Optimization design methods, devices, equipment, media and products for underground gas storage facilities

Abstract

This invention relates to the field of underground gas storage technology, and discloses an optimization design method, apparatus, equipment, medium, and product for underground gas storage. The optimization design method for underground gas storage includes: acquiring target engineering data of the underground gas storage; selecting decision variables from the target engineering data; constructing a multi-scenario working condition set based on the target engineering data; constructing multiple objective functions and multiple constraints based on the decision variables and the multi-scenario working condition set; sampling the value space of the decision variables to obtain multiple sample points; constructing an index determination model based on the multi-scenario working condition set, each sample point, multiple objective functions, and multiple constraints; and performing a global search based on the index determination model to obtain multiple optimized design schemes that satisfy multiple constraints. This invention combines multiple scenarios to construct an index determination model, achieving rapid response of various structural response indicators and improving the accuracy of the optimized design scheme.

Description

Technical Field

[0001] This invention relates to the field of compressed air energy storage power station technology, specifically to the optimized design methods, devices, equipment, media, and products of underground air storage facilities. Background Technology

[0002] The artificial underground gas storage facility in a compressed air energy storage power station is a key infrastructure component designed to meet the long-term energy storage and peak-shaving / frequency regulation needs of the power system. It involves injecting compressed air into an artificially constructed underground gas storage chamber under specific pressure and temperature conditions for storage, and driving the expander unit to generate electricity during the venting phase. As the core component of a compressed air energy storage power station, the artificial underground gas storage facility involves significant investment, complex structure and sealing system, and is subject to cyclic pressure and temperature fluctuations caused by periodic inflation and deflation during long-term operation. Furthermore, it is susceptible to risks such as stress accumulation, crack evolution, and deterioration of airtightness due to uncertainties such as the storage environment, the dispersion of surrounding rock parameters, and construction deviations. Therefore, it is necessary to conduct systematic optimization design of the artificial underground gas storage facility before project implementation to balance safety, airtightness, and economy.

[0003] In related technologies, the optimization design method for underground gas storage facilities involves determining mechanical boundary conditions, extracting the most unfavorable operating conditions, and calculating key parameters of the pressure-bearing structure based on theoretical equations. However, this method can only consider a single most unfavorable operating condition and cannot take into account the impact of dynamic changes in multiple operating conditions on design parameters during the entire operation cycle of the gas storage facility. Consequently, the resulting design scheme is not sufficiently consistent with actual conditions. Summary of the Invention

[0004] This invention provides an optimized design method, apparatus, equipment, medium, and product for underground gas storage facilities, in order to solve the problem that the optimized design methods for underground gas storage facilities in related technologies do not meet the actual situation.

[0005] In a first aspect, the present invention provides an optimization design method for an underground gas storage facility, comprising: acquiring target engineering data of the underground gas storage facility; selecting decision variables from the target engineering data; constructing a multi-scenario working condition set based on the target engineering data; the decision variables being adjustable structural and construction parameters; constructing multiple objective functions and multiple constraints based on the decision variables and the multi-scenario working condition set; the multiple objective functions being used to characterize the cost and safety margin optimization objectives, and the multiple constraints being used to characterize the technical boundaries of the underground gas storage facility design; sampling the value space of the decision variables to obtain multiple sample points; constructing an index determination model based on the multi-scenario working condition set, each sample point, multiple objective functions, and multiple constraints; the index determination model being used to predict various structural response indicators corresponding to the decision variables; and performing a global search based on the index determination model to obtain multiple optimized design schemes that satisfy multiple constraints.

[0006] The present invention provides an optimized design method for underground gas storage facilities. This method involves acquiring target engineering data for the underground gas storage facility, selecting decision variables from this data, constructing a multi-scenario operating condition set based on the target engineering data, and selecting decision variables with adjustable ranges from the target engineering data. This multi-scenario operating condition set provides a foundation for constructing subsequent indicator determination models for different operating conditions and multiple scenarios. Based on the decision variables and the multi-scenario operating condition set, the present invention constructs multiple objective functions and multiple constraints. These multiple objective functions correspond to multiple optimization objectives, establishing a complete multi-objective optimization system. The multiple constraints define the feasible design range, ensuring the stability and feasibility of the underground gas storage facility design. This invention samples the value space of decision variables to obtain multiple sample points. Based on a set of multiple scenario conditions, each sample point, multiple objective functions, and multiple constraints, an index determination model is constructed. Full-domain sampling is performed across the complete value range of the decision variables to obtain multiple sets of sample data. The index determination model is then constructed by integrating multiple factors related to multiple scenarios, objectives, and constraints, achieving multi-dimensional factor coupling modeling. By constructing the index determination model, various structural response indicators can be quickly predicted based on any given decision variable, reducing redundant calculations in the global search process and improving the overall efficiency of optimization design. This invention performs a global search based on the index determination model to obtain multiple optimized design schemes that satisfy multiple constraints. Global optimization search based on the index model can traverse all feasible parameter combinations to obtain multiple sets of optimized design schemes that fully satisfy all technical constraints. Compared with related technologies, this invention fully integrates the multi-condition operating characteristics of underground gas storage facilities, balancing engineering construction economy and long-term structural safety margins, making the obtained optimized design schemes more feasible and more in line with actual conditions.

[0007] In one optional implementation, acquiring target engineering data for an underground gas storage facility includes: acquiring static target engineering data and operational boundary data; unifying the data structures of the static target engineering data and operational boundary data to obtain initial engineering data; and generating corresponding identifiers for different types and scenarios of data in the initial engineering data to obtain target engineering data.

[0008] This invention unifies the data structure of static target engineering data and operational boundary data to obtain initial engineering data. It unifies the data structure, eliminates format differences between data from different sources, and generates corresponding identifiers for different types and scenarios of data in the initial engineering data. This enables rapid differentiation of data of different types and scenarios, avoids data confusion, and improves data processing efficiency.

[0009] In one optional implementation, a multi-scenario working condition set is constructed based on the target engineering data, including: determining candidate scenario elements based on the operational boundary data, construction deviations, and uncertainties in the surrounding rock parameters in the target engineering data; combining the candidate scenario elements according to preset rules to obtain an initial scenario set; performing preliminary screening of multiple candidate scenarios in the initial scenario set based on the estimated values ​​of stress, cracks, and leakage corresponding to each candidate scenario in the initial scenario set to obtain a target scenario set; screening multiple candidate scenarios in the target scenario set based on the degree of adverseness of each candidate scenario in the target scenario set to obtain a target working condition; and obtaining a multi-scenario working condition set based on the target scenario set and the target working condition.

[0010] In one optional implementation, multiple objective functions and multiple constraints are constructed based on decision variables and a set of multiple scenario conditions, including: determining multiple construction costs based on decision variables and the set of multiple scenario conditions; determining a target cost function based on the sum of the multiple construction costs; determining multiple structural safety control indicators based on decision variables and the set of multiple scenario conditions; constructing a safety margin objective function based on the multiple structural safety control indicators; and constructing constraints corresponding to each structural safety control indicator based on the multiple structural safety control indicators. The multiple constraints include structural strength constraints, crack width constraints, and gas leakage constraints.

[0011] In one optional implementation, an index determination model is constructed based on a multi-scenario working condition set, each sample point, multiple objective functions, and multiple constraints. This includes: determining the structural safety control index value and objective function value corresponding to each sample point under each candidate scenario in the multi-scenario working condition set, based on multiple objective functions and multiple constraints; and inputting the multi-scenario working condition set, each sample point, multiple objective functions, multiple constraints, the structural safety control index value and objective function value corresponding to each sample point under each candidate scenario in the multi-scenario working condition set, into an initial index determination model for training to obtain the index determination model.

[0012] In one optional implementation, the optimization design method for underground gas storage facilities further includes: obtaining user-input demand information, filtering multiple optimization design schemes based on the demand information, and obtaining a target optimization design scheme.

[0013] Secondly, the present invention provides an optimization design device for an underground gas storage facility, comprising: a working condition set determination module, used to acquire target engineering data of the underground gas storage facility, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structural and construction parameters; a condition construction module, used to construct multiple objective functions and multiple constraints based on the decision variables and the multi-scenario working condition set; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage facility design; an index model construction module, used to sample the value space of the decision variables to obtain multiple sample points, and construct an index determination model based on the multi-scenario working condition set, each sample point, multiple objective functions, and multiple constraints; the index determination model is used to predict various structural response indicators corresponding to the decision variables; and a design scheme determination module, used to perform a global search based on the index determination model to obtain multiple optimized design schemes that satisfy multiple constraints.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the optimized design method of the underground gas storage facility described in the first aspect or any corresponding embodiment.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the optimization design method for an underground gas storage facility according to the first aspect or any corresponding embodiment described above.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the optimization design method for an underground gas storage facility according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of the optimized design method for an underground gas storage facility according to an embodiment of the present invention; Figure 3This is a schematic diagram of the second process of the optimized design method for an underground gas storage facility according to an embodiment of the present invention; Figure 4 This is a structural block diagram of an optimized design device for an underground gas storage facility according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] As an optional application scenario of this invention, such as Figure 1 As shown, the optimized design system for this underground gas storage facility may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0023] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0024] In related technologies, the optimization design methods for underground gas storage facilities involve determining mechanical boundary conditions, extracting the most unfavorable operating conditions, and calculating key parameters of the pressure-bearing structure based on theoretical equations. However, structural safety, temperature field, leakage and airtightness, construction deviation sensitivity, and the identification of the most unfavorable operating conditions are often scattered across different software and teams, making automated iterative optimization under a unified data model difficult. The numerous design variables, multiple operating conditions (pressure-temperature cyclic spectrum), and discrete material and surrounding rock parameters result in extremely low computational efficiency using traditional methods. The results of the same design are difficult to reproduce under different data versions and different grid / boundary conditions, impacting design review, verification, and project delivery. Ultimately, only a single recommended solution is often provided, lacking a set of alternative solutions, sensitivity interpretation, key constraint dominating factors, and a description of the feasible region under constructability constraints.

[0025] This invention provides an optimized design method for underground gas storage facilities. By combining multiple scenarios to construct an index determination model, it achieves rapid response of various structural response indicators, thereby improving the accuracy of the optimized design scheme.

[0026] According to an embodiment of the present invention, an optimized design method for an underground gas storage facility is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] This embodiment provides an optimized design method for an underground gas storage facility, which can be used with computer equipment. Figure 2 This is a first flowchart of an optimized design method for an underground gas storage facility according to an embodiment of the present invention, as shown below. Figure 2 As shown, the process includes the following steps: Step S201: Obtain the target engineering data of the underground gas storage facility, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structure and construction parameters.

[0028] Among them, the underground gas storage facility is an artificial underground gas storage facility in a compressed air energy storage power station. It refers to an underground pressurized gas storage facility that injects compressed air into an artificially constructed or modified underground sealed space (such as an artificial cavern or a rock-lined cavern) under certain pressure and temperature conditions to meet the long-term energy storage and peak-shaving needs of the power system. During the gas release phase, it drives the expansion unit to generate electricity. The underground gas storage facility can be an artificial underground gas storage facility. The target engineering data is the original basic data for the underground gas storage facility, including static data and operational boundary data. For example, the static data includes the surrounding rock zoning and parameters (elastic modulus, Poisson's ratio, tensile strength, permeability coefficient, initial geostress field), groundwater conditions, cavern orientation and burial depth, lining and sealing material parameters, and construction allowable deviations (such as allowable void thickness range). The operational boundary data includes the maximum internal pressure, minimum internal pressure, pressurization rate, depressurization rate, number of cycles, and allowable maximum and minimum temperatures.

[0029] In some optional implementations, the decision variables are core input parameters that can be adjusted, modified, and iteratively optimized to ultimately determine the merits of the design scheme. Specifically, the decision variables include the equivalent inner radius of the tunnel, the effective length of the tunnel, the burial depth or overburden thickness, the concrete lining thickness, the sealing layer thickness, the grouting ring thickness or the grouting reinforcement influence range parameter, the lining reinforcement ratio, and other decision variables that can be extended to the tunnel group spacing, tunnel cross-sectional shape parameters, drainage structure parameters, etc. For example, the decision variables can be expressed as:

[0030] in, As decision variables, The equivalent inner radius of the cavern. For the effective length of the cavern, For burial depth or overburden thickness, For concrete lining thickness, For the thickness of the sealing layer, For parameters related to the thickness of the grouting ring or the range of influence of grouting reinforcement, This refers to the reinforcement ratio of the lining.

[0031] In some optional implementations, the multi-scenario operating condition set is a set of operating conditions that integrate different operating states, load conditions, external environmental fluctuations, and extreme service scenarios throughout the entire life cycle of the gas storage facility.

[0032] Step S202: Based on the decision variables and the set of multiple scenario conditions, construct multiple objective functions and multiple constraints; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage design.

[0033] Among them, the objective function is the core evaluation index used to quantitatively evaluate the merits of the design scheme. It is the optimization objective for optimization solution. For example, multiple objective functions include the objective cost function and the safety margin objective function.

[0034] In some alternative implementations, the constraints are hard-limit boundary conditions that the design parameters must meet. For example, multiple constraints include structural strength constraints, crack width constraints, and gas leakage constraints.

[0035] Step S203: Sample the value space of the decision variables to obtain multiple sample points. Based on the set of multiple scenario conditions, each sample point, multiple objective functions, and multiple constraints, construct an index determination model. The index determination model is used to predict the various structural response indicators corresponding to the decision variables.

[0036] The value space refers to the range of feasible values ​​and parameter intervals allowed for each decision variable. Experimental design sampling is carried out within the value space of the decision variables to obtain multiple sample points. Each sample point is a complete combination of decision variable parameters generated by sampling within the overall feasible value space of the decision variables.

[0037] In some alternative implementations, the index determination model is a surrogate prediction model, which establishes a mapping model from "input parameters to engineering performance indicators" and can quickly calculate all structural performance results corresponding to the input parameters, replacing high-precision simulation calculations.

[0038] Step S204: Based on the indicators, determine the model and perform a global search to obtain multiple optimized design schemes that satisfy multiple constraints.

[0039] Among them, global search is a multi-objective optimization algorithm that searches all potential high-quality parameter combinations in the complete feasible parameter space of all decision variables, without being limited to local intervals and avoiding local optimal solutions.

[0040] In some alternative implementations, the optimized design scheme is a complete gas storage design scheme consisting of multiple sets of decision variable parameters.

[0041] The optimization design method for underground gas storage facilities provided in this embodiment acquires target engineering data for the underground gas storage facility, selects decision variables from the target engineering data, constructs a multi-scenario operating condition set based on the target engineering data, and selects decision variables with adjustable ranges from the target engineering data to construct the multi-scenario operating condition set. This provides a foundation for the construction of subsequent indicator determination models for different operating conditions and multiple scenarios. Based on the decision variables and the multi-scenario operating condition set, this embodiment of the invention constructs multiple objective functions and multiple constraints. Multiple objective functions correspond to multiple optimization objectives, establishing a complete multi-objective optimization system. Multiple constraints define the feasible design range, ensuring the stability and feasibility of the underground gas storage facility design. This invention samples the value space of decision variables to obtain multiple sample points. Based on a set of multiple scenario conditions, each sample point, multiple objective functions, and multiple constraints, an index determination model is constructed. Full-domain sampling is performed across the complete value range of the decision variables to obtain multiple sets of sample data. The index determination model is then constructed by integrating multiple factors related to multiple scenarios, objectives, and constraints, achieving multi-dimensional factor coupling modeling. By constructing the index determination model, various structural response indicators can be quickly predicted based on any given decision variable, reducing redundant calculations in the global search process and improving the overall efficiency of optimization design. This invention performs a global search based on the index determination model to obtain multiple optimized design schemes that satisfy multiple constraints. Global optimization search based on the index model can traverse all feasible parameter combinations to obtain multiple sets of optimized design schemes that fully satisfy all technical constraints. Compared with related technologies, this invention fully integrates the multi-condition operating characteristics of underground gas storage facilities, balancing engineering construction economy and long-term structural safety margins, making the obtained optimized design schemes more feasible and more in line with actual conditions.

[0042] This embodiment provides an optimized design method for an underground gas storage facility, which can be used with computer equipment. Figure 3 This is a second flowchart of the optimized design method for an underground gas storage facility according to an embodiment of the present invention, as shown below. Figure 3 As shown, the process includes the following steps: Step S301: Obtain the target engineering data of the underground gas storage facility, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structure and construction parameters.

[0043] Specifically, step S301 includes: Step S3011: Obtain static target engineering data and running boundary data, and unify the data structure of static target engineering data and running boundary data to obtain initial engineering data.

[0044] In this process, static target engineering data and operational boundary data are written into a unified data structure to obtain initial engineering data.

[0045] Step S3012: Generate corresponding identifiers for different types and scenarios of data in the initial engineering data to obtain the target engineering data.

[0046] The data, including input data, parametric modeling rules, mesh generation rules, solution software environment, and criterion thresholds, varies across different types and scenarios. Unique identifiers are generated for each of these elements. Consistency checks are then performed based on these unique identifiers, and the entire calculation batch is frozen. When any unique identifier undergoes a valid change, a new batch identifier is generated, and the source of the change is recorded. Each calculation result is bound and stored to its corresponding unique identifiers for input data, parametric modeling rules, mesh generation rules, solution software environment, and criterion thresholds. This ensures comparability between different solutions and scenarios, reproducibility of calculation results, and controllable error propagation during optimization iterations.

[0047] Step S3013: Select decision variables from the target engineering data, determine candidate scenario elements based on the operational boundary data, construction deviations and uncertainties of surrounding rock parameters in the target engineering data, and combine the candidate scenario elements according to preset rules to obtain an initial scenario set.

[0048] The operational boundary data in the target engineering data includes maximum internal pressure, rapid pressure rise and fall, long-term pressure holding, extreme temperature, extreme water level, etc. The preset rules can be set according to the actual situation. For example, the preset rules are to carry out element classification and combination based on the influence weight, risk level and working condition coupling correlation of each candidate scenario element.

[0049] In some optional implementations, the target engineering data is classified according to the operational boundary data, construction deviations and uncertainties of surrounding rock parameters in the target engineering data to obtain multiple candidate scenario elements. The candidate scenario elements are then combined according to preset rules to obtain an initial scenario set.

[0050] For example, the pressure history, temperature boundary, water pressure boundary, construction deviation, and surrounding rock parameters are combined to form an initial scenario set. Taking the scenario of "rapid inflation-temperature rise-unfavorable surrounding rock combination" as an example: internal pressure time history p1(t): during the pressurization period, the internal pressure linearly increases from the minimum internal pressure to the maximum internal pressure, and the maximum internal pressure is maintained during the pressure holding period; temperature time history T1(t): during the pressurization stage, the temperature increases from the initial temperature to the maximum temperature, and the maximum temperature is maintained during the pressure holding stage; groundwater pressure boundary: the hydrostatic pressure distribution is determined according to the groundwater level; unfavorable combination of surrounding rock parameters: the combination of low elastic modulus, low tensile strength, and high permeability coefficient is selected.

[0051] Step S3014: Based on the estimated values ​​of stress, cracks and leakage corresponding to each candidate scenario in the initial scenario set, perform preliminary screening of multiple candidate scenarios in the initial scenario set to obtain the target scenario set.

[0052] For each candidate scenario, a simplified theoretical model is used to quickly calculate conservative estimates of stress, cracks, and leakage. The maximum ratio of the conservative estimate to the allowable value is used as the adverseness score for ranking. The top-scoring representatives are retained to obtain the target scenario set.

[0053] Step S3015: Based on the adverseness value of each candidate scenario in the target scenario set, filter multiple candidate scenarios in the target scenario set to obtain the target working condition. Based on the target scenario set and the target working condition, obtain a multi-scenario working condition set.

[0054] Specifically, the structural safety control index value corresponding to each candidate scenario is determined based on the data of each candidate scenario in the target scenario set, namely the maximum control stress. Maximum crack width Maximum leakage amount or leakage rate .

[0055] In some alternative implementations, the severity value of each candidate scenario is calculated based on the maximum control stress, the maximum crack width, and the maximum leakage amount or leakage rate. For example, the formula for determining the severity value is:

[0056] in, Candidate scenarios The degree of disadvantage, To obtain the maximum value, To give decision variables and candidate scenarios The maximum control stress obtained under the condition of The material's design strength is defined as the upper limit of the maximum allowable stress index. To give decision variables and candidate scenarios The maximum crack width obtained under the given conditions, The maximum allowable crack width or equivalent crack aperture upper limit. To give decision variables and candidate scenarios The maximum leakage amount or leakage rate obtained under the following conditions. The maximum allowable leakage amount.

[0057] In some alternative implementations, the candidate scenario with the highest degree of adverseness is selected as the target operating condition.

[0058] In some optional implementations, a multi-scenario set is formed by the target operating condition and the target scenario set.

[0059] In some alternative implementations, the set of multiple scenario conditions can be represented as:

[0060] in, It is a collection of multiple scenario working conditions. For the first One candidate scenario.

[0061] In some alternative implementations, each candidate scenario consists of a set of boundary conditions and unfavorable assumptions, including at least the following elements: internal pressure time history p(t), representing a function or piecewise function of the change in gas pressure inside the cavern over time; temperature boundary or temperature time history T(t), representing a function of the change in temperature of the cavern wall or gas over time, or given in the form of thermal boundary conditions; groundwater pressure boundary, representing the spatial distribution boundary of groundwater pressure (e.g., hydrostatic pressure distribution determined by water level and depth, or given by zonal seepage pressure); construction deviation scenario, representing a combination of unfavorable deviations introduced by construction, such as the thickness of voids between the lining and the surrounding rock, the range of voids, and deviations in lining thickness; and combination of surrounding rock parameters, representing a combination of values ​​for surrounding rock mechanics and permeability parameters, which can be representative values ​​or combinations of unfavorable values ​​(e.g., elastic modulus, tensile strength, permeability coefficient, initial in-situ stress, etc.).

[0062] Step S302: Based on the decision variables and the set of multiple scenario conditions, construct multiple objective functions and multiple constraints; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage design.

[0063] Specifically, step S302 includes: Step S3021: Based on the decision variables and the set of multiple scenario conditions, determine multiple construction costs, and based on the sum of the multiple construction costs, determine the target cost function.

[0064] For example, the target cost function can be expressed as:

[0065] in, Let the objective cost function be... To cover excavation costs, The equivalent inner radius of the cavern. The effective length of the cavern. To cover lining costs, For the thickness of the concrete lining, For the cost of metal lining, For the thickness of the sealing layer, For sealing costs, For metal thickness, To cover grouting costs, This refers to the thickness of the grouting ring or the range of influence of grouting reinforcement.

[0066] Step S3022: Based on the decision variables and the set of multiple scenario working conditions, determine multiple structural safety control indicators, and construct a safety margin objective function based on the multiple structural safety control indicators.

[0067] For example, the safety margin objective function can be expressed as:

[0068] in, For the safety margin objective function, To obtain the minimum value, To give decision variables and candidate scenarios The maximum control stress obtained under the condition of The material's design strength is defined as the upper limit of the maximum allowable stress index. To give decision variables and candidate scenarios The maximum crack width obtained under the given conditions, The maximum allowable crack width or equivalent crack aperture upper limit. To give decision variables and candidate scenarios The maximum leakage amount or leakage rate obtained under the following conditions. The maximum allowable leakage amount.

[0069] Step S3023: Based on multiple structural safety control indicators, construct the constraint conditions corresponding to each structural safety control indicator; the multiple constraint conditions include structural strength constraints, crack width constraints, and gas leakage constraints.

[0070] The structural strength constraint can be expressed as:

[0071] in, For structural strength constraints, To give decision variables and candidate scenarios The maximum control stress obtained under the condition of For material design strength.

[0072] In some alternative implementations, the crack width constraint can be expressed as:

[0073] in, To constrain the crack width, To give decision variables and candidate scenarios The maximum crack width obtained under the given conditions, This represents the maximum allowable crack width or the upper limit of the equivalent crack opening.

[0074] In some alternative implementations, gas leakage constraint can be expressed as:

[0075] in, To constrain gas leakage, To give decision variables and candidate scenarios The maximum leakage amount or leakage rate obtained under the following conditions. The maximum allowable leakage amount.

[0076] In some alternative implementations, Indicates the first There are several constraint functions used to determine whether the constraints are satisfied. When the constraint is satisfied, The time indicates that the limit has been exceeded or the condition has not been met.

[0077] Step S303: Sample the value space of the decision variables to obtain multiple sample points. Based on the set of multiple scenario conditions, each sample point, multiple objective functions, and multiple constraints, construct an index determination model. The index determination model is used to predict the various structural response indicators corresponding to the decision variables.

[0078] Specifically, step S303 includes: Step S3031: Sample the value space of the decision variables to obtain multiple sample points. Based on multiple objective functions and multiple constraints, determine the structural safety control index value and objective function value corresponding to each sample point under each candidate scenario in the multi-scenario working condition set.

[0079] Among them, structural safety control index values ​​include maximum control stress, maximum crack width, maximum leakage amount or leakage rate, etc.

[0080] Step S3032: Input the multi-scenario working condition set, each sample point, multiple objective functions, multiple constraints, the structural safety control index value corresponding to each sample point under each candidate scenario in the multi-scenario working condition set, and the objective function value into the initial index determination model for training, and obtain the index determination model.

[0081] In some optional implementations, under the condition of satisfying multiple constraints, the structural safety control index value and objective function value corresponding to each candidate scenario are calculated, which can be expressed as:

[0082] in, For the sample response of the nth sample point, The maximum control stress at the nth sample point. The maximum crack width at the nth sample point. The maximum leakage amount or leakage rate at the nth sample point. Let be the target cost function value for the nth sample point.

[0083] In some optional implementations, the set of multiple scenario conditions, each sample point, multiple objective functions, multiple constraints, the structural safety control index value corresponding to each sample point under each candidate scenario in the set of multiple scenario conditions, and the objective function value are input into the initial index determination model for training, so as to obtain the index determination model and output the prediction uncertainty.

[0084] Step S304: Based on the indicators, determine the model and perform a global search to obtain multiple optimized design schemes that satisfy multiple constraints. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0085] In some optional implementations, the optimization design method for underground gas storage facilities further includes: obtaining user-input demand information, filtering multiple optimization design schemes based on the demand information, and obtaining a target optimization design scheme.

[0086] For example, the demand information can be information such as cost priority, safety margin priority, or construction complexity priority. Based on the demand information, multiple optimization design schemes are filtered to obtain the target optimization design scheme that best matches the demand information.

[0087] The optimization design method for underground gas storage facilities provided in this embodiment is a multi-task intelligent agent collaborative optimization design process for artificial underground gas storage facilities. It integrates data governance, parametric modeling, operating condition spectrum generation, structural and airtightness verification, worst-case operating condition identification, optimization solution, and audit traceability into an automatically executable collaborative process. An adaptive supplementary mechanism for proxy models and high-precision calculations automatically triggers supplementary calculations based on constraint boundary distances and prediction uncertainties during the optimization process, balancing computational efficiency and reliability. This embodiment significantly improves design efficiency by replacing repetitive manual modeling and trial calculations with intelligent agent collaborative automation, covering more schemes and operating conditions within the same timeframe. It enhances scheme reliability by identifying the worst-case operating condition and applying uncertainty constraints, avoiding insufficient safety margins caused by designing only for a single operating condition or single parameter value. It balances structural safety and airtightness constraints by uniformly handling leakage constraints, crack constraints, and strength constraints within the same optimization framework, reducing scheme deviations such as "meeting strength requirements but unacceptable airtightness" or "pursuing only airtightness leading to overly conservative structures." Traceable, verifiable, and easy to deliver, any output solution comes with the input version, model version, threshold source, and explanation of the worst-case scenario, meeting the needs of engineering review and subsequent recalculation.

[0088] This embodiment also provides an optimized design device for an underground gas storage facility, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0089] This embodiment provides an optimized design device for an underground gas storage facility, such as... Figure 4 As shown, it includes: The working condition set determination module 401 is used to acquire the target engineering data of the underground gas storage facility, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structure and construction parameters.

[0090] The condition construction module 402 is used to construct multiple objective functions and multiple constraints based on decision variables and a set of multiple scenario conditions; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage design.

[0091] The indicator model construction module 403 is used to sample the value space of decision variables to obtain multiple sample points. Based on the set of multiple scenario conditions, each sample point, multiple objective functions, and multiple constraints, an indicator determination model is constructed. The indicator determination model is used to predict the various structural response indicators corresponding to the decision variables.

[0092] The design scheme determination module 404 is used to perform a global search based on the indicator determination model to obtain multiple optimized design schemes that satisfy multiple constraints.

[0093] In some optional implementations, the working condition set determination module 401 includes: The data structure processing unit is used to acquire static target engineering data and operational boundary data, and to unify the data structures of the static target engineering data and operational boundary data to obtain initial engineering data.

[0094] The identifier generation unit is used to generate corresponding identifiers for different types and scenarios of data in the initial engineering data, so as to obtain the target engineering data.

[0095] The initial scenario set determination unit is used to determine candidate scenario elements based on the operational boundary data, construction deviations, and uncertainties of surrounding rock parameters in the target engineering data, and to combine the candidate scenario elements according to preset rules to obtain the initial scenario set.

[0096] The preliminary screening unit is used to perform preliminary screening of multiple candidate scenarios in the initial scenario set based on the estimated values ​​of stress, cracks and leakage corresponding to each candidate scenario in the initial scenario set, so as to obtain the target scenario set.

[0097] The precise filtering unit is used to filter multiple candidate scenarios in the target scenario set based on the degree of adverseness of each candidate scenario in the target scenario set to obtain the target operating condition. Based on the target scenario set and the target operating condition, a multi-scenario operating condition set is obtained.

[0098] In some alternative implementations, the condition construction module 402 includes: The target cost determination unit is used to determine multiple cost components based on decision variables and a set of multiple scenario conditions, and to determine the target cost function based on the sum of the multiple cost components.

[0099] The safety margin determination unit is used to determine multiple structural safety control indicators based on decision variables and a set of multiple scenario operating conditions, and to construct a safety margin objective function based on these multiple structural safety control indicators.

[0100] The constraint construction unit is used to construct the constraint conditions corresponding to each structural safety control index based on multiple structural safety control indices; the multiple constraint conditions include structural strength constraints, crack width constraints, and gas leakage constraints.

[0101] In some alternative implementations, the indicator model construction module 403 includes: The index value determination unit is used to determine the structural safety control index value and objective function value of each sample point under each candidate scenario in the multi-scenario working condition set, based on multiple objective functions and multiple constraints.

[0102] The model training unit is used to input the set of multiple scenario conditions, each sample point, multiple objective functions, multiple constraints, the structural safety control index value corresponding to each sample point under each candidate scenario in the set of multiple scenario conditions, and the objective function value into the initial index determination model for training, so as to obtain the index determination model.

[0103] In some alternative implementations, the optimized design of the underground gas storage facility further includes: The design scheme filtering module is used to obtain the user's input requirement information, filter multiple optimized design schemes based on the requirement information, and obtain the target optimized design scheme.

[0104] The underground gas storage optimization design device provided in this embodiment of the invention can execute the underground gas storage optimization design method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0105] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0106] The following is a detailed reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0107] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0108] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the optimized design method for underground gas storage facilities according to embodiments of the present invention.

[0109] Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0110] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the optimized design method for underground gas storage shown in the above embodiments is implemented.

[0111] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0112] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An optimized design method for an underground gas storage facility, characterized in that, The method includes: Acquire target engineering data for an underground gas storage facility, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structural and construction parameters. Based on the decision variables and the set of multiple scenario conditions, multiple objective functions and multiple constraints are constructed; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage design. The value space of the decision variables is sampled to obtain multiple sample points. Based on the multi-scenario working condition set, each sample point, the multiple objective functions, and the multiple constraints, an index determination model is constructed. The index determination model is used to predict the various structural response indicators corresponding to the decision variables. Based on the aforementioned indicators, a global search is performed on the determined model to obtain multiple optimized design schemes that satisfy the aforementioned multiple constraints.

2. The method according to claim 1, characterized in that, The acquisition of target engineering data for underground gas storage facilities includes: Acquire static target engineering data and operational boundary data, and unify the data structure of the static target engineering data and operational boundary data to obtain initial engineering data; Generate corresponding identifiers for different types and scenarios of data in the initial engineering data to obtain the target engineering data.

3. The method according to claim 1 or 2, characterized in that, The step of constructing a multi-scenario working condition set based on the target project data includes: Based on the operational boundary data, construction deviations, and uncertainties in the surrounding rock parameters in the target project data, candidate scenario elements are determined, and the candidate scenario elements are combined according to preset rules to obtain an initial scenario set. Based on the estimated values ​​of stress, cracks, and leakage corresponding to each candidate scenario in the initial scenario set, a preliminary screening of multiple candidate scenarios in the initial scenario set is performed to obtain a target scenario set; Based on the degree of adverseness of each candidate scenario in the target scenario set, multiple candidate scenarios in the target scenario set are filtered to obtain the target working condition. Based on the target scenario set and the target working condition, the multi-scenario working condition set is obtained.

4. The method according to claim 1 or 2, characterized in that, The step of constructing multiple objective functions and multiple constraints based on the decision variables and the set of multiple scenario conditions includes: Based on the decision variables and the set of multiple scenario conditions, multiple construction costs are determined, and based on the sum of the multiple construction costs, a target cost function is determined. Based on the decision variables and the multi-scenario working condition set, multiple structural safety control indicators are determined, and a safety margin objective function is constructed based on the multiple structural safety control indicators. Based on the plurality of structural safety control indicators, the constraint conditions corresponding to each of the structural safety control indicators are constructed; the plurality of constraint conditions include structural strength constraints, crack width constraints, and gas leakage constraints.

5. The method according to claim 1 or 2, characterized in that, The step of constructing an index determination model based on the multi-scenario working condition set, each of the sample points, the multiple objective functions, and the multiple constraints includes: Based on the multiple objective functions and multiple constraints, determine the structural safety control index value and objective function value corresponding to each sample point under each candidate scenario in the set of multiple scenario conditions; The set of multiple operating conditions, each of the sample points, the multiple objective functions, the multiple constraints, the structural safety control index value corresponding to each sample point under each candidate scenario in the set of multiple operating conditions, and the objective function value are input into the initial index determination model for training to obtain the index determination model.

6. The method according to claim 1 or 2, characterized in that, The method further includes: Obtain the user's input requirements information, filter through multiple optimization design schemes based on the requirements information, and obtain the target optimization design scheme.

7. An optimized design device for an underground gas storage facility, characterized in that, The device includes: The working condition set determination module is used to acquire target engineering data of underground gas storage, select decision variables from the target engineering data, and construct a multi-scenario working condition set based on the target engineering data; the decision variables are adjustable structures and construction parameters. The condition construction module is used to construct multiple objective functions and multiple constraints based on the decision variables and the set of multiple scenario conditions; the multiple objective functions are used to characterize the cost and safety margin optimization objectives, and the multiple constraints are used to characterize the technical boundaries of the underground gas storage design; The indicator model construction module is used to sample the value space of the decision variables to obtain multiple sample points, and construct an indicator determination model based on the multi-scenario working condition set, each sample point, the multiple objective functions, and the multiple constraints; the indicator determination model is used to predict the various structural response indicators corresponding to the decision variables. The design scheme determination module is used to perform a global search based on the indicators to obtain multiple optimized design schemes that satisfy the multiple constraints.

8. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the optimization design method for the underground gas storage facility as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the optimization design method for the underground gas storage facility as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the optimization design method for an underground gas storage facility as described in any one of claims 1 to 6.

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