An underground cavern excavation strategy optimization method and system
By acquiring geological data and establishing an excavation simulation model, and using particle swarm optimization to optimize the excavation strategy, the problem of underground cavern excavation being unable to cope with complex geological conditions was solved, achieving the effects of risk reduction and cost reduction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUODIAN DADU RIVER JINCHUAN HYDROPOWER CONSTR CO LTD
- Filing Date
- 2026-02-26
- Publication Date
- 2026-06-12
AI Technical Summary
Existing technologies for underground cavern excavation are ill-suited to complex and diverse geological conditions, resulting in high risks and costs associated with the excavation operation.
By acquiring geological condition data, determining geological state parameters, establishing an excavation simulation model, optimizing the excavation strategy using the particle swarm optimization algorithm, and outputting the optimal excavation strategy.
This approach optimizes the excavation process for underground caverns, reduces excavation risks, improves excavation quality, and lowers construction costs.
Smart Images
Figure CN122197132A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of underground cavern construction technology, and more specifically, to an optimization method and system for underground cavern excavation strategies. Background Technology
[0002] The excavation of underground caverns disrupts the original stress equilibrium, causing a redistribution of stress in the surrounding rock mass and resulting in stress concentration around the cavern. If the stress field at each point on the cavern wall does not exceed the critical value that would lead to rock mass failure, the surrounding rock is stable; otherwise, loosening deformation or even collapse will occur, gradually progressing radially into the rock mass from the cavern wall. This severely affects the stability of the engineering structure and construction safety.
[0003] The study of stress-strain variation during the excavation and evolution of underground caverns has always been a hot topic and a difficult point in the field of geology and geotechnical engineering. The current theoretical system for underground cavern construction is still immature, and empirical design and engineering analogy are still the main methods, which are difficult to cope with complex and diverse geological conditions, resulting in high risks and high construction costs in excavation operations. Summary of the Invention
[0004] This invention provides a method and system for optimizing underground cavern excavation strategies, to solve the problem that existing underground cavern excavation techniques struggle to cope with complex and diverse geological conditions, including:
[0005] Geological condition data of the area to be excavated is obtained, geological state parameters are determined based on the geological condition data, and excavation requirement parameter set is determined based on the geological state parameters; an excavation simulation model is established based on the excavation requirement parameter set to simulate the underground cavern excavation process, and the failure parameter set corresponding to multiple excavation strategies is output; a fitness function is constructed based on the failure parameter set, and the fitness function is optimized based on the particle swarm optimization algorithm to obtain the optimal excavation strategy.
[0006] Furthermore, determining the geological state parameters based on geological condition data includes: determining the rock strata quality data and in-situ stress data of the area to be excavated based on the geological condition data, and determining the geological state parameters based on the rock strata quality data and in-situ stress data. , , in, Geological state parameters, For rock strata quality data, To preset standard quality data, For the preset range coefficient, For the first Ground stress data at measuring points, To preset standard ground stress data, The total number of measuring points. It is a natural exponential function.
[0007] Further, determining the excavation requirement parameter set based on geological state parameters includes: acquiring preset standard state parameters, calculating the difference between the geological state parameters and the preset standard state parameters, and determining whether the difference between the geological state parameters and the preset standard state parameters is greater than a first preset threshold; if the difference between the geological state parameters and the preset standard state parameters is greater than the first preset threshold, then the first requirement parameter is set as the excavation requirement parameter set; if the difference between the geological state parameters and the preset standard state parameters is less than or equal to the first preset threshold, then determining whether the difference between the regional state parameters and the preset standard state parameters is greater than a second preset threshold; if the difference between the geological state parameters and the preset standard state parameters is greater than the second preset threshold, then the second requirement parameter is set as the excavation requirement parameter set; if the difference between the geological state parameters and the preset standard state parameters is less than or equal to the second preset threshold, then the third requirement parameter is set as the excavation requirement parameter set.
[0008] Furthermore, the step of establishing an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process and outputting multiple sets of hazard parameters corresponding to excavation strategies includes: acquiring geological condition data of the area to be excavated; establishing an excavation simulation model based on the geological condition data and the excavation requirement parameter set; inputting each excavation strategy into the excavation simulation model to simulate the excavation process of the underground cavern and obtaining simulation results; dividing the surrounding rock into zones based on the simulation results; and establishing a set of failure parameters based on each surrounding rock zone.
[0009] Furthermore, the step of dividing the surrounding rock into zones based on the simulation results includes: determining the stress state information of each plastic zone of the surrounding rock during the excavation process based on the simulation results; determining the degree of damage of each plastic zone of the surrounding rock based on the stress state information; and clustering each plastic zone of the surrounding rock based on the degree of damage to obtain the surrounding rock zoning of each plastic zone.
[0010] Furthermore, the step of clustering each plastic zone of the surrounding rock based on the degree of damage includes: establishing a sample dataset based on the degree of damage of each plastic zone of the surrounding rock, and randomly selecting k cluster centers from the sample dataset; calculating the Manhattan distance from each sample data in the sample dataset to the initial cluster center, and dividing each sample data into corresponding cluster partitions based on the Manhattan distance from each sample data in the sample dataset to the initial cluster center; calculating the mean of the sample data in each cluster partition, and recalculating the cluster center based on the mean of the sample data in each cluster partition; iteratively calculating the cluster center of each cluster partition until the cluster center no longer changes or reaches the preset maximum number of iterations, and obtaining the clustering result.
[0011] Further, the step of establishing a set of failure parameters based on each surrounding rock zone includes: statistically analyzing the failure degree of all surrounding rock plastic zones; determining a first failure parameter based on the average failure degree of all surrounding rock plastic zones; obtaining a preset allowable failure degree threshold; selecting surrounding rock zones with cluster center values greater than the preset allowable failure degree threshold; determining a second failure parameter based on the number of selected surrounding rock zones; setting a preset danger radius for each surrounding rock plastic zone based on the cluster center value corresponding to each surrounding rock zone; statistically analyzing the remaining surrounding rock plastic zones within the preset danger radius of any target surrounding rock plastic zone and their corresponding failure degrees; calculating the failure degree and value of all surrounding rock plastic zones within the preset danger radius of the target surrounding rock plastic zone to obtain the failure concentration of the target surrounding rock plastic zone; statistically analyzing the average failure concentration of all surrounding rock plastic zones to obtain a third failure parameter; and establishing a set of failure parameters based on the first failure parameter, the second failure parameter, and the third failure parameter.
[0012] Furthermore, the step of constructing the fitness function based on the set of destruction parameters includes: constructing a fitness function based on the first destruction parameter, the second destruction parameter, and the third destruction parameter in the set of destruction parameters, the expression of which is,
[0013] in, The first damage parameter, The second damage parameter, The third destructive parameter, , , These are the preset first weight, preset second weight, and preset third weight, respectively.
[0014] Furthermore, the optimization of the fitness function based on the particle swarm optimization algorithm to obtain the optimal excavation strategy includes: Step 1, randomly initializing the velocity and position of each particle in the search space, calculating the fitness function value of each particle, and the position of the particle with the minimum fitness function value is the global optimal position; Step 2, correcting the particle flight direction using velocity update formulas and position update formulas; Step 3, evaluating the particle fitness function value, and updating the individual optimal position and the global optimal position of the particle; Step 4, determining whether the particle swarm optimization algorithm meets the convergence condition. If it does, outputting the global optimal position and ending the process, and determining the particle corresponding to the global optimal position as the optimal excavation strategy; otherwise, repeating steps 1 to 3.
[0015] To achieve the above objectives, the present invention also provides an underground cavern excavation strategy optimization system, comprising: The first module is used to acquire geological condition data of the area to be excavated, determine geological state parameters based on the geological condition data, and determine the excavation requirement parameter set based on the geological state parameters. The second module is used to build an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process and output the failure parameter set corresponding to multiple excavation strategies. The third module is used to construct a fitness function based on the failure parameter set, optimize the fitness function based on the particle swarm optimization algorithm, and obtain the optimal excavation strategy.
[0016] The beneficial effects of this invention are as follows: By applying the above technical solution, this invention simulates the excavation process of underground caverns, sets the optimal excavation strategy based on the simulation results, optimizes the excavation strategy of underground caverns, obtains stable pressure control parameters, can adapt to various complex working conditions, reduces the risk of underground cavern excavation operations, improves excavation quality, and reduces construction costs. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 The overall flowchart of an optimization method for underground cavern excavation strategy proposed in an embodiment of the present invention is shown; Figure 2 A schematic diagram of an underground cavern excavation strategy optimization system proposed in an embodiment of the present invention is shown. Detailed Implementation
[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0020] This application provides an optimization method for underground cavern excavation strategies, such as... Figure 1 As shown, it includes: S101, Obtain geological condition data of the area to be excavated, determine geological state parameters based on the geological condition data, and determine the excavation requirement parameter set based on the geological state parameters; In this embodiment, geological condition data is determined by detecting the rock strata quality data of the area to be excavated and the geostress data of multiple measuring points, thereby obtaining geological state parameters and determining the set of excavation requirement parameters.
[0021] In some embodiments of this application, determining geological state parameters based on geological condition data includes: determining rock stratum quality data and in-situ stress data of the area to be excavated based on geological condition data, and determining geological state parameters based on the rock stratum quality data and in-situ stress data. , , in, Geological state parameters, For rock strata quality data, To preset standard quality data, For the preset range coefficient, For the first Ground stress data at measuring points, To preset standard ground stress data, The total number of measuring points. It is a natural exponential function.
[0022] In this embodiment, preset standard quality data is used. and preset standard ground stress data The preset range coefficient is used as an empirical value. Setting it to 2, combined with rock stratum quality data and geostress data from each measuring point, the geological state parameters are calculated, which can accurately reflect the geological state of the area to be excavated.
[0023] In some embodiments of this application, determining the excavation requirement parameter set based on geological state parameters includes: obtaining preset standard state parameters, calculating the difference between the geological state parameters and the preset standard state parameters, and determining whether the difference between the geological state parameters and the preset standard state parameters is greater than a first preset threshold; if the difference between the geological state parameters and the preset standard state parameters is greater than the first preset threshold, then setting the first requirement parameter as the excavation requirement parameter set; if the difference between the geological state parameters and the preset standard state parameters is less than or equal to the first preset threshold, then determining whether the difference between the regional state parameters and the preset standard state parameters is greater than a second preset threshold; if the difference between the geological state parameters and the preset standard state parameters is greater than the second preset threshold, then setting the second requirement parameter as the excavation requirement parameter set; if the difference between the geological state parameters and the preset standard state parameters is less than or equal to the second preset threshold, then setting the third requirement parameter as the excavation requirement parameter set.
[0024] In this embodiment, preset standard state parameters are set in advance, and the excavation requirement parameters are determined by the difference between the geological state parameters and the preset standard state parameters. The first preset threshold is greater than the second preset threshold, and the first requirement parameter is less than the second requirement parameter and the third requirement parameter. The smaller the geological state parameter, the higher the corresponding requirement parameter. The requirement parameters are design parameters such as the number of sub-blocks in the excavation process and the excavation period.
[0025] S102, Establish an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process, and output the failure parameter set corresponding to multiple excavation strategies; In some embodiments of this application, the step of establishing an excavation simulation model based on an excavation requirement parameter set to simulate the underground cavern excavation process and outputting multiple sets of hazard parameters corresponding to excavation strategies includes: acquiring geological condition data of the area to be excavated; establishing an excavation simulation model based on the geological condition data and the excavation requirement parameter set; inputting each excavation strategy into the excavation simulation model to simulate the underground cavern excavation process and obtain simulation results; dividing the surrounding rock into zones based on the simulation results; and establishing a set of failure parameters based on each surrounding rock zone.
[0026] In this embodiment, an excavation simulation model is established using geological condition data and excavation requirement parameter sets. The system stores multiple excavation strategies designed by experts. The excavation simulation model is used to simulate each excavation strategy, thereby obtaining the surrounding rock zoning and establishing the failure parameter set corresponding to each excavation strategy.
[0027] In some embodiments of this application, the step of dividing the surrounding rock into zones based on simulation results includes: determining the stress state information of each plastic zone of the surrounding rock during the excavation process based on the simulation results; determining the degree of damage of each plastic zone of the surrounding rock based on the stress state information; and clustering each plastic zone of the surrounding rock based on the degree of damage to obtain the surrounding rock zoning of each plastic zone.
[0028] In this embodiment, the ratio of plastic shear strain to ultimate shear strain of the surrounding rock in the plastic zone is calculated using stress state information, thereby obtaining the degree of damage to the plastic zone of the surrounding rock.
[0029] In some embodiments of this application, the step of clustering each plastic zone of the surrounding rock according to the degree of damage includes: establishing a sample dataset based on the degree of damage of each plastic zone of the surrounding rock, and randomly selecting k cluster centers from the sample dataset; calculating the Manhattan distance from each sample data in the sample dataset to the initial cluster center, and dividing each sample data into corresponding cluster partitions based on the Manhattan distance from each sample data in the sample dataset to the initial cluster center; calculating the mean of the sample data in each cluster partition, and recalculating the cluster center based on the mean of the sample data in each cluster partition; iteratively calculating the cluster center of each cluster partition until the cluster center no longer changes or reaches the preset maximum number of iterations, and obtaining the clustering result.
[0030] In this embodiment, the k-means clustering algorithm is used to cluster each plastic zone of the surrounding rock according to the degree of damage, thereby dividing each plastic zone of the surrounding rock into the corresponding surrounding rock partition.
[0031] In some embodiments of this application, the step of establishing a set of failure parameters based on each surrounding rock zone includes: statistically analyzing the failure degree of all surrounding rock plastic zones; determining a first failure parameter based on the average failure degree of all surrounding rock plastic zones; obtaining a preset permissible failure degree threshold; filtering out surrounding rock zones with cluster center values greater than the preset permissible failure degree threshold; determining a second failure parameter based on the number of filtered surrounding rock zones; setting a preset danger radius for each surrounding rock plastic zone based on the cluster center value corresponding to each surrounding rock zone; statistically analyzing the remaining surrounding rock plastic zones within the preset danger radius of any target surrounding rock plastic zone and their corresponding failure degrees; calculating the failure degree and value of all surrounding rock plastic zones within the preset danger radius of the target surrounding rock plastic zone to obtain the failure concentration of the target surrounding rock plastic zone; statistically analyzing the average failure concentration of all surrounding rock plastic zones to obtain a third failure parameter; and establishing a set of failure parameters based on the first failure parameter, the second failure parameter, and the third failure parameter.
[0032] In this embodiment, the corresponding danger radius is determined according to the cluster center value of each surrounding rock partition through the cluster center-danger radius mapping table. This danger radius is set as the preset danger radius of each plastic zone of the surrounding rock in the surrounding rock partition. The failure concentration is calculated by the failure degree and value of all plastic zones of the surrounding rock within the preset danger radius of any target plastic zone. This value reflects the concentration of excavation risks around the target plastic zone. The third failure parameter is obtained based on the average failure concentration of all plastic zones of the surrounding rock. The first failure parameter, the second failure parameter and the third failure parameter are combined into a failure parameter set.
[0033] S103. Construct a fitness function based on the set of destruction parameters, and optimize the fitness function based on the particle swarm optimization algorithm to obtain the optimal excavation strategy.
[0034] In some embodiments of this application, constructing the fitness function based on the set of destruction parameters includes: constructing a fitness function based on a first destruction parameter, a second destruction parameter, and a third destruction parameter in the set of destruction parameters, the expression of which is:
[0035] in, The first damage parameter, The second damage parameter, The third destructive parameter, , , These are the preset first weight, preset second weight, and preset third weight, respectively.
[0036] In some embodiments of this application, the optimization of the fitness function based on the particle swarm optimization algorithm to obtain the optimal excavation strategy includes: Step 1: Randomly initialize the velocity and position of each particle in the search space, and calculate the fitness function value of each particle. The position of the particle with the lowest fitness function value is the global optimum position. Step 2: Correct the particle flight direction using velocity update formulas and position update formulas. Step 3: Evaluate the particle fitness function value, and update the individual optimum position and the global optimum position of each particle. Step 4: Determine whether the particle swarm optimization algorithm meets the convergence condition. If it does, output the global optimum position and terminate the algorithm, determining the particle corresponding to the global optimum position as the optimal excavation strategy. Otherwise, repeat steps 1 to 3.
[0037] In this embodiment, the particle swarm optimization algorithm is used to optimize each excavation strategy to obtain the optimal excavation strategy, which can effectively reduce the risk of underground cavern excavation, improve excavation quality, and reduce construction costs.
[0038] Based on the same technological concept, such as Figure 2 As shown, the present invention also provides an underground cavern excavation strategy optimization system, comprising: The first module is used to acquire geological condition data of the area to be excavated, determine geological state parameters based on the geological condition data, and determine the excavation requirement parameter set based on the geological state parameters. The second module is used to build an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process and output the failure parameter set corresponding to multiple excavation strategies. The third module is used to construct a fitness function based on the failure parameter set, optimize the fitness function based on the particle swarm optimization algorithm, and obtain the optimal excavation strategy.
[0039] By applying the above technical solutions, this invention acquires geological condition data of the area to be excavated, determines geological state parameters based on the geological condition data, and determines a set of excavation requirement parameters based on the geological state parameters. It then establishes an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process, outputting a set of failure parameter values corresponding to multiple excavation strategies. Finally, it constructs a fitness function based on the failure parameter set and optimizes the fitness function using a particle swarm optimization algorithm to obtain the optimal excavation strategy. This invention can adapt to various complex working conditions, reduce the risk of underground cavern excavation operations, improve excavation quality, and reduce construction costs.
[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for optimizing underground cavern excavation strategies, characterized in that, include: Obtain geological condition data of the area to be excavated, determine geological state parameters based on the geological condition data, and determine the excavation requirement parameter set based on the geological state parameters. An excavation simulation model is established based on the excavation requirement parameter set to simulate the underground cavern excavation process, and the failure parameter set corresponding to multiple excavation strategies is output. A fitness function is constructed based on the set of destruction parameters, and the fitness function is optimized based on the particle swarm optimization algorithm to obtain the optimal excavation strategy.
2. The method for optimizing underground cavern excavation strategies according to claim 1, characterized in that, The determination of geological state parameters based on geological condition data includes: Based on geological condition data, determine the rock strata quality data and in-situ stress data of the area to be excavated. Based on the rock strata quality data and in-situ stress data, determine the geological state parameters. , ， in, Geological state parameters, For rock strata quality data, To preset standard quality data, For the preset range coefficient, For the first Ground stress data at measuring points, To preset standard ground stress data, The total number of measuring points. It is a natural exponential function.
3. The method for optimizing underground cavern excavation strategies according to claim 1, characterized in that, The determination of the excavation requirement parameter set based on geological state parameters includes: Obtain preset standard state parameters, calculate the difference between geological state parameters and preset standard state parameters, and determine whether the difference between geological state parameters and preset standard state parameters is greater than a first preset threshold. If the difference between the geological state parameters and the preset standard state parameters is greater than the first preset threshold, then the first demand parameter is set as the excavation demand parameter set. If the difference between the geological state parameters and the preset standard state parameters is less than or equal to the first preset threshold, then determine whether the difference between the regional state parameters and the preset standard state parameters is greater than the second preset threshold. If the difference between the geological state parameters and the preset standard state parameters is greater than the second preset threshold, then the second requirement parameters will be set as the excavation requirement parameter set. If the difference between the geological state parameters and the preset standard state parameters is less than or equal to the second preset threshold, then the third requirement parameter is set as the excavation requirement parameter set.
4. The method for optimizing underground cavern excavation strategies according to claim 1, characterized in that, The excavation simulation model is established based on the excavation requirement parameter set to simulate the underground cavern excavation process, and outputs multiple sets of hazard parameters corresponding to excavation strategies, including: Obtain geological condition data of the area to be excavated, and establish an excavation simulation model based on the geological condition data and the set of excavation requirements parameters. Each excavation strategy is input into the excavation simulation model to simulate the excavation process of the underground cavern and obtain the simulation results. Based on the simulation results, the surrounding rock is divided into zones, and a set of failure parameters is established for each zone.
5. The method for optimizing underground cavern excavation strategies according to claim 4, characterized in that, The step of dividing the surrounding rock into zones based on simulation results includes: Based on the simulation results, the stress state information of each plastic zone of the surrounding rock during the excavation process is determined, and the degree of damage of each plastic zone of the surrounding rock is determined based on the stress state information. Clustering of the plastic zones of each surrounding rock based on the degree of damage yields the surrounding rock zoning for each plastic zone.
6. The method for optimizing underground cavern excavation strategies according to claim 5, characterized in that, The clustering of each surrounding rock plastic zone based on the degree of damage includes: A sample dataset is established based on the degree of damage to each plastic zone of the surrounding rock, and k cluster centers are randomly selected from the sample dataset. Calculate the Manhattan distance from each sample data in the sample dataset to the initial cluster center, and divide each sample data into the corresponding cluster partition based on the Manhattan distance from each sample data in the sample dataset to the initial cluster center; Calculate the mean of the sample data within each cluster partition, and recalculate the cluster centers based on the mean of the sample data within each cluster partition; The cluster centers of each cluster partition are calculated iteratively until the cluster centers no longer change or the preset maximum number of iterations is reached, and the clustering results are obtained.
7. The method for optimizing underground cavern excavation strategies according to claim 5, characterized in that, The establishment of a set of failure parameters based on each surrounding rock zone includes: The degree of failure in all plastic zones of the surrounding rock is statistically analyzed, and the first failure parameter is determined based on the average degree of failure in all plastic zones of the surrounding rock. Obtain a preset permissible damage threshold, filter out surrounding rock zones with cluster center values greater than the preset permissible damage threshold, and determine the second damage parameter based on the number of filtered surrounding rock zones; Based on the cluster center value corresponding to each surrounding rock zone, the preset danger radius of each surrounding rock plastic zone is set, and the remaining surrounding rock plastic zones and their corresponding damage degree within the preset danger radius of any target surrounding rock plastic zone are statistically analyzed. Calculate the degree of failure and value of all plastic zones within the preset danger radius of the target surrounding rock plastic zone to obtain the degree of failure concentration of the target surrounding rock plastic zone; The third failure parameter is obtained by statistically analyzing the average failure concentration of all surrounding rock plastic zones. A set of failure parameters is established based on the first failure parameter, the second failure parameter, and the third failure parameter.
8. The method for optimizing underground cavern excavation strategies according to claim 1, characterized in that, The construction of the fitness function based on the set of destruction parameters includes: A fitness function is constructed based on the first, second, and third failure parameters in the failure parameter set, and its expression is as follows: in, The first damage parameter, The second damage parameter, The third destructive parameter, , , These are the preset first weight, preset second weight, and preset third weight, respectively.
9. The method for optimizing underground cavern excavation strategies according to claim 1, characterized in that, The optimization of the fitness function based on the particle swarm optimization algorithm to obtain the optimal excavation strategy includes: Step 1: Randomly initialize the velocity and position of each particle in the search space, calculate the fitness function value of each particle, and the position of the particle with the minimum fitness function value is the global optimal position. Step 2: Correct the particle's flight direction using velocity update formulas and position update formulas; Step 3: Evaluate the fitness function value of the particle and update the individual optimal position and global optimal position of the particle; Step 4: Determine whether the particle swarm optimization algorithm meets the convergence condition. If it does, output the global optimal position and end the process. The particle corresponding to the global optimal position is determined as the optimal excavation strategy. Otherwise, repeat steps 1 to 3.
10. A system for optimizing underground cavern excavation strategies, characterized in that, include: The first module is used to acquire geological condition data of the area to be excavated, determine geological state parameters based on the geological condition data, and determine the excavation requirement parameter set based on the geological state parameters. The second module is used to establish an excavation simulation model based on the excavation requirement parameter set to simulate the underground cavern excavation process and output the failure parameter set corresponding to multiple excavation strategies. The third module is used to construct a fitness function based on the set of destruction parameters, and optimize the fitness function based on the particle swarm optimization algorithm to obtain the optimal excavation strategy.