson correlation coefficient of two parameters, For the first The time series data sequence of the geological disaster monitoring data, For the first The time series data of the geological disaster types mentioned above, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of the geological disaster type;

[0079] On the other hand, the method for obtaining the scope influence factor is:

[0080] ;

[0081] in, is a natural constant, is the total area of ​​the scenic area, For the The area within the scenic area where the geological disaster monitoring data mentioned above is effective.

[0082] On this basis, the method for determining the mapping relationship table based on the influencing factors of the geological disaster type according to each geological disaster monitoring data is:

[0083] Preset impact factor threshold;

[0084] Jordi The geological disaster monitoring data mentioned above is If the impact factor of the geological disaster type is greater than the impact factor threshold, the The geological disaster monitoring data is put into the A mapping relationship table of the geological disaster types.

[0085] When establishing a mapping relationship table, this embodiment can utilize data from a large number of regions to identify and establish data trend relationships. It also analyzes the area characteristics of a scenic area to construct a relationship between local data and the degree of disaster impact, assigning higher correction factors to areas with larger impact areas. For example, parameters such as rainfall and water level can be set. The rainfall impact area can be set to the entire area, while the water level is targeted at areas such as lakes and streams. By combining the two types of data, computational simplicity is maintained while effectively correcting the relationships between large amounts of data, achieving cost-effective monitoring results. This method not only improves data processing efficiency but also ensures the reliability of the results, allowing the monitoring system to provide accurate geological hazard assessments even with limited resources. As a specific example, when providing flood warnings, the water level is generally an important indicator. However, if the area of ​​a lake in a scenic area is very small, the range impact factor is very small and can be filtered out in the impact factor calculation, making it more targeted to that scenic area. In contrast, in another scenic area dominated by lakes, the range impact factor will be very high. It is worth noting that when obtaining the occurrence trend impact factor, the first data from multiple regions is used. The time series data sequence of the geological disaster type can be a true value, that is, 1 if the corresponding geological disaster occurs and 0 if it does not occur, or it can be a specific parameter value, for example, for an earthquake, the earthquake level is recorded, and 0 if it does not occur.

[0086] In the following steps, the disaster prediction model includes an input layer, a data cleaning layer, a semantic matching layer, a feature extraction layer, and a recognition layer;

[0087] The input layer is used to receive the geological disaster type to be predicted, multiple geological disaster monitoring data for N consecutive days and the pre-stored mapping relationship table;

[0088] The function of the data cleaning layer is: if any geological disaster occurs in N consecutive days, the multiple geological disaster monitoring data of the corresponding date will be cleared and the cleared data will be supplemented by the Kalman filter;

[0089] The function of the semantic matching layer is:

[0090] The name of each input geological disaster monitoring data is converted into a word vector by using the Word2Vec function;

[0091] Obtaining the word vector of the geological disaster monitoring data required for prediction according to the type of geological disaster to be predicted through the mapping relationship table;

[0092] Performing semantic matching through word vectors to select the geological disaster monitoring data required for prediction from the input geological disaster monitoring data;

[0093] The feature extraction layer is used to extract features based on the geological disaster monitoring data required for the prediction to obtain multiple feature values;

[0094] The recognition layer is used to perform nonlinearization on the characteristic value and output the prediction result of the geological disaster based on the activation function.

[0095] The semantic matching layer set in this embodiment can utilize the semantic features of word vectors, which can better understand and match the relationship between geological disaster monitoring data and the type of geological disaster to be predicted, and thus has strong adaptability and versatility. When faced with measurement data from different monitoring equipment or personnel, the required data can be accurately matched even if the names have certain deviations.

[0096] Furthermore, the method for extracting features from the geological disaster monitoring data required for the prediction is as follows:

[0097] ;

[0098] ;

[0099] ;

[0100] in, for and The eigenvalue array formed, and Prediction The first of the geological disaster monitoring data required for the geological disaster type The first characteristic value and the second characteristic value of the geological disaster monitoring data are To find the standard deviation function, For N consecutive days The first of the geological disaster monitoring data required for the geological disaster type An array of geological disaster monitoring data, and They are Middle and elements.

[0101] When performing output judgment, the activation function is:

[0102] ;

[0103] ;

[0104] in, For the The output of the activation function of the identification layer of the geological disaster type, Represents the input parameters of the activation function after nonlinearization, Represents the geological disaster monitoring data required for prediction The geological disaster monitoring data The nonlinear output of the eigenvalue, , The total amount of geological disaster monitoring data required for prediction, is a natural constant, and Representing the Hedi The geological disaster monitoring data mentioned above is The influencing factors of the geological disaster types, 、 and are the first training weight, the first training bias, and the second training weight respectively.

[0105] The method for determining the first training weight, the first training bias and the second training weight is preferably to minimize the first Loss function of the geological disaster type Sure:

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] in, is the judgment threshold, To find the function of the parameter that minimizes the function value, is the total number of training data sets, is the intermediate parameter, Representative Calculated from the training data The value of Representative Calculated from the training data The value of is the truth judgment function, Representative The first set of training data The true value corresponding to the prediction result of the geological disaster type is specifically expressed as a true value, that is, 1 is recorded if it occurs and 0 is recorded if it does not occur.

[0111] In the process of building the geological disaster prediction model, this embodiment assigns different weights to different types of geological disaster monitoring data based on the previously calculated influencing factors. This strategy makes monitoring more targeted, thereby enhancing the reliability of geological disaster monitoring. By reasonably allocating weights, the model can more accurately reflect the actual risks of various types of disasters, thereby improving the response capability of the overall monitoring system. In the process of model training, an appropriate loss function is used to further optimize the specific coefficients of the weights. The loss function involves Partial optimization excludes the influence of weight changes and involves The part comprehensively considers the impact of weight changes. The comprehensive establishment of loss function helps to improve the accuracy of prediction and ensure that the model can adapt to the changing environment and data characteristics.

[0112] Example 2

[0113] This embodiment provides a scenic area geological disaster monitoring system based on big data, which is applied to a scenic area geological disaster monitoring method based on big data in the above embodiment, including:

[0114] Initialization module, used to set various geological disaster monitoring data and various geological disaster types to be predicted;

[0115] A mapping establishment module is used to establish a mapping relationship table corresponding to a plurality of geological disaster monitoring data to a geological disaster type based on the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of the geological disaster type obtained based on the big data of multiple regions;

[0116] The prediction module is used to predict each type of geological disaster to be predicted through the disaster prediction model.

[0117] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for monitoring geological disasters in scenic areas based on big data, characterized in that: The following steps are involved: Set up a variety of geological disaster monitoring data and a variety of geological disaster types to be predicted; According to the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of geological disaster types obtained based on the big data of multiple regions, the influencing factors are obtained, and a mapping relationship table corresponding to multiple geological disaster monitoring data and one geological disaster type is established; Use the disaster prediction model to predict each type of geological disaster to be predicted; The method for establishing a mapping relationship between multiple geological disaster monitoring data and one geological disaster type includes the following steps: Obtaining the influence factors of each type of geological disaster monitoring data on the geological disaster type respectively, wherein the influence factors include an occurrence trend influence factor and an action range influence factor, and a larger value of the occurrence trend influence factor represents a greater degree of influence on the occurrence trend, and a larger value of the action range influence factor represents a greater degree of influence on the action range; Determine the mapping relationship between the influencing factors of the geological disaster type according to each of the geological disaster monitoring data; The method for obtaining the occurrence trend influencing factor is: ; ; in, For the first The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is From multiple regions The geological disaster monitoring data and The correlation coefficient of the occurrence of the above-mentioned geological disaster types is is the total number of types of geological disaster monitoring data, is the natural logarithm function, To find the function of the Pearson correlation coefficient of two parameters, For the first The time series data sequence of the geological disaster monitoring data, For the first The time series data of the geological disaster types mentioned above, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of the geological disaster type; The method for obtaining the scope influence factor is: ； in, is a natural constant, is the total area of ​​the scenic area, For the The area within the scenic area where the geological disaster monitoring data mentioned above is effective.

2. A method for monitoring geological disasters in scenic areas based on big data according to claim 1, characterized in that: The impact factor is the product of the occurrence trend impact factor and the scope impact factor: ; in, Representative The geological disaster monitoring data mentioned above is The influencing factors of the above-mentioned geological disaster types, Representative The geological disaster monitoring data mentioned above is The occurrence trend influencing factors of the geological disaster types, Representative The geological disaster monitoring data mentioned above is The impact factors of the scope of action of the above-mentioned geological disaster types.

3. A method for monitoring geological disasters in scenic areas based on big data according to claim 2, characterized in that: The method for determining the mapping relationship table based on the influencing factors of the geological disaster type according to each type of geological disaster monitoring data is: Preset impact factor threshold; Jordi The geological disaster monitoring data mentioned above is If the impact factor of the geological disaster type is greater than the impact factor threshold, the The geological disaster monitoring data is put into the A mapping relationship table of the geological disaster types.

4. The method for monitoring geological disasters in scenic areas based on big data according to claim 1, characterized in that: The disaster prediction model includes an input layer, a data cleaning layer, a semantic matching layer, a feature extraction layer and a recognition layer; The input layer is used to receive the geological disaster type to be predicted, multiple geological disaster monitoring data for N consecutive days and the pre-stored mapping relationship table; The function of the data cleaning layer is: if any geological disaster occurs in N consecutive days, the multiple geological disaster monitoring data of the corresponding date will be cleared and the cleared data will be supplemented by the Kalman filter; The function of the semantic matching layer is: The name of each input geological disaster monitoring data is converted into a word vector by using the Word2Vec function; Obtaining the word vector of the geological disaster monitoring data required for prediction according to the type of geological disaster to be predicted through the mapping relationship table; Performing semantic matching through word vectors to select the geological disaster monitoring data required for prediction from the input geological disaster monitoring data; The feature extraction layer is used to extract features based on the geological disaster monitoring data required for the prediction to obtain multiple feature values; The recognition layer is used to perform nonlinearization on the characteristic value and output the prediction result of the geological disaster based on the activation function.

5. The method for monitoring geological disasters in scenic areas based on big data according to claim 4, characterized in that: The method for extracting features from the geological disaster monitoring data required for the prediction is: ; ； ； in, for and The eigenvalue array formed, and Prediction The first of the geological disaster monitoring data required for the geological disaster type The first characteristic value and the second characteristic value of the geological disaster monitoring data are To find the standard deviation function, For N consecutive days The first of the geological disaster monitoring data required for the geological disaster type An array of geological disaster monitoring data, and They are Middle and elements.

6. A method for monitoring geological disasters in scenic areas based on big data according to claim 5, characterized in that: The activation function is: ； ; in, For the The output of the activation function of the identification layer of the geological disaster type, Represents the input parameters of the activation function after nonlinearization, Represents the geological disaster monitoring data required for prediction The geological disaster monitoring data The nonlinear output of the eigenvalue, , The total amount of geological disaster monitoring data required for prediction, is a natural constant, and Representing the Hedi The geological disaster monitoring data mentioned above is The influencing factors of the geological disaster types, 、 and are the first training weight, the first training bias, and the second training weight respectively.

7. A method for monitoring geological disasters in scenic areas based on big data according to claim 6, characterized in that: The first training weight, the first training bias and the second training weight are determined by minimizing the Loss function of the geological disaster type Sure: ; ； ; ; in, is the judgment threshold, To find the function of the parameter that minimizes the function value, is the total number of training data sets, is the intermediate parameter, Representative Calculated from the training data The value of Representative Calculated from the training data The value of is the truth judgment function, Representative The first set of training data The true value corresponding to the prediction result of the geological disaster type.

8. A scenic area geological disaster monitoring system based on big data, applied to a scenic area geological disaster monitoring method based on big data according to any one of claims 1 to 7, characterized in that: include: Initialization module, used to set various geological disaster monitoring data and various geological disaster types to be predicted; A mapping establishment module is used to establish a mapping relationship table corresponding to a plurality of geological disaster monitoring data to a geological disaster type based on the scope of the geological disaster monitoring data obtained based on the big data of the scenic area and the degree of influence on the occurrence trend of the geological disaster type obtained based on the big data of multiple regions; The prediction module is used to predict each type of geological disaster to be predicted through the disaster prediction model.

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