A method for optimizing the design of compressed air energy storage chamber

Abstract

The present invention provides a method for optimizing the design of a compressed air energy storage chamber, and relates to the technical field of optimizing the design of energy storage chambers. The present invention establishes a multi-dimensional stress distribution model by collecting surrounding rock stress data, seismic wave propagation velocity, sound wave attenuation coefficient and other information, and accurately identifies stress concentration areas and their changing trends; through iterative simulation and optimization processes, comprehensively considering parameters such as elastic modulus, wave velocity ratio, and crack density, the basic shape and support structure of the chamber are optimized, and the risk of damage caused by stress concentration is significantly reduced; the method improves the safety and stability of the chamber, extends its service life, and improves its bearing capacity, thereby meeting the needs of large-scale energy storage applications.

Description

Technical Field

[0001] The invention relates to the technical field of energy storage chamber optimization design, and in particular to a compressed air energy storage chamber optimization design method. Background Art

[0002] Compressed Air Energy Storage (CAES) technology, as a large-scale energy storage method, has received widespread attention and research in recent years. Its basic principle is to use electricity during low electricity price periods to compress air and store it in underground chambers, and then release the air to generate electricity through expanders during peak electricity demand periods. Traditional CAES technology is mainly used in deep underground salt caverns, abandoned mines, or rock chambers under specific geological conditions. The advantages of these energy storage forms are large energy storage capacity, high efficiency, relatively low operating costs, and the ability to better regulate grid load;

[0003] In the prior art, the publication number is CN116842726A, and the name is a method for optimizing the design of a shallow hard rock compressed air energy storage chamber, which includes the following steps: preliminarily determining the maximum air pressure inside the chamber to be built according to the use requirements; determining the initial burial depth of the chamber; obtaining the ground stress around the chamber to determine the direction of the chamber; calculating the zero stress axis ratio and the optimal axis ratio of the chamber; verifying whether the air pressure inside the chamber is lower than the maximum value that the surrounding rock of the chamber can withstand at the initial burial depth; verifying whether the surrounding rock pressure of the chamber at the initial burial depth under the optimal axis ratio is greater than a preset threshold, thereby determining the burial depth of the chamber; determining the cross-sectional area of ​​the chamber, calculating the vertical semi-axis and the horizontal semi-axis lengths according to the zero stress axis ratio or the optimal axis ratio, determining the outer shape of the chamber, and thereby determining a shallow buried chamber.

[0004] The existing technology has obvious deficiencies in the optimization design of energy storage chambers. First, the traditional energy storage chamber design usually only considers the stress distribution under static conditions, ignoring the problems of rock mass damage and chamber stability caused by stress concentration. In addition, the design of the support structure often relies on empirical formulas and simple numerical simulations, lacking systematic and scientific optimization strategies. This leads to uneven stress distribution in the energy storage chamber in actual applications, and the stress concentration area is prone to damage, affecting the safety and stability of the energy storage system.

[0005] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the design of a compressed air energy storage chamber to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for optimizing the design of a compressed air energy storage chamber, the specific steps comprising:

[0009] Step S1: collecting stress distribution data at different positions on the surrounding rock of the energy storage chamber, performing simulation analysis through finite element analysis software, and constructing a stress distribution model based on the simulation results to identify stress concentration areas, and sort these stress concentration areas to form a stress distribution sequence set;

[0010] Step S2: collecting seismic wave propagation velocity and acoustic wave attenuation coefficient data of different stress concentration areas in the stress distribution sequence concentration to form a preliminary data set after preprocessing; and using the preliminary data set to calculate the elastic modulus and wave velocity ratio of different stress concentration areas in the stress distribution sequence concentration;

[0011] Step S3: collecting crack density and average displacement gradient data of each stress concentration area in the stress distribution sequence, and combining and analyzing the average stress of each stress concentration area with the corresponding crack density and average displacement gradient data, to generate a first evaluation index for evaluating the crack change trend corresponding to each stress concentration area, wherein the first evaluation index includes a low value range indicating rock mass stability;

[0012] Step S4: determining the basic shape of each stress concentration area, combining the elastic modulus, wave velocity ratio and first evaluation index of the current stress concentration area, and comprehensively generating an evaluation coefficient for providing an optimization strategy for the current stress concentration area;

[0013] The output value range of the evaluation coefficient is divided into several evaluation level intervals. The higher the interval level, the higher the structural rock strength in the current stress concentration area. The optimization strategy includes providing the best adjustment plan for the foundation shape and support structure according to the structural rock strength of each stress concentration area.

[0014] Step S5: Re-simulate the shape of the energy storage chamber and the stress distribution of the support structure optimized in step S4 through the finite element analysis software to determine whether the output value of the first evaluation index in the current stress concentration area reaches the low value range. If it does not reach the low value range, perform the following steps:

[0015] Re-perform the crack density and average displacement gradient data collection and analysis in step S3, and generate a revised first evaluation index;

[0016] According to the modified first evaluation index, re-execute step S4 to obtain an optimized evaluation coefficient;

[0017] According to the optimized evaluation coefficient, the optimal adjustment scheme is re-implemented until the output value of the first evaluation index of the current stress concentration area reaches the low value range.

[0018] Compared with the prior art, the beneficial effects of the present invention are: by collecting surrounding rock stress data, seismic wave propagation velocity, sound wave attenuation coefficient and other information, a multi-dimensional stress distribution model is established to accurately identify stress concentration areas and their changing trends; through iterative simulation and optimization processes, the elastic modulus, wave velocity ratio, crack density and other parameters are comprehensively considered to optimize the basic shape and support structure of the chamber, significantly reducing the risk of damage caused by stress concentration; this method improves the safety and stability of the chamber, extends its service life, and increases its bearing capacity to meet the needs of large-scale energy storage applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0020] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0021] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0022] Embodiment 1:

[0023] See also Figure 1 , the present invention provides a technical solution:

[0024] A method for optimizing the design of a compressed air energy storage chamber, the specific steps comprising:

[0025] Step S1: collecting stress distribution data at different positions on the surrounding rock of the energy storage chamber, performing simulation analysis through finite element analysis software, and constructing a stress distribution model based on the simulation results to identify stress concentration areas, and sort these stress concentration areas to form a stress distribution sequence set;

[0026] Step S2: collecting seismic wave propagation velocity and acoustic wave attenuation coefficient data of different stress concentration areas in the stress distribution sequence concentration to form a preliminary data set after preprocessing; and using the preliminary data set to calculate the elastic modulus and wave velocity ratio of different stress concentration areas in the stress distribution sequence concentration;

[0027] Step S3: collecting crack density and average displacement gradient data of each stress concentration area in the stress distribution sequence, and combining and analyzing the average stress of each stress concentration area with the corresponding crack density and average displacement gradient data, to generate a first evaluation index for evaluating the crack change trend corresponding to each stress concentration area, wherein the first evaluation index includes a low value range indicating rock mass stability;

[0028] Step S4: determining the basic shape of each stress concentration area, combining the elastic modulus, wave velocity ratio and first evaluation index of the current stress concentration area, and comprehensively generating an evaluation coefficient for providing an optimization strategy for the current stress concentration area;

[0029] The output value range of the evaluation coefficient is divided into several evaluation level intervals. The higher the interval level, the higher the structural rock strength in the current stress concentration area. The optimization strategy includes providing the best adjustment plan for the foundation shape and support structure according to the structural rock strength of each stress concentration area.

[0030] Step S5: Re-simulate the shape of the energy storage chamber and the stress distribution of the support structure optimized in step S4 through the finite element analysis software to determine whether the output value of the first evaluation index in the current stress concentration area reaches the low value range. If it does not reach the low value range, perform the following steps:

[0031] Re-perform the crack density and average displacement gradient data collection and analysis in step S3, and generate a revised first evaluation index;

[0032] According to the modified first evaluation index, re-execute step S4 to obtain an optimized evaluation coefficient;

[0033] According to the optimized evaluation coefficient, the optimal adjustment scheme is re-implemented until the output value of the first evaluation index of the current stress concentration area reaches the low value range.

[0034] Further explanation: constructing a stress distribution model and forming a stress distribution sequence set specifically include:

[0035] Stress sensors are arranged at key locations such as the roof, side walls and bottom plate of the energy storage chamber; the interval between sensors is determined according to the scale of the energy storage chamber. In this embodiment, a sensor is arranged every 1 meter to ensure the density and accuracy of data collection;

[0036] Continuously collect stress data at different positions of the energy storage chamber; the data collection time at each position is not less than 10 minutes to obtain stable data;

[0037] Import the collected stress data into the data analysis software for data preprocessing; data preprocessing includes noise filtering and outlier removal; the data preprocessing method of this embodiment includes Fourier transform, low-pass filtering and outlier detection algorithm;

[0038] The data analysis software is MATLAB or Python;

[0039] Input the shape of the energy storage chamber and the corresponding parameters of the support structure into the finite element analysis software for simulation; the finite element analysis software is ANSYS or ABAQUS; the shape includes basic dimensions such as length, width, and height, as well as physical property parameters of the surrounding rock, such as density, elastic modulus, Poisson's ratio, etc.; and includes the prestressed anchor rods, steel mesh and shotcrete contained in the support structure;

[0040] Boundary conditions and load settings: Set the boundary conditions and stress loads of the energy storage chamber to simulate the stress distribution under actual working conditions. Boundary conditions include fixed constraints, sliding constraints, etc., and stress loads include deadweight, ground stress, and external loads.

[0041] Meshing: Perform average meshing on the simulation model of the energy storage chamber to ensure that the geometry and material properties of the model are accurately expressed. The degree of detail of the meshing will affect the calculation accuracy and time, so adaptive meshing technology is used.

[0042] Finite element calculation: Run the finite element analysis software to perform stress distribution calculations and record the stress and strain distribution of each node and unit in the model.

[0043] Import the preprocessed stress data into the finite element model to perform three-dimensional stress distribution calculation to obtain analytical calculation results; specifically use the stress analysis module of the software to calculate the stress value of each node inside the energy storage chamber and visualize the results;

[0044] Analyze the calculation results, focus on the stress concentration area of ​​the optimized energy storage chamber, and evaluate whether its stress distribution meets the goals of the optimization strategy. The analysis includes:

[0045] The maximum and minimum stress values ​​of each area; and setting the limit value of the maximum stress value; the limit value is determined by the expert group based on experimental data;

[0046] Stress dispersion effect in stress concentration areas;

[0047] The synergistic working of rock formation strength and support structure.

[0048] By analyzing the calculation results, the grid areas with maximum stress values ​​greater than the limit value are identified as stress concentration areas in the energy storage chamber. These stress concentration areas are sorted from high to low according to the stress values ​​and numbered to form a stress distribution sequence set {1,2,…,i,…,n}, where n represents the total number of stress concentration areas and i represents the index of the i-th stress concentration area.

[0049] Further explanation: calculating the elastic modulus and wave velocity ratio of different stress concentration areas in the stress distribution sequence concentration includes:

[0050] The elastic modulus and wave speed ratio calculation formula are defined as follows:

[0051] ;

[0052] in, is the elastic modulus of the ith stress concentration area, is the rock density of the ith stress concentration area, is the seismic wave propagation velocity of the ith stress concentration area, is the shear wave velocity of the ith stress concentration region, is the Poisson's ratio of the i-th stress concentration area; based on experimental demonstration or expert group analysis, The value range is between (0.2, 0.35); in this embodiment, The value is 0.25; is the wave velocity ratio of the i-th stress concentration area;

[0053] The larger the elastic modulus, the harder it is for the surrounding rock mass to deform, and the more stable the corresponding energy storage chamber is in this area;

[0054] Changes in wave velocity ratio can reflect changes in rock type and pore structure;

[0055] Assess rock mass strength and stability: areas with higher wave velocity ratios indicate denser rocks with higher strength, more uniform pore structures, and less pore content;

[0056] Crack density is the number of cracks per unit volume or unit area, reflecting the degree of rupture in the stress concentration area. The number of cracks is measured and the crack density is calculated by ultrasonic detection of crack detection technology.

[0057] The displacement gradient is the rate of change of displacement in space, which reflects the deformation characteristics of the rock mass under stress. The displacement gradient is calculated by measuring the displacement values ​​at different positions.

[0058] Further explanation: generating a first evaluation index for evaluating the crack change trend corresponding to each stress concentration area specifically includes:

[0059] The first evaluation index of the i-th stress concentration area is marked as , the calculation formula is as follows:

[0060] ;

[0061] Among them, the first evaluation index The value range is (0,1); is the constant weight factor of the i-th stress concentration area, which is used to adjust the sensitivity of the formula and is usually obtained through experimental calibration;

[0062] is the average stress value of the i-th stress concentration area; is the crack density value of the i-th stress concentration area; is the average displacement gradient value of the i-th stress concentration area; e is the base of the natural logarithm;

[0063] , and All are output values ​​that are standardized to the range of (0,1);

[0064] The stress values ​​at different positions in the i-th stress concentration area are measured by a stress sensor, and the average value is calculated and then standardized to the range of (0, 1);

[0065] It uses ultrasonic detection technology to measure the number of cracks per unit volume or unit area, calculates the crack density and then standardizes it to the range of (0,1);

[0066] It is achieved by arranging displacement sensors in the stress concentration area, measuring the displacement values ​​at different positions, calculating the gradient and then normalizing it to the range of (0,1);

[0067] The first evaluation index The value range (0,1) is divided into the low value range (0,0.4), the middle value range [0.4,0.6] and the high value range (0.6,1);

[0068] For the low value range (0, 0.4), it means that the crack change trend in the stress concentration area is low level and the rock mass is stable; the low level means that the probability of crack change is within 10%;

[0069] For the median range [0.4, 0.6], it means that the crack change trend in the stress concentration area is medium, and it is necessary to increase the current monitoring frequency by 20% and shorten the current maintenance cycle by 30%. The medium level means that the probability of crack change is between 10% and 35%.

[0070] The current monitoring frequency is once every 2 hours, which will be adjusted to once every 1.67 hours;

[0071] The current maintenance cycle is 30 days, which will be adjusted to 21 days;

[0072] For the high value range (0.6, 1), it means that the crack change trend in the stress concentration area is high-level, and there is a probability of more than 35% that the crack will rupture or deform, and emergency measures need to be taken for reinforcement or adjustment.

[0073] Emergency measures, including: additional support structures, grouting reinforcement, dynamic monitoring systems and decompression measures:

[0074] For adding support structures: add temporary or permanent support structures in stress concentration areas to reduce stress concentration in the rock mass;

[0075] Specific method: Anchor support: Anchors are arranged in stress concentration areas to increase rock stability. The length of the anchors should be more than 1.5 times the depth of the cracks, and the spacing should not exceed 1 meter.

[0076] Shotcrete: A layer of concrete with a thickness of 5-10 cm is sprayed on the surface of the stress concentration area to form a protective layer.

[0077] Steel mesh support: Before spraying concrete, arrange the steel mesh with a grid spacing not exceeding 20 cm.

[0078] For grouting reinforcement: through grouting technology, slurry is injected into the cracks to increase the integrity and strength of the rock mass.

[0079] Specific method: Select cement slurry, chemical slurry (such as polyurethane) or clay slurry, and select the appropriate slurry type according to the width and location of the crack.

[0080] Grouting pressure: controlled between 0.5-1.5MPa to ensure that the slurry fully fills the cracks.

[0081] Grouting volume: Calculated based on the crack volume, the grouting volume per cubic meter of crack should not be less than 500 liters.

[0082] For dynamic monitoring system: install dynamic monitoring system to monitor stress and displacement changes in real time and issue early warning.

[0083] Specific method: Sensor arrangement: Stress sensors and displacement sensors are arranged around stress concentration areas and cracks, and the sensor spacing does not exceed 1 meter.

[0084] Data collection frequency: Set to collect data every 10 minutes and transmit it to the monitoring center in real time.

[0085] Early warning system: Set the early warning threshold, and automatically trigger the early warning when the stress change exceeds 10% or the displacement change exceeds 5 mm.

[0086] For decompression measures: reduce the stress concentration of the rock mass by digging decompression holes or decompression grooves.

[0087] Specific method: Drill pressure relief holes in the stress concentration area. The hole depth should be more than 2 times the thickness of the stress concentration area. The hole diameter should be no less than 50 mm and the hole spacing should not exceed 1 meter.

[0088] A decompression groove shall be excavated in the stress concentration area, with a width of not less than 0.5 meters and a depth of at least 1.5 times the thickness of the stress concentration area.

[0089] Further explanation: the evaluation coefficients for providing optimization strategies for the current stress concentration areas are comprehensively generated. The optimization strategies include providing the best adjustment scheme for the foundation shape and support structure according to the structural rock strength of each stress concentration area, including:

[0090] Use evaluation coefficient Optimization strategy for characterizing the i-th stress concentration region:

[0091] ;

[0092] in, is the evaluation coefficient of the ith stress concentration area, and determines The valid value range of is (0,1);

[0093] a1, a2, a3 are weight factors of corresponding parameters, ; a1, a2, and a3 are numerically determined by the expert group through experimental data; is the adjustment factor, which is used to combine a1, a2, and a3 to ensure The value range of is (0,1).

[0094] Further explanation: the evaluation coefficient The value range (0,1) is divided into the first interval (0,0.35), the second interval [0.35,0.64] and the third interval (0.64,1); the optimal adjustment plan specifically includes the following contents:

[0095] for When the value is in the first interval (0, 0.35), it indicates that the rock strength in the current i-th stress concentration area is low; the optimization strategy requires the following adjustments:

[0096] For basic shape optimization: redesign the basic shape of the energy storage chamber, and use a composite shape or a multi-curvature shape to disperse the stress; the goal is to reduce the stress of the current i-th stress concentration area by 30%;

[0097] For support structure optimization: increase the density and length of prestressed anchor rods, with the density increasing by 40% and the length increasing by 20%;

[0098] For steel mesh: the mesh spacing is reduced by 30%;

[0099] For shotcrete: Increase the shotcrete thickness by 25%;

[0100] for When the value is in the second interval [0.35, 0.64], it indicates that the strength of the structural rock layer in the current i-th stress concentration area is medium;

[0101] The optimization strategy requires the following adjustments and optimizations;

[0102] For basic shape optimization: optimize to elliptical or composite shape to improve stress distribution;

[0103] The goal is to reduce the stress in the current i-th stress concentration area by 15%;

[0104] For support structure optimization: increase the density and length of prestressed anchor rods, with the density increasing by 25% and the length increasing by 10%;

[0105] For steel mesh: reduce the mesh spacing by 15%;

[0106] For shotcrete: Increase the shotcrete thickness by 15%;

[0107] for When the value is in the third interval (0.64, 1), it indicates that the rock strength in the current i-th stress concentration area is relatively high;

[0108] The optimization strategy is to continue to execute according to the current strategy;

[0109] For base shape optimization: base shape preservation;

[0110] The goal is to reduce the stress in the current i-th stress concentration area by 5%;

[0111] For support structure optimization: fine-tune and optimize the current prestressed anchor density and length, increasing the density by 6% and the length by 3%;

[0112] For steel mesh: reduce the mesh spacing by 5%;

[0113] For shotcrete: Add 5% to the shotcrete thickness.

[0114] The optimization strategy adjustment ranges corresponding to the first interval (0, 0.35), the second interval [0.35, 0.64] and the third interval (0.64, 1) decrease successively.

[0115] Embodiment 2:

[0116] Based on the first embodiment, this embodiment aims to verify the effectiveness of optimization strategies for different stress concentration areas, especially for the evaluation coefficient The optimization effect in different intervals is evaluated; based on the evaluation coefficient formula, different rock strength intervals are selected and different optimization strategies are implemented respectively; the test object is the selected underground energy storage chamber, and the evaluation coefficient of the stress concentration area Derived through measurement and calculation;

[0117] The trial is divided into three main phases:

[0118] 1) Preliminary assessment and regional division:

[0119] First, through geological survey and stress testing, a preliminary assessment of the various stress concentration areas of the energy storage chamber is carried out; based on the measurement results, the evaluation coefficient of each stress concentration area is calculated. The preliminary assessment results are as follows:

[0120] The i-th stress concentration area ;

[0121] The i+1th stress concentration area ;

[0122] The i+2 stress concentration area ;

[0123] According to the evaluation coefficients of different intervals, the foundation shape, support structure, steel mesh and shotcrete of the energy storage chamber are optimized and adjusted;

[0124] After implementing the optimal adjustment plan, re-measure each stress concentration area and record the stress value before and after optimization, prestressed anchor density and length, steel mesh spacing, shotcrete thickness and rock strength and other parameters; the effect of the optimization measures is verified by comparing and analyzing the data in Table 1 below;

[0125] Table 1

[0126] ;

[0127] It can be seen from the data record table that the parameters of different stress concentration areas change significantly before and after the implementation of the optimization measures;

[0128] For stress concentration area i, the stress value after optimization was reduced from 15MPa to 10.5MPa, a decrease of 30%. The density and length of prestressed anchor rods, the spacing of steel mesh and the thickness of shotcrete were adjusted according to the predetermined optimization strategy to achieve the expected effect. The rock formation strength increased from 30MPa to 39MPa, an increase of 30%.

[0129] For the stress concentration area i+1, after optimization, the stress value is reduced from 20MPa to 17MPa, a decrease of 15%, and the rock formation strength is increased from 40MPa to 46MPa, an increase of 15%;

[0130] For the stress concentration area i+2, after optimization, the stress value decreased from 25MPa to 23.75MPa, a decrease of 5%, and the rock formation strength increased from 50MPa to 52.5MPa, an increase of 5%.

[0131] Through the data analysis of this embodiment, the evaluation coefficient The corresponding optimization strategies in different intervals can significantly improve the rock strength in the stress concentration area. The optimization measures not only improve the structural stability of the energy storage chamber, but also provide a reference technical solution for subsequent similar projects.

[0132] Further explanation: re-performing the crack density and average displacement gradient data collection and analysis in step S3 and generating a revised first evaluation index specifically includes:

[0133] The first evaluation index of the i-th stress concentration area is marked as After correction and adjustment, the calculation formula after adjustment is as follows:

[0134] ;

[0135] in, is the first evaluation index after correction of the i-th stress concentration area; is the evaluation coefficient of the i-th stress concentration area when the optimal adjustment solution is not reached.

[0136] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0137] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing the design of a compressed air energy storage chamber, characterized in that: The specific steps include: Step S1: collecting stress distribution data at different positions on the surrounding rock of the energy storage chamber, performing simulation analysis through finite element analysis software, and constructing a stress distribution model based on the simulation results to identify stress concentration areas, and sort these stress concentration areas to form a stress distribution sequence set; Step S2: collecting seismic wave propagation velocity and acoustic wave attenuation coefficient data of different stress concentration areas in the stress distribution sequence concentration to form a preliminary data set after preprocessing; and using the preliminary data set to calculate the elastic modulus and wave velocity ratio of different stress concentration areas in the stress distribution sequence concentration; Step S3: collecting crack density and average displacement gradient data of each stress concentration area in the stress distribution sequence, and combining and analyzing the average stress of each stress concentration area with the corresponding crack density and average displacement gradient data, to generate a first evaluation index for evaluating the crack change trend corresponding to each stress concentration area, wherein the first evaluation index includes a low value range indicating rock mass stability; Step S4: determining the basic shape of each stress concentration area, combining the elastic modulus, wave velocity ratio and first evaluation index of the current stress concentration area, and comprehensively generating an evaluation coefficient for providing an optimization strategy for the current stress concentration area; The output value range of the evaluation coefficient is divided into several evaluation level intervals. The higher the interval level, the higher the structural rock strength in the current stress concentration area. The optimization strategy includes providing the best adjustment plan for the foundation shape and support structure according to the structural rock strength of each stress concentration area. Step S5: Re-simulate the shape of the energy storage chamber and the stress distribution of the support structure optimized in step S4 through the finite element analysis software to determine whether the output value of the first evaluation index in the current stress concentration area reaches the low value range. If it does not reach the low value range, perform the following steps: Re-perform the crack density and average displacement gradient data collection and analysis in step S3, and generate a revised first evaluation index; According to the corrected first evaluation index, re-execute step S4 to obtain an optimized evaluation coefficient; According to the optimized evaluation coefficient, the optimal adjustment scheme is re-implemented until the output value of the first evaluation index of the current stress concentration area reaches the low value range.

2. The method for optimizing the design of a compressed air energy storage chamber according to claim 1, characterized in that: Construct a stress distribution model and form a stress distribution sequence set, including: Continuously collect stress data at different positions of the energy storage chamber; Import the collected stress data into the data analysis software for data preprocessing; Input the shape of the energy storage chamber and the corresponding parameters of the support structure into the finite element analysis software for simulation; The simulation includes average meshing of the simulation model of the energy storage chamber; Import the preprocessed stress data into the finite element model, perform three-dimensional stress distribution calculation, and obtain the analysis and calculation results of each grid area; Analyze the calculation results, including calculating the maximum and minimum stress values ​​of different grid areas on the energy storage chamber, and set the limit value of the maximum stress value; By analyzing the calculation results, the grid areas with maximum stress values ​​greater than the limit value are identified as stress concentration areas in the energy storage chamber. These stress concentration areas are sorted from high to low according to the stress values ​​and numbered to form a stress distribution sequence set {1,2,…,i,…,n}, where n represents the total number of stress concentration areas and i represents the index of the i-th stress concentration area.

3. The method for optimizing the design of a compressed air energy storage chamber according to claim 2, characterized in that: Calculate the elastic modulus and wave velocity ratio of different stress concentration areas in the stress distribution sequence, including: The elastic modulus and wave speed ratio calculation formula are defined as follows: ； in, is the elastic modulus of the ith stress concentration area, is the rock density of the ith stress concentration area, is the seismic wave propagation velocity in the ith stress concentration area, is the shear wave velocity of the ith stress concentration region, is the Poisson’s ratio of the i-th stress concentration area; The value is between (0.2, 0.35); is the wave velocity ratio of the i-th stress concentration area.

4. The method for optimizing the design of a compressed air energy storage chamber according to claim 3, characterized in that: Generate a first evaluation index for evaluating the crack change trend corresponding to each stress concentration area, specifically including: The first evaluation index of the i-th stress concentration area is marked as , the calculation formula is as follows: ； Among them, the first evaluation index The value range is (0,1); is the constant weight factor of the i-th stress concentration area, which is used to adjust the sensitivity of the formula and is obtained through experimental calibration; is the average stress value of the i-th stress concentration area; is the crack density value of the i-th stress concentration area; is the average displacement gradient value of the i-th stress concentration area; e is the base of the natural logarithm; , and All are output values ​​that are normalized to the range of (0,1).

5. The method for optimizing the design of a compressed air energy storage chamber according to claim 4, characterized in that: The first evaluation index The value range (0,1) is divided into the low value range (0,0.4), the middle value range [0.4,0.6] and the high value range (0.6,1); For the low value range (0, 0.4), it means that the crack change trend in the stress concentration area is low level and the rock mass is stable; For the median range [0.4, 0.6], it means that the crack change trend in the stress concentration area is medium, and it is necessary to increase the current monitoring frequency and shorten the current maintenance cycle; For the high value range (0.6, 1), it means that the crack change trend in the stress concentration area is at a high level, and emergency measures need to be taken for reinforcement or adjustment.

6. The method for optimizing the design of a compressed air energy storage chamber according to claim 5, characterized in that: Comprehensively generate evaluation coefficients for providing optimization strategies for current stress concentration areas. The optimization strategies include providing optimal adjustment solutions for foundation shape and support structure according to the structural rock strength of each stress concentration area, including: Use evaluation coefficient Optimization strategy for characterizing the i-th stress concentration region: ； in, is the evaluation coefficient of the ith stress concentration area, and determines The valid value range of is (0,1); a1, a2, a3 are weight factors of corresponding parameters, ; is the adjustment factor, which is used to combine a1, a2, and a3 to ensure The value range of is (0,1).

7. The method for optimizing the design of a compressed air energy storage chamber according to claim 6, characterized in that: The evaluation coefficient The value range (0,1) is divided into the first interval (0,0.35), the second interval [0.35,0.64] and the third interval (0.64,1); the optimal adjustment plan specifically includes the following contents: for When the value is in the first interval (0, 0.35), it indicates that the rock strength in the current i-th stress concentration area is low; The optimization strategy requires the following adjustments: For basic shape optimization: redesign the basic shape of the energy storage chamber and use a composite shape or multi-curvature shape to disperse stress; For support structure optimization: increase the density and length of prestressed anchor rods; For steel mesh: reduce the mesh spacing; For shotcrete: increase the shotcrete thickness; for When the value is in the second interval [0.35, 0.64], it indicates that the strength of the structural rock layer in the current i-th stress concentration area is medium; The optimization strategy requires the following adjustments and optimizations; For basic shape optimization: optimize to elliptical or composite shape to improve stress distribution; For support structure optimization: increase the density and length of prestressed anchor rods; For steel mesh: reduce the mesh spacing; For shotcrete: increase the shotcrete thickness; for When the value is in the third interval (0.64, 1), it indicates that the rock strength in the current i-th stress concentration area is relatively high; The optimization strategy is to continue to execute according to the current strategy; For base shape optimization: base shape preservation; For support structure optimization: fine-tune and optimize the current prestressed anchor density and length; For steel mesh: reduce the mesh spacing; For shotcrete: increase the shotcrete thickness; The optimization strategy adjustment ranges corresponding to the first interval (0, 0.35), the second interval [0.35, 0.64] and the third interval (0.64, 1) decrease successively.

8. The method for optimizing the design of a compressed air energy storage chamber according to claim 7, characterized in that: The crack density and average displacement gradient data collection and analysis of step S3 are performed again, and a revised first evaluation index is generated, specifically including: If the first evaluation index of the current i-th stress concentration area In the middle range [0.4, 0.6] or high range (0.6, 1), the first evaluation index of the i-th stress concentration area is marked as After correction and adjustment, the calculation formula after adjustment is as follows: ； in, is the first evaluation index after correction of the i-th stress concentration area; is the evaluation coefficient of the i-th stress concentration area when the optimal adjustment solution is not reached.

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