Digital Twin-based Disaster Experiment Deduction Method and System for Tourist Attractions

Abstract

The present invention relates to the field of digital twin technology. Specifically, it relates to a method and system for disaster experiment deduction of tourist attractions based on digital twins, including: obtaining the geological basic data of tourist attractions, evaluating the geological basic data of tourist attractions based on the analytic hierarchy process, dividing the disaster risk zones of tourist attractions according to the risk level, selecting the zone with the highest risk level to construct the physical model of the tourist attraction and the three-dimensional geological digital model of the tourist attraction corresponding to the physical model zone, mapping and matching and data synchronization of the physical model of the tourist attraction and the three-dimensional geological digital model of the tourist attraction to form a digital twin system, and conducting experiment deduction on the disaster scenario through the digital twin system to complete the accurate prediction of the disaster evolution process of the tourist attraction. Through the construction of the digital twin system, the present invention realizes the real-time mapping and synchronization of the geological model and the three-dimensional digital model of the tourist attraction, significantly improving the accuracy and timeliness of disaster risk assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and more specifically, to a method and system for disaster experiment deduction of tourist scenic spots based on digital twins. Background Art

[0002] With the rapid development of the tourism industry, the prevention and control of geological disasters in scenic spots have become an important link in ensuring the safety of tourists and the sustainable development of scenic spots. Obtaining accurate geological basic data is a prerequisite for disaster risk assessment. At present, traditional geological disaster prevention and control technologies for tourist scenic spots mainly rely on regular on-site surveys and static data analysis. Not only is the data collection cycle long, making it difficult to timely reflect the dynamic changes of the geological environment, resulting in a lag in risk assessment behind the actual situation, but also its ability in disaster simulation and evolution prediction is limited, unable to provide a comprehensive disaster scenario deduction, which restricts the foresight and pertinence of prevention and control measures. In addition, due to the lack of an effective real-time monitoring and feedback mechanism, traditional geological disaster prevention and control technologies for tourist scenic spots are difficult to respond to sudden disasters in a timely manner, weakening the efficiency and effectiveness of disaster emergency management. Therefore, in order to overcome the obvious deficiencies of traditional geological disaster prevention and control technologies for tourist scenic spots in terms of data real-time, comprehensive analysis ability, and disaster prediction accuracy, we designed a method and system for disaster experiment deduction of tourist scenic spots based on digital twins. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for disaster experiment deduction of tourist scenic spots based on digital twins, which realizes the real-time mapping and synchronization of the geological model and the three-dimensional digital model of the tourist scenic spot through the construction of the digital twin system, significantly improving the accuracy and timeliness of disaster risk assessment.

[0004] The present invention is realized through the following technical solutions:

[0005] A method for disaster experiment deduction of tourist scenic spots based on digital twins, the steps of the method include:

[0006] Obtain the geological basic data of the tourist scenic spot, evaluate the geological basic data of the tourist scenic spot based on the analytic hierarchy process, and divide the disaster risk zones of the tourist scenic spot according to the risk level;

[0007] Select the zone with the highest risk level based on the geological basic data of the tourist scenic spot to construct a physical model of the tourist scenic spot;

[0008] Based on the geological basic data of the tourist scenic spot, construct a three-dimensional geological digital model of the tourist scenic spot corresponding to the physical model through GIS;

[0009] Perform mapping matching and data synchronization on the physical model of the tourist scenic spot and the three-dimensional geological digital model of the tourist scenic spot to form a digital twin system of the physical model of the tourist scenic spot - the three-dimensional geological digital model of the tourist scenic spot;

[0010] Collect real-time monitoring data through the sensor units deployed in the tourist scenic area, and conduct experimental deduction on the disaster scenarios through the digital twin system to complete the accurate prediction of the disaster evolution process in the tourist scenic area.

[0011] Optionally, the evaluation of the geological basic data of the tourist scenic area based on the analytic hierarchy process is specifically as follows:

[0012] Obtain the topographic and geomorphic data, stratigraphic lithology data, and geological structure data of the tourist scenic area, and integrate them into the geological basic data of the tourist scenic area;

[0013] Construct a judgment matrix based on the geological basic data of the tourist scenic area, calculate the weight vectors of each layer of indicators of the judgment matrix, and calculate the maximum eigenvalue of the judgment matrix based on the weight vectors;

[0014] Conduct a consistency test on the judgment matrix through the maximum eigenvalue, and solve the risk degree of geological disasters in the tourist scenic area.

[0015] Optionally, the construction of the physical model of the tourist scenic area by selecting the area with the highest risk degree based on the geological basic data of the tourist scenic area is specifically as follows:

[0016] Divide the tourist scenic area into the area with the highest risk degree, the area with medium risk degree, and the area with the lowest risk degree according to the descending order of the risk degree of geological disasters in the tourist scenic area;

[0017] Select the area with the highest risk degree, obtain the basic data of the area with the highest risk degree, and determine the similarity ratio of the physical model of the tourist scenic area according to the basic data of the area with the highest risk degree;

[0018] Convert the similarity ratio of the physical model of the tourist scenic area into mechanical parameters, and combine the mechanical parameters to complete the construction of the physical model of the tourist scenic area.

[0019] Optionally, the similarity ratio of the physical model of the tourist scenic area includes: geometric similarity ratio, specific weight similarity ratio, strength similarity ratio, and coupling similarity ratio;

[0020] Among them, for the geometric similarity ratio, its calculation formula is:

[0021]

[0022] Among them, is the geometric similarity ratio, is the linear dimension of the area with the highest risk degree, is the linear dimension of the physical model of the tourist scenic area;

[0023] For the specific weight similarity ratio, its calculation formula is:

[0024]

[0025] Among them, is the severe similarity ratio, is the material density of the area with the highest risk level, is the material density of the physical model of the tourist scenic area;

[0026] The calculation formula of the strength similarity ratio is:

[0027]

[0028] Among them, is the strength similarity ratio;

[0029] The calculation formula of the coupling similarity ratio is:

[0030]

[0031] Among them, is the coupling similarity ratio, , are the adjustment coefficients respectively, , , .

[0032] Optionally, based on the geological basic data of the tourist scenic area, a three-dimensional geological digital model of the tourist scenic area corresponding to the physical model is constructed through GIS, and specifically:

[0033] Obtain the basic data of the area with the highest risk level;

[0034] In the GIS system, a digital elevation model is constructed based on the basic data of the area with the highest risk level and is three-dimensionally discretized to form a grid model of the tourist scenic area;

[0035] Map the similarity ratio of the grid model of the tourist scenic area to the physical model of the tourist scenic area, perform three-dimensional rendering on the grid model of the tourist scenic area, and define the maximization of the comprehensive fitness as the optimization goal to complete the construction of the three-dimensional geological digital model of the tourist scenic area.

[0036] Optionally, the calculation formula for the maximization of the comprehensive fitness as the optimization goal is:

[0037]

[0038] Among them, is the objective function, is the comprehensive fitness function, with a range of (0, 1], is the measured value, is the predicted value, M is the total number of measurement points, is a positive number, is the change amplitude of the k-th parameter between the n-th iteration and the (n - 1)-th iteration, , is the k-th parameter, is the weight of the k-th parameter, and K is the total number of parameters.

[0039] Optionally, the mapping matching and data synchronization of the physical model of the tourist scenic area and the three-dimensional geological digital model of the tourist scenic area are as follows: mapping and data synchronization of the similarity ratio of the three-dimensional geological digital model of the tourist scenic area with the physical model of the tourist scenic area, and its calculation formula is:

[0040]

[0041]

[0042]

[0043] Wherein, is the parameter of the three-dimensional geological digital model of the tourist scenic area, C is the total similarity ratio scaling factor, is the parameter of the physical model of the tourist scenic area, are respectively the geometric scaling index, specific gravity scaling index, strength scaling index and coupling scaling index of parameter k, is the real-time measurement data of the three-dimensional geological digital model of the tourist scenic area, S is the data synchronization scaling factor, is the real-time measurement data of the physical model of the tourist scenic area.

[0044] The tourist scenic area disaster experiment deduction system based on digital twin includes:

[0045] Data acquisition unit, which acquires the geological basic data of the tourist scenic area, evaluates the geological basic data of the tourist scenic area based on the analytic hierarchy process, and divides the disaster risk area of the tourist scenic area according to the risk degree;

[0046] Physical model construction unit, which constructs the physical model of the tourist scenic area by selecting the area with the highest risk degree based on the geological basic data of the tourist scenic area;

[0047] Digital model construction unit, which constructs the three-dimensional geological digital model of the tourist scenic area corresponding to the physical model area based on the geological basic data of the tourist scenic area through GIS;

[0048] Data twin unit, which performs mapping matching and data synchronization on the physical model of the tourist scenic area and the three-dimensional geological digital model of the tourist scenic area to form a digital twin system of the physical model of the tourist scenic area - three-dimensional geological digital model of the tourist scenic area;

[0049] Deduction unit, which acquires real-time monitoring data collected by the sensor unit arranged in the tourist scenic area, and performs experimental deduction on the disaster scenario through the digital twin system to complete the accurate prediction of the disaster evolution process of the tourist scenic area.

[0050] The technical solution of the present invention has at least the following advantages and beneficial effects:

[0051] On the one hand, through the construction of the digital twin system, the present invention realizes the real-time mapping and synchronization of the geological model and the 3D digital model of the tourist scenic area, significantly improving the accuracy and timeliness of disaster risk assessment; on the other hand, through the analytic hierarchy process and the GIS system, it comprehensively evaluates and accurately divides the disaster hazard zones to ensure key monitoring of key areas. Integrating real-time sensor data supports dynamic updates and disaster scenario simulations, enhancing the prediction ability of the disaster evolution process. In addition, the digital twin system designed by the present invention can effectively reduce the frequency and cost of on-site surveys, improve data processing efficiency, promote the scientific formulation and implementation of disaster prevention and control measures, and comprehensively improve the safety management level and emergency response ability of the scenic area. Description of the Drawings

[0052] Figure 1 It is a logical schematic diagram of the method for experimental deduction of disasters in tourist scenic areas based on digital twins provided by the present invention. Detailed Embodiments

[0053] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, what is described is a part of the present invention, not all of it. Usually, the components of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0054] Referring to Figure 1 as shown, Figure 1 It is a logical schematic diagram of the method for experimental deduction of disasters in tourist scenic areas based on digital twins provided by the present invention.

[0055] In one embodiment, the present invention provides a method for experimental deduction of disasters in tourist scenic areas based on digital twins. The steps of the method include:

[0056] Obtain the geological basic data of the tourist scenic area, evaluate the geological basic data of the tourist scenic area based on the analytic hierarchy process, and divide the disaster hazard zones of the tourist scenic area according to the risk level;

[0057] Select the zone with the highest risk level based on the geological basic data of the tourist scenic area to construct a physical model of the tourist scenic area;

[0058] Based on the geological basic data of the tourist scenic area, construct a 3D geological digital model of the tourist scenic area corresponding to the physical model through GIS;

[0059] Map and match the physical model of the tourist scenic area with the 3D geological digital model of the tourist scenic area, and synchronize the data to form a digital twin system of the physical model of the tourist scenic area - the 3D geological digital model of the tourist scenic area;

[0060] Collect real-time monitoring data according to the sensor units arranged in the tourist scenic area, and conduct experimental deduction on the disaster scenario through the digital twin system to complete the accurate prediction of the disaster evolution process in the tourist scenic area.

[0061] In the specific implementation of the above embodiment, in order to effectively construct and implement the geological disaster prevention and control system of the tourist scenic area, this embodiment comprehensively collects through various means, including a comprehensive on-site investigation by a professional geological team, and detailed records of key data such as geological structures, rock and soil layers, and fault distributions; uses satellite remote sensing and UAV aerial photography technologies to obtain high-resolution topographic maps and geological feature maps to supplement and enrich on-site data; and collects historical geological disaster records, meteorological data, and hydrological data of the tourist scenic area to form the geological basic data of the tourist scenic area. After preprocessing, based on the analytic hierarchy process, calculate the comprehensive score of the risk level of the tourist scenic area, divide it into three levels of disaster risk zones: high, medium, and low, and mark them on the geological map to provide a basis for the selection of high-risk areas and the construction of physical models. After selecting the high-risk zones, simulate the stratum structure, fault plane, and hydrological conditions, construct a physical model, and test the stability and accuracy of the model. At the same time, use the geographic information system (GIS) to construct a 3D geological digital model, import the sorted geological data, combine the digital elevation model (DEM) and the digital surface model (DSM) to establish the 3D terrain and geological layer distribution, accurately model geological structures such as geological faults, optimize the model details and enhance the visualization effect. After completing the 3D geological digital model, construct a digital twin system, align the key parameters of the physical model and the digital model through mapping and matching, and establish a real-time data connection. Finally, based on the digital twin system, conduct real-time monitoring and disaster prediction, select sensors such as stress-strain sensors, piezometers, and seismographs to be arranged in high-risk areas, establish a data collection and transmission system, clean, filter, and fuse data from different sensors, and construct a comprehensive geological parameter database. Based on the processed data, set the disaster scenario parameters, simulate the disaster scenario through the digital twin system, identify risk points and issue early warning information to guide management and emergency response, and at the same time continuously optimize the system, adjust the model parameters through feedback to ensure that the system accurately reflects the geological dynamic changes. This embodiment realizes the high consistency and real-time interaction between the physical and digital models through digital twin technology, significantly enhances the scientificity and timeliness of disaster prediction, ensures the safety of tourists, and promotes the sustainable development of the tourism industry.

[0062] Furthermore, the evaluation of the geological basic data of the tourist scenic area based on the analytic hierarchy process is specifically as follows:

[0063] Obtain the topographic and geomorphic data, stratigraphic lithology data, and geological structure data of the tourist scenic area, and integrate them into the geological basic data of the tourist scenic area;

[0064] Construct a judgment matrix based on the geological basic data of the tourist scenic area, calculate the weight vectors of each layer of indicators of the judgment matrix, and calculate the maximum eigenvalue of the judgment matrix based on the weight vectors;

[0065] Conduct a consistency test on the judgment matrix through the maximum eigenvalue, and solve the geological disaster risk degree of the tourist scenic area.

[0066] In implementation, in this embodiment, an evaluation index hierarchy structure is first constructed. The risk-related elements (such as geological structure, lithology, terrain slope, hydrological characteristics, etc.) are subdivided into multi-level indicators to form the hierarchy structure of AHP. Construct a judgment matrix , and conduct pairwise comparisons for the indicators of each layer. Among them, represents the importance score of the i-th indicator relative to the j-th indicator, and calculate the weight vector . The weight vector is specifically the eigenvector of the judgment matrix and satisfies the characteristic equation: , where is the maximum eigenvalue of the judgment matrix. And conduct a consistency test to solve the consistency index and the consistency ratio , where n is the dimension of the judgment matrix, is the random consistency index. Calculate the relative weight of each indicator again : , where is the normalized weight vector and satisfies . Finally, solve the comprehensive score of the geological disaster risk degree of the tourist scenic area: , where is the standardized score of the j-th area on the i-th indicator. According to the risk degree threshold, the tourist scenic area is divided into three levels of disaster risk zones: high, medium, and low, and marked on the GIS.

[0067] In this embodiment, the physical model of the tourist scenic area is constructed by selecting the area with the highest risk degree based on the geological basic data of the tourist scenic area. Specifically:

[0068] Divide the tourist scenic area into the highest-risk area, medium-risk area, and lowest-risk area according to the geological disaster risk degree from high to low;

[0069] Select the area with the highest risk degree, and obtain the basic data of the area with the highest risk degree. Determine the similarity ratio of the physical model of the tourist scenic area according to the basic data of the area with the highest risk degree;

[0070] The similarity ratios of the physical model of a tourist scenic area include: geometric similarity ratio, specific weight similarity ratio, strength similarity ratio, and coupling similarity ratio;

[0071] Among them, for the geometric similarity ratio, its calculation formula is:

[0072]

[0073] Where, is the geometric similarity ratio, is the linear dimension of the area with the highest risk level, is the linear dimension of the physical model of the tourist scenic area;

[0074] For the specific weight similarity ratio, its calculation formula is:

[0075]

[0076] Where, is the specific weight similarity ratio, is the specific weight of the material in the area with the highest risk level, is the specific weight of the material of the physical model of the tourist scenic area;

[0077] For the strength similarity ratio, its calculation formula is:

[0078]

[0079] Where, is the strength similarity ratio;

[0080] For the coupling similarity ratio, its calculation formula is:

[0081]

[0082] Where, is the coupling similarity ratio, 、 are adjustment coefficients respectively, used to control the weights and non - linear penalties of each similarity ratio in the logarithmic term, used to determine the amplification or attenuation effect of each similarity ratio on the comprehensive result in the exponential term, , , ;

[0083] Convert the similarity ratios of the physical model of the tourist scenic area into mechanical parameters, and combine the mechanical parameters to complete the construction of the physical model of the tourist scenic area.

[0084] In specific implementation, after completing the risk assessment and zoning, the area with the highest risk is selected as the basis for constructing the physical model. Key indicators such as the core geology, terrain, and lithology of this high-risk area are extracted. Combining the experimental purpose and site conditions, the boundary dimensions, geometric shape, and sensor layout plan of the physical model are determined. Based on the coupling similarity ratio formula of this embodiment, the non-linear relationship between multiple similarity ratios is comprehensively considered to achieve fine adjustment of the physical model parameters of the tourist scenic area. In this embodiment, based on the coupling similarity ratio, the mechanical parameters of the prototype rock mass in the tourist scenic area are converted into the corresponding parameters of the physical model of the tourist scenic area:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] Among them, are respectively the compressive stress, cohesion, internal friction angle, elastic modulus, and Poisson's ratio mechanical parameters of the prototype rock mass in the tourist scenic area, are respectively the compressive stress, cohesion, internal friction angle, elastic modulus, and Poisson's ratio mechanical parameters corresponding to the physical model of the tourist scenic area.

[0091] In this embodiment, based on the geological basic data of the tourist scenic area, a three-dimensional geological digital model of the tourist scenic area corresponding to the physical model is constructed through GIS. Specifically:

[0092] Obtain the basic data of the area with the highest risk;

[0093] In the GIS system, based on the basic data of the area with the highest risk, a digital elevation model is constructed and discretized in three dimensions to form a grid model of the tourist scenic area;

[0094]

[0095]

[0096] Among them, is the elevation value of the i-th known sampling point, is the distance from the i-th sampling point to the interpolation point (x, y), and p is the distance weight index, is the grid index, corresponding to the three-dimensional coordinates ( ), is the grid where the geological attribute set is located, such as density, strength, permeability coefficient, etc.;

[0097] Map the similarity ratio of the scenic area grid model to the physical model of the scenic area, perform three-dimensional rendering on the scenic area grid model, and define the optimization goal as maximizing the comprehensive fitness to complete the construction of the three-dimensional geological digital model of the scenic area.

[0098] In implementation, the digital model construction generates a digital elevation model through an interpolation algorithm, determines the depth of each rock layer based on borehole data, and combines the digital elevation model data to draw a stratigraphic profile and perform vertical stratification in GIS. The grid is divided by a three-dimensional discretization method, and geological attributes (such as density, strength, permeability coefficient, etc.) are assigned to the corresponding grid. Finally, through the similarity ratio mapping of geometric and mechanical parameters, the consistency of the digital model and the physical model in spatial size and material properties is ensured, realizing digital twin or synchronous analysis.

[0099] Specifically, the optimization goal of maximizing the comprehensive fitness has the following calculation formula:

[0100]

[0101] where, is the objective function, is the comprehensive fitness function, with a range of (0, 1], is the measured value, is the predicted value, M is the total number of measurement points, is a positive number, is the change amplitude of the kth parameter between the nth iteration and the (n - 1)th iteration, , is the kth parameter, such as elastic modulus, cohesion, internal friction angle, permeability coefficient, etc., is the weight of the kth parameter, and K is the total number of parameters.

[0102] The objective function of this embodiment aims to optimize the digital model to achieve the best balance between the matching degree and stability. The comprehensive fitness function consists of the weighted sum of the matching degree and the stability degree. The matching degree measures the degree of coincidence between the model predicted value and the measured value. The smaller the error, the higher the matching degree; the stability degree evaluates the change amplitude of the model parameters during the iteration process. The smaller the change, the higher the stability degree. By maximizing the objective function, the model is prompted to accurately reflect the actual geological conditions while maintaining the stability of numerical calculations, thereby improving the accuracy and reliability of the model.

[0103] More specifically, the mapping matching and data synchronization of the physical model of the scenic area and the three-dimensional geological digital model of the scenic area are as follows: Map and synchronize the similarity ratio of the three-dimensional geological digital model of the scenic area to the physical model of the scenic area, and its calculation formula is:

[0104]

[0105]

[0106]

[0107] Among them, are the parameters of the 3D geological digital model of the tourist scenic area, C is the total similarity ratio scaling factor, are the parameters of the physical model of the tourist scenic area, are respectively the geometric scaling index, the specific weight scaling index, the strength scaling index and the coupling scaling index of the parameter k, are the real-time measurement data of the 3D geological digital model of the tourist scenic area, S is the data synchronization scaling factor, are the real-time measurement data of the physical model of the tourist scenic area.

[0108] In the specific application of this embodiment, this embodiment collects geological parameter data in real time by deploying sensors (such as stress-strain sensors and piezometers), constructs a 3D geological digital model based on the initial geological data, and sets key parameters. The physical model parameters are mapped to the digital model using the similarity ratio to ensure the correspondence and consistency of scale and material properties. The real-time monitoring data of the physical model is converted and synchronized to the digital model to maintain the data consistency of both, and the comprehensive fitness function is calculated. The geological parameters are adjusted through an optimization algorithm to maximize the matching degree and stability. After completing the parameter optimization, disaster simulations are performed in the digital model according to the set disaster scenarios (such as earthquakes, landslides, and debris flows), and the changes in parameters such as stress, strain, displacement, and water flow are calculated using finite element analysis or other numerical simulation tools. Finally, the simulation results are analyzed to identify the trends and affected areas of disaster development, predict key events and time nodes, and feedback the results to the model correction and continuous optimization link to ensure the accuracy of the prediction and the dynamic update of the model.

[0109] In another embodiment, the present invention provides a disaster experiment deduction system for tourist scenic areas based on digital twins, including:

[0110] A data acquisition unit, which acquires the geological basic data of the tourist scenic area, evaluates the geological basic data of the tourist scenic area based on the analytic hierarchy process, and divides the disaster risk areas of the tourist scenic area according to the risk level;

[0111] A physical model construction unit, which constructs a physical model of the tourist scenic area by selecting the area with the highest risk level based on the geological basic data of the tourist scenic area;

[0112] A digital model construction unit, which constructs a 3D geological digital model of the tourist scenic area corresponding to the physical model area based on the geological basic data of the tourist scenic area through GIS;

[0113] The data twin unit maps and matches the physical model of the tourist scenic area with the 3D geological digital model of the tourist scenic area and synchronizes the data to form a digital twin system of the physical model of the tourist scenic area - the 3D geological digital model of the tourist scenic area;

[0114] The deduction unit collects real-time monitoring data according to the sensor units arranged in the tourist scenic area, and conducts experimental deductions on the disaster scenarios through the digital twin system to accurately predict the disaster evolution process of the tourist scenic area.

[0115] The above is only the preference of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A tourist scenic area disaster experimental simulation method based on digital twins, characterized in that: The steps of the method include: Obtain basic geological data of tourist attractions, evaluate them based on the analytic hierarchy process, and divide the disaster risk zones of tourist attractions according to the degree of risk; Based on the basic geological data of the tourist attraction, the most dangerous sub-area is selected to construct the physical model of the tourist attraction; Based on the basic geological data of the tourist attraction, a three-dimensional geological digital model of the tourist attraction corresponding to the physical model is constructed through GIS; Mapping and matching the physical model of the tourist attraction with the three-dimensional geological digital model of the tourist attraction and data synchronization to form a digital twin system of the physical model of the tourist attraction and the three-dimensional geological digital model of the tourist attraction; Real-time monitoring data is collected by sensor units installed in tourist attractions, and disaster scenarios are experimentally simulated through the digital twin system to accurately predict the evolution process of disasters in tourist attractions.

2. The tourist attraction disaster experimental simulation method based on digital twin according to claim 1 is characterized in that: The evaluation of the geological basic data of the tourist attraction based on the hierarchical analysis method is specifically as follows: Acquire topographic data, stratigraphic lithology data and geological structure data of tourist attractions, and integrate them into the basic geological data of the tourist attractions; Construct a judgment matrix based on the geological basic data of the tourist attraction, calculate the weight vector of each layer of the judgment matrix, and calculate the maximum eigenvalue of the judgment matrix based on the weight vector; The consistency test of the judgment matrix is ​​carried out through the maximum eigenvalue root to solve the geological disaster risk degree of the tourist attraction.

3. The tourist scenic area disaster experimental simulation method based on digital twin according to claim 2 is characterized in that: The method of selecting the most dangerous zone based on the basic geological data of the tourist attraction to construct the physical model of the tourist attraction is as follows: According to the geological disaster risk level of tourist attractions, they are divided into the highest risk zone, the medium risk zone and the lowest risk zone from high to low; Select the zone with the highest risk, obtain basic data of the zone with the highest risk, and determine the similarity ratio of the physical model of the tourist attraction according to the basic data of the zone with the highest risk; The similarity ratio of the physical model of the tourist attraction is converted into mechanical parameters, and the mechanical parameters are combined to complete the construction of the physical model of the tourist attraction.

4. The tourist scenic area disaster experimental simulation method based on digital twin according to claim 3 is characterized in that: The similarity ratios of the physical model of the tourist attraction include: geometric similarity ratio, weight similarity ratio, intensity similarity ratio and coupling similarity ratio; Wherein, the geometric similarity ratio is calculated as follows: in, is the geometric similarity ratio, is the linear size of the most dangerous zone, is the linear size of the physical model of the tourist attraction; The calculation formula of the severe similarity ratio is: in, is the heavy similarity ratio, The material weight of the most dangerous zone is The material weight of the physical model of the tourist attraction; The intensity similarity ratio is calculated as follows: in, is the intensity similarity ratio; The coupling similarity ratio is calculated as follows: in, is the coupling similarity ratio, , are the adjustment coefficients, , , .

5. The tourist attraction disaster experimental simulation method based on digital twin according to claim 4 is characterized in that: The three-dimensional geological digital model of the tourist attraction corresponding to the physical model is constructed by GIS based on the geological basic data of the tourist attraction, which is specifically: Obtain basic data of the most dangerous zone; In the GIS system, a digital elevation model is constructed based on the basic data of the most dangerous zone, and a grid model of the tourist attraction is formed through three-dimensional discretization; The similarity ratio of the grid model of the tourist attraction is mapped with the physical model of the tourist attraction, the grid model of the tourist attraction is rendered in three dimensions, and the maximization of comprehensive fitness is defined as the optimization goal to complete the construction of the three-dimensional geological digital model of the tourist attraction.

6. The tourist attraction disaster experimental simulation method based on digital twin according to claim 5 is characterized in that: The optimization goal is to maximize the comprehensive fitness, and the calculation formula is: in, is the objective function, is the comprehensive fitness function, ranging from (0,1], is the measured value, is the predicted value, M is the total number of measurement points, is a positive number, is the change of the kth parameter between the nth iteration and the n−1th iteration, , is the kth parameter, is the weight of the kth parameter, and K is the total number of parameters.

7. The tourist attraction disaster experimental simulation method based on digital twin according to claim 6 is characterized in that: The mapping and matching of the physical model of the tourist attraction with the three-dimensional geological digital model of the tourist attraction and data synchronization are specifically: mapping and data synchronization of the similarity ratio of the three-dimensional geological digital model of the tourist attraction with the physical model of the tourist attraction, and the calculation formula is: in, is the parameter of the three-dimensional geological digital model of the tourist attraction, C is the total similarity ratio scaling factor, are the parameters of the physical model of the tourist attraction, are the geometric scaling exponent, weight scaling exponent, intensity scaling exponent and coupling scaling exponent of parameter k, is the real-time measurement data of the three-dimensional geological digital model of the tourist attraction, S is the data synchronization scaling factor, Real-time measurement data for the physical model of tourist attractions.

8. The tourist scenic area disaster experiment simulation system based on digital twin is characterized by: include: The data collection unit obtains the basic geological data of the tourist attractions, evaluates the basic geological data of the tourist attractions based on the analytic hierarchy process, and divides the disaster risk zones of the tourist attractions according to the risk level; The physical model building unit selects the most dangerous zone based on the basic geological data of the tourist attraction to build the physical model of the tourist attraction; The digital model building unit, based on the basic geological data of the tourist scenic area, constructs a three-dimensional geological digital model of the tourist scenic area corresponding to the physical model through GIS; The data twin unit maps and matches the physical model of the tourist attraction with the three-dimensional geological digital model of the tourist attraction and synchronizes the data to form a digital twin system of the physical model of the tourist attraction and the three-dimensional geological digital model of the tourist attraction; The simulation unit collects real-time monitoring data based on the sensor units deployed in the tourist scenic area, and conducts experimental simulation of disaster scenarios through the digital twin system to accurately predict the evolution process of disasters in the tourist scenic area.

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