Method for suppressing pressure fluctuation in air bag storage stage of underwater compressed air energy storage

Abstract

The application relates to a method for suppressing pressure fluctuation of an air bag in a gas storage stage of underwater compressed air energy storage. The method comprises the following steps: in the gas storage stage of the underwater compressed air energy storage, current internal pressure data of the air bag are collected in real time; according to the current internal pressure data and preset reference pressure data, current pressure deviation and a current pressure deviation change rate of the air bag are determined; according to the current pressure deviation and the current pressure deviation change rate, proportional parameters, integral parameters and differential parameters of a valve opening degree change amount of an air inlet valve of the air bag are determined; according to the proportional parameters, the integral parameters and the differential parameters, the valve opening degree change amount is obtained, and the opening and closing of the air inlet valve of the air bag are adjusted based on the valve opening degree change amount, so as to suppress internal pressure fluctuation of the underwater compressed air energy storage in the gas storage stage of the air bag. The method can improve the safety of the underwater compressed air energy storage.

Description

Technical Field

[0001] This application relates to the field of energy storage system technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium and computer program product for suppressing pressure fluctuations during the air storage stage of underwater compressed air energy storage. Background Technology

[0002] Underwater compressed air energy storage technology, as a derivative technology of compressed air energy storage systems, can effectively solve the problems of poor geographical adaptability, high construction costs, and significant potential environmental impact of traditional terrestrial compressed air energy storage systems. This technology converts surplus electricity into compressed air and stores it in sealed air chambers deployed underwater. During peak electricity demand periods, the stored air is released to drive power generation devices, completing the energy recovery process. Due to the excellent pressure adaptability and space utilization potential of water bodies, this approach has significant advantages in system layout flexibility, energy density, and construction costs, making it an important development direction in the field of new energy storage.

[0003] However, in underwater compressed air energy storage systems using flexible airbags as storage units, the internal pressure is highly sensitive to factors such as gas injection rate, water depth, ambient pressure, and external hydrodynamic loads due to the strong deformability of the airbag structure. During the gas storage phase, especially the initial injection phase, gas tends to accumulate at the top of the airbag under buoyancy, causing the internal gas pressure to exhibit a non-linear growth trend. When the internal pressure approaches or even exceeds the external ambient pressure, the system experiences a momentary overpressure, which is then gradually reduced to a steady-state pressure through material release and environmental regulation. However, during this process, the internal pressure of the airbag may fluctuate frequently around the target pressure value. Frequent pressure jumps can cause structural risks such as airbag material fatigue and decreased sealing performance, thus affecting the safety of the underwater compressed air energy storage system. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for suppressing pressure fluctuations during the air bladder storage stage of underwater compressed air energy storage, which can improve the safety of underwater compressed air energy storage.

[0005] In a first aspect, this application provides a method for suppressing pressure fluctuations during the gas storage stage of an underwater compressed air energy storage system, comprising:

[0006] During the air storage phase of underwater compressed air energy storage, the current internal air pressure data of the airbag is collected in real time.

[0007] Based on the current internal air pressure data and the preset reference pressure data, determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag;

[0008] Based on the current pressure deviation and the rate of change of the current pressure deviation, determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag;

[0009] The valve opening change is obtained based on the proportional parameter, the integral parameter, and the differential parameter. Based on the valve opening change, the opening and closing of the air inlet valve of the airbag is adjusted to suppress the internal pressure fluctuations that occur during the airbag storage phase of the underwater compressed air energy storage.

[0010] In one embodiment, determining the proportional parameter, integral parameter, and derivative parameter for adjusting the valve opening change of the airbag's intake valve based on the current pressure deviation and the rate of change of the current pressure deviation includes:

[0011] Based on the preset fuzzy level membership information, determine several first fuzzy levels corresponding to the current pressure deviation and the first matching membership degree corresponding to each first fuzzy level, and determine several second fuzzy levels corresponding to the current pressure deviation change rate and the second matching membership degree corresponding to each second fuzzy level.

[0012] Based on the first fuzzy levels and the second fuzzy levels, several third fuzzy levels corresponding to each target parameter are determined; the target parameters are the proportional parameter, the integral parameter, and the differential parameter.

[0013] The target parameters are calculated based on each third fuzziness level, the first matching membership degree, and the second matching membership degree.

[0014] In one embodiment, calculating the target parameters based on each third fuzziness level, the first matching membership degree, and the second matching membership degree includes:

[0015] Based on the first matching membership degree and the second matching membership degree, determine the third matching membership degree corresponding to each third fuzzy level;

[0016] For any target parameter, the fuzzy output value corresponding to each third fuzzy level of the target parameter is weighted and averaged with the corresponding third matching membership degree to obtain the change of the target parameter.

[0017] The target parameter is obtained based on the amount of change of any of the target parameters.

[0018] In one embodiment, obtaining the target parameter based on the change in any target parameter includes:

[0019] In the case that the current adjustment is the first adjustment, the change in any target parameter is weighted and summed with the preset initial value corresponding to any target parameter to obtain the target parameter of the current adjustment.

[0020] If the current adjustment is not the first adjustment, the change in any of the target parameters is weighted and summed with the target parameter determined in the previous adjustment to obtain the target parameter of the current adjustment.

[0021] In one embodiment, obtaining the valve opening change based on the proportional parameter, the integral parameter, and the derivative parameter includes:

[0022] Based on the proportional-integral-derivative control method, the proportional parameter, the integral parameter, and the derivative parameter are used as input parameters to calculate the change in valve opening.

[0023] In one embodiment, the real-time acquisition of the airbag's current internal air pressure data includes:

[0024] Multiple pressure sensors are used to collect pressure sensor detection data in real time; the pressure sensors include at least a sensor located at the top of the airbag, a sensor located at the equatorial plane of the airbag, and a sensor located at the bottom of the airbag.

[0025] The current internal air pressure data is obtained based on the detection data from the multiple pressure sensors.

[0026] Secondly, this application also provides a pressure fluctuation suppression device for the gas storage stage of underwater compressed air energy storage, comprising:

[0027] The air pressure acquisition module is used to collect the current internal air pressure data of the airbag in real time during the air storage stage of underwater compressed air energy storage.

[0028] The deviation determination module is used to determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag based on the current internal air pressure data and the preset reference pressure data.

[0029] The parameter acquisition module is used to determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag, based on the current pressure deviation and the rate of change of the current pressure deviation.

[0030] The valve adjustment module is used to obtain the valve opening change amount according to the proportional parameter, the integral parameter and the differential parameter, and adjust the opening and closing of the air inlet valve of the airbag based on the valve opening change amount, so as to suppress the internal pressure fluctuation that occurs during the airbag storage stage of the underwater compressed air storage.

[0031] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0032] During the air storage phase of underwater compressed air energy storage, the current internal air pressure data of the airbag is collected in real time.

[0033] Based on the current internal air pressure data and the preset reference pressure data, determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag;

[0034] Based on the current pressure deviation and the rate of change of the current pressure deviation, determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag;

[0035] The valve opening change is obtained based on the proportional parameter, the integral parameter, and the differential parameter. Based on the valve opening change, the opening and closing of the air inlet valve of the airbag is adjusted to suppress the internal pressure fluctuations that occur during the airbag storage phase of the underwater compressed air energy storage.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] During the air storage phase of underwater compressed air energy storage, the current internal air pressure data of the airbag is collected in real time.

[0038] Based on the current internal air pressure data and the preset reference pressure data, determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag;

[0039] Based on the current pressure deviation and the rate of change of the current pressure deviation, determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag;

[0040] The valve opening change is obtained based on the proportional parameter, the integral parameter, and the differential parameter. Based on the valve opening change, the opening and closing of the air inlet valve of the airbag is adjusted to suppress the internal pressure fluctuations that occur during the airbag storage phase of the underwater compressed air energy storage.

[0041] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0042] During the air storage phase of underwater compressed air energy storage, the current internal air pressure data of the airbag is collected in real time.

[0043] Based on the current internal air pressure data and the preset reference pressure data, determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag;

[0044] Based on the current pressure deviation and the rate of change of the current pressure deviation, determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag;

[0045] The valve opening change is obtained based on the proportional parameter, the integral parameter, and the differential parameter. Based on the valve opening change, the opening and closing of the air inlet valve of the airbag is adjusted to suppress the internal pressure fluctuations that occur during the airbag storage phase of the underwater compressed air energy storage.

[0046] The aforementioned method, device, computer equipment, computer-readable storage medium, and computer program product for suppressing pressure fluctuations during the gas storage stage of underwater compressed air energy storage firstly, during the gas storage stage, collects real-time internal air pressure data of the gasbag. By arranging a real-time pressure sensor inside the gasbag, the current internal pressure data of the gasbag can be continuously acquired during the storage stage, ensuring that the system has an immediate response capability to changes in the state of the gas injection process. This provides basic data support for subsequent adjustment and judgment, and helps to detect abnormal fluctuation trends in the early stages of pressure changes, avoiding control lag due to delayed response. Next, based on the current internal air pressure data and preset reference pressure data, the current pressure deviation and the rate of change of the current pressure deviation of the gasbag are determined. By comparing the real-time collected current air pressure data with the preset reference pressure based on the installation water depth, the deviation between the current pressure of the gasbag and the target pressure can be quantified, and the rate of change of the deviation can be calculated in conjunction with the time axis, thereby accurately reflecting the direction and speed of pressure fluctuations, improving the system's ability to perceive abnormal change trends, and providing support for subsequent adjustment. The system establishes clear control criteria. Then, based on the current pressure deviation and its rate of change, it determines the proportional, integral, and derivative parameters of the valve opening change of the air bladder's intake valve. Calculating these parameters based on the pressure deviation and its rate of change helps construct a dynamically adjustable control model. This allows the control strategy to not only respond instantly to sudden changes but also correct steady-state errors over the long term and suppress drastic fluctuations, improving the accuracy and robustness of the control process. Finally, based on the proportional, integral, and derivative parameters, the valve opening change is obtained. Based on this change, the air bladder's intake valve is adjusted to suppress internal pressure fluctuations during the air bladder's storage phase. By using proportional, integral, and derivative parameters to calculate the valve opening change and adjusting the intake valve opening in real time, the balance between air intake and pressure control requirements can be dynamically matched. This effectively avoids overpressure problems caused by instantaneous over-injection, improves the smoothness of the pressure recovery process, and reduces the impact load on the air bladder structure. In the above method, by constructing a closed-loop regulation mechanism of "pressure monitoring - error identification - parameter adjustment - execution control", it is possible to suppress sudden changes and fluctuations in the internal pressure of the airbag in real time during the gas storage process, improve the system's ability to regulate nonlinear pressure dynamics, and avoid problems such as material fatigue, sealing failure and structural damage caused by pressure runaway. This enhances the structural stability and operational safety of the entire underwater compressed air energy storage system during operation. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for suppressing pressure fluctuations during the air storage stage of underwater compressed air energy storage in one embodiment.

[0049] Figure 2 This is a flowchart illustrating the target parameter acquisition steps in one embodiment;

[0050] Figure 3 This is a schematic diagram of an underwater compressed air energy storage device in one embodiment;

[0051] Figure 4 This is a schematic diagram showing the arrangement of pressure sensors in the air bladder of an underwater compressed air energy storage device in one embodiment;

[0052] Figure 5 This is a structural block diagram of a pressure fluctuation suppression device for the airbag storage stage of underwater compressed air energy storage in one embodiment.

[0053] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0055] In one embodiment, such as Figure 1 As shown, a method for suppressing pressure fluctuations during the gas storage stage of underwater compressed air energy storage is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, or to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. The terminal can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. In this embodiment, the method includes the following steps:

[0056] Step S101: During the air storage stage of underwater compressed air energy storage, the current internal air pressure data of the airbag is collected in real time.

[0057] Among them, air pressure data refers to the actual pressure value of the gas per unit volume inside the airbag obtained by pressure sensors, which is used to reflect the real-time pressure state of the airbag during the air storage stage; the inside of the airbag refers to the air storage space surrounded by the closed flexible airbag shell, and the internal air pressure is affected by the combined effects of air intake rate, underwater depth and structural deformation.

[0058] For example, the terminal establishes a communication connection with multiple pressure sensors deployed within the airbag, periodically acquiring the current pressure values ​​reported by each sensor. To improve the representativeness and stability of the data, the terminal can use methods such as weighted averaging, filtering smoothing, or reliability fusion to process the raw pressure data from multiple sampling points in real time, thereby obtaining a unified air pressure assessment value inside the airbag at the current moment. The terminal can also configure timestamp and sampling period parameters in the data acquisition module to ensure the traceability and timeliness of the collected air pressure data, providing basic support for subsequent pressure deviation calculation and adjustment control.

[0059] Step S102: Determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag based on the current internal air pressure data and the preset reference pressure data.

[0060] Among them, pressure deviation refers to the difference between the actual air pressure value inside the airbag currently collected and the target reference pressure value, which is used to quantify the degree of deviation between the current air storage state and the ideal steady state; pressure deviation change rate refers to the change range of pressure deviation per unit time, which is used to reflect the speed and direction of the air pressure deviation trend; preset reference pressure data refers to the target hydrostatic pressure value calculated based on physical parameters such as the water depth of the airbag installation, water density and gravitational acceleration, which is usually preset during the system initialization stage.

[0061] For example, the terminal compares the collected current air pressure value with preset reference pressure data in real time, and obtains the pressure deviation using a difference calculation method. Simultaneously, the terminal retrieves historical deviation values ​​stored in the previous sampling period, subtracts the current deviation value from the current deviation value, and divides by the sampling time interval to obtain the current pressure deviation change rate. To improve response sensitivity and anti-interference capability, the terminal can also perform filtering, boundary limiting, or outlier removal operations on the calculation results to ensure the stability and accuracy of the obtained pressure deviation and change rate, thereby providing a reliable input basis for subsequent control strategies.

[0062] Step S103: Based on the current pressure deviation and the rate of change of the current pressure deviation, determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag.

[0063] Among them, the proportional parameter is the instantaneous response coefficient used to reflect the current pressure deviation to the regulation behavior, which is suitable for rapid correction of instantaneous errors; the integral parameter is the regulation coefficient used to accumulate the influence of historical deviations, which can eliminate steady-state errors caused by long-term small deviations; the derivative parameter is the regulation coefficient used to characterize the trend of deviation changes, which helps to predict and suppress system overshoot or violent fluctuations in advance; the three together constitute the core regulation factors in the proportional-integral-derivative control strategy, which determine the dynamic response performance of the regulation output.

[0064] For example, the terminal uses the obtained pressure deviation and pressure deviation change rate as input variables, inputting them into the inference model constructed based on fuzzy control logic. By searching a preset fuzzy rule base, it matches the corresponding output fuzzy level. The terminal further employs a defuzzification method, such as the centroid method, to perform weighted calculations on each fuzzy output level and its membership degree, obtaining the changes in proportional parameter, integral parameter, and derivative parameter, respectively. Then, the terminal weightedly superimposes these changes with the initial proportional, integral, and derivative parameters set by the system to determine the corresponding proportional, integral, and derivative parameter values ​​for the current control cycle, for use in subsequent valve regulation and control.

[0065] Step S104: Based on the proportional parameter, integral parameter and differential parameter, obtain the valve opening change, and adjust the opening and closing of the air inlet valve of the airbag according to the valve opening change, so as to suppress the internal pressure fluctuation that occurs during the airbag storage stage of underwater compressed air energy storage.

[0066] Among them, the valve opening change refers to the adjustment range of the intake valve opening within the current control cycle compared to the previous cycle, which is used to dynamically control the injection rate of compressed air; the intake valve is a control actuator connecting the air source and the air bladder, and its opening size directly affects the pressure change trend inside the air bladder.

[0067] For example, based on the acquired proportional, integral, and derivative parameters, the terminal calculates the valve opening change using a proportional-integral-derivative (PID) control algorithm. It comprehensively considers the current pressure deviation, cumulative deviation, and deviation rate of change to ensure that the adjustment has both immediate response capability and suppresses overshoot and fluctuations. The terminal then applies the calculated valve opening change to the control signal output module, driving the electrically controlled or pneumatic valve at the bottom of the airbag to perform opening and closing actions, thereby dynamically adjusting the airflow entering the airbag. Through this adjustment process, the terminal can achieve closed-loop control of the internal pressure during the gas storage stage, reducing the system risks caused by nonlinear pressurization, effectively suppressing structural fatigue or sealing failure caused by pressure fluctuations, and enhancing the stability and safety of system operation.

[0068] Furthermore, the terminal can also use a feature extraction model (such as a convolutional neural network model) to extract features from the proportional parameter, integral parameter, and differential parameter, obtaining a first feature vector corresponding to the proportional parameter, a second feature vector corresponding to the integral parameter, and a third feature vector corresponding to the differential parameter. Then, the first, second, and third feature vectors are input into an attention mechanism model, which determines the first weight, the second weight, and the third weight corresponding to the third feature vector. Next, the first, second, and third feature vectors are fused according to their respective weights (e.g., weighted summation) to obtain a fused feature vector. This fused feature vector is then input into a pre-trained first valve opening change prediction model (such as a neural network model or deep learning model) to obtain multiple predicted valve opening changes and their corresponding prediction probabilities. Finally, the predicted valve opening change with the highest prediction probability is selected from these multiple predicted valve opening changes as the first valve opening change. Then, the terminal inputs the fused feature vector into a pre-trained second valve opening change prediction model (such as a machine learning model, a large AI model, etc.). The pre-trained second valve opening change prediction model performs a series of prediction processes on the fused feature vector to obtain the second valve opening change. The model structures of the first valve opening change prediction model and the second valve opening change prediction model are different. Finally, according to the first model weights corresponding to the first valve opening change prediction model and the second model weights corresponding to the second valve opening change prediction model, the first valve opening change and the second valve opening change are fused (such as weighted summation) to obtain the fused valve opening change, which is used as the valve opening change.

[0069] In the aforementioned method for suppressing pressure fluctuations during the gas storage stage of underwater compressed air energy storage, firstly, during the gas storage stage, the current internal pressure data of the gasbag is collected in real time. By arranging a real-time pressure sensor inside the gasbag, the current internal pressure data can be continuously acquired during the gas storage stage, ensuring that the system has an immediate response capability to changes in the state of the gas injection process. This provides basic data support for subsequent adjustment and judgment, and helps to detect abnormal fluctuation trends in the early stages of pressure changes, avoiding control lag due to delayed response. Next, based on the current internal pressure data and preset reference pressure data, the current pressure deviation and the rate of change of the current pressure deviation of the gasbag are determined. By comparing the real-time collected current pressure data with the preset reference pressure based on the installation water depth, the deviation between the current pressure of the gasbag and the target pressure can be quantified, and the rate of change of the deviation can be calculated in conjunction with the time axis, thereby accurately reflecting the direction and speed of pressure fluctuations, improving the system's ability to perceive abnormal change trends, and providing a clear control basis for subsequent adjustments. Then, based on By determining the proportional, integral, and derivative parameters of the valve opening change of the air intake valve used to adjust the airbag, based on the current pressure deviation and its rate of change, it is possible to construct a dynamically adjustable control model. This allows the control strategy to not only respond instantly to sudden changes but also correct steady-state errors over the long term and suppress drastic fluctuations, thus improving the accuracy and robustness of the adjustment process. Finally, based on the proportional, integral, and derivative parameters, the valve opening change is obtained. Based on this change, the opening and closing of the air intake valve of the airbag are adjusted to suppress internal pressure fluctuations that occur during the airbag's storage phase in underwater compressed air storage. By using the proportional, integral, and derivative parameters to calculate the valve opening change and adjusting the air intake valve opening in real time, the balance between the air intake volume and pressure control requirements can be dynamically matched, effectively avoiding overpressure problems caused by instantaneous over-injection, while improving the smoothness of the pressure recovery process and reducing the impact load on the airbag structure. In the above method, by constructing a closed-loop regulation mechanism of "pressure monitoring - error identification - parameter adjustment - execution control", it is possible to suppress sudden changes and fluctuations in the internal pressure of the airbag in real time during the gas storage process, improve the system's ability to regulate nonlinear pressure dynamics, and avoid problems such as material fatigue, sealing failure and structural damage caused by pressure runaway. This enhances the structural stability and operational safety of the entire underwater compressed air energy storage system during operation.

[0070] In one exemplary embodiment, such as Figure 2 As shown, step S103 above, which determines the proportional, integral, and derivative parameters of the valve opening change of the air intake valve used to adjust the airbag based on the current pressure deviation and the rate of change of the current pressure deviation, can also be achieved through the following steps:

[0071] Step S201: Based on the preset fuzzy level membership information, determine several first fuzzy levels corresponding to the current pressure deviation and the first matching membership degree corresponding to each first fuzzy level, and determine several second fuzzy levels corresponding to the current pressure deviation change rate and the second matching membership degree corresponding to each second fuzzy level.

[0072] Step S202: Based on each first fuzzy level and each second fuzzy level, determine several third fuzzy levels corresponding to each target parameter; the target parameters are proportional parameters, integral parameters, and differential parameters.

[0073] Step S203: Calculate the target parameters based on each third fuzzy level, the first matching membership degree, and the second matching membership degree.

[0074] The first fuzzy level refers to the set of linguistic values ​​that divide the pressure deviation according to a preset membership function, such as "negative large", "negative medium", "zero", "positive small", etc.; the second fuzzy level refers to the set of linguistic levels that divide the pressure deviation change rate according to the same principle; the third fuzzy level refers to the output level obtained by mapping the first fuzzy level and the second fuzzy level through a fuzzy rule table, which is used to represent the fuzzy output state that the target parameter should correspond to under the input conditions; the matching membership degree refers to the matching strength between the current value and the fuzzy level, and the value range is usually between 0 and 1.

[0075] For example, the terminal first fuzzifies the current pressure deviation into multiple first fuzzy levels based on preset triangular or trapezoidal membership functions, with each level corresponding to a first matching membership degree. Similarly, it fuzzifies the current pressure deviation change rate into multiple second fuzzy levels, with each level corresponding to a second matching membership degree. The terminal combines and maps each set of first and second fuzzy levels according to a fuzzy rule base (e.g., the fuzzy level matching table shown in Table 1), determining the third fuzzy level set for the proportional, integral, and differential parameters. Subsequently, the terminal calculates the activation degree of each rule as the third matching membership degree by multiplying the first and second matching membership degrees of each activation rule. The terminal further uses a weighted average or centroid method to calculate the membership function center value and its third matching membership degree corresponding to each third fuzzy level, ultimately obtaining the changes in the proportional, integral, and differential parameters, providing input for subsequent control steps.

[0076] Table 1 Fuzzy Level Matching Table

[0077]

[0078] Among them, NB, NM, NS, Z, PS, PM and PB are negative large, negative medium, negative small, zero, positive small, positive medium and positive large, respectively.

[0079] In this embodiment, based on the current pressure deviation and its rate of change, fuzzy logic reasoning combined with a weighted defuzzification method is used to dynamically calculate the adjustment amounts of proportional, integral, and derivative parameters. Through fuzzy level matching and membership weighting, the nonlinear variation characteristics of the internal pressure of the airbag are accurately reflected, achieving smooth and effective parameter adjustment. By utilizing the real-time adjusted proportional, integral, and derivative parameters, the change in the intake valve opening is precisely controlled, effectively suppressing overshoot and fluctuations in the internal pressure of the airbag during the gas storage phase, mitigating the risk of airbag material fatigue and seal failure, and improving the operational stability and safety performance of the underwater compressed air energy storage system.

[0080] In an exemplary embodiment, step S203, which calculates the target parameter based on each third fuzziness level, the first matching membership degree, and the second matching membership degree, further includes: determining the third matching membership degree corresponding to each third fuzziness level based on the first matching membership degree and the second matching membership degree; for any target parameter, performing a weighted average calculation of the fuzzy output value corresponding to each third fuzziness level of the target parameter with the corresponding third matching membership degree to obtain the change amount of the target parameter; and obtaining the target parameter based on the change amount of the target parameter.

[0081] The first and second matching membership degrees represent the degree of matching between the current pressure deviation and the rate of change of pressure deviation and their respective fuzzy levels, respectively, with values ​​ranging from 0 to 1. The third fuzzy level is the output fuzzy level obtained by mapping the first and second fuzzy levels according to the fuzzy rule base, and is used to represent the fuzzy state of the proportional parameter, integral parameter, and differential parameter. The third matching membership degree is obtained by multiplying the corresponding first matching membership degree by the second matching membership degree, and is used to reflect the activation intensity of the output fuzzy level.

[0082] The fuzzy output value corresponding to the third fuzzy level is a preset numerical representation value corresponding to the fuzzy output level (i.e., the third fuzzy level) set for the target parameters such as proportional parameters, integral parameters, or differential parameters. This value is usually the center value of the membership function and is used to convert linguistic variables (such as "negative center", "positive small", "positive large", etc.) into specific numerical values ​​during the defuzzification process. For example, if the fuzzy output level is "positive small (PS)", its corresponding fuzzy output value can be set to +0.2; if it is "negative center (NM)", the corresponding fuzzy output value can be set to -0.4. This output value is multiplied by the third matching membership degree in the weighted average calculation to obtain the change in the target parameter.

[0083] For example, the terminal first fuzzifies the current pressure deviation and the rate of change of pressure deviation, obtaining multiple first and second fuzzy levels and their matching membership degrees. Then, based on a preset fuzzy rule table, and combining all combinations of the two fuzzy levels, it determines several third fuzzy levels for the proportional parameter, integral parameter, and differential parameter. The terminal then calculates the third matching membership degree corresponding to each rule as the activation degree of that rule. Subsequently, for each target parameter, the terminal performs a weighted average (centroid method) based on each activation degree and the corresponding membership function center value to achieve defuzzification, obtaining the specific changes in the proportional parameter, integral parameter, and differential parameter.

[0084] In a specific example, taking fuzzy inference with proportional parameters as an example, assume the current pressure deviation is 0.3, belonging to the fuzzy levels "Positive Small (PS)" and "Positive Medium (PM)," with corresponding first matching membership degrees of 0.7 and 0.3, respectively. Referring to Table 1 above, the pressure deviation change rate is -0.2, belonging to the fuzzy levels "Negative Small (NS)" and "Zero (Z)," with corresponding second matching membership degrees of 0.6 and 0.4, respectively. Then the fuzzy rule combination relationship is: "Positive Small - Negative Small" maps to the fuzzy output level of proportional parameters "Zero," with a matching membership degree of 0.7 * 0.6 = 0.42; "Positive Small - Zero" maps to "Positive Small," with a matching membership degree of 0.7 * 0.4 = 0.28; "Positive Medium - Negative Small" maps to "Positive Small," with a matching membership degree of 0.3 * 0.6 = 0.18; "Positive Medium - Zero" maps to "Positive Medium," with a matching membership degree of 0.3 * 0.4 = 0.12.

[0085] Then, by using the centroid method to defuzzify, the exact changes in the target parameters are calculated:

[0086]

[0087] Where Δu is the change in the target parameter u. Let be the third matching membership degree of the i-th third fuzzy level. This is the fuzzy output value corresponding to the i-th third fuzzy level.

[0088] In this embodiment, by performing multi-level fuzzy reasoning and weighted defuzzification on the pressure deviation and its rate of change, the adjustment amounts of proportional, integral, and derivative parameters are dynamically calculated, achieving real-time and precise adjustment of the control parameters. This effectively integrates transient pressure changes and trend information, improving the control system's response capability and suppression effect on complex nonlinear pressure fluctuations within the airbag. With the help of dynamically adjusted control parameters, the opening changes of the intake valve can be precisely adjusted, significantly reducing pressure overshoot and oscillation phenomena, improving the stability and sealing performance of the airbag structure, and enhancing the overall safety and operational reliability of the underwater compressed air energy storage system.

[0089] In an exemplary embodiment, obtaining the target parameter based on the change of any target parameter further includes: when the current adjustment is the first adjustment, weighting and superimposing the change of any target parameter with the preset initial value corresponding to any target parameter to obtain the target parameter of the current adjustment; when the current adjustment is not the first adjustment, weighting and superimposing the change of any target parameter with the target parameter determined in the previous adjustment to obtain the target parameter of the current adjustment.

[0090] In the case where the current adjustment is the first adjustment, the preset initial value refers to the proportional parameter, integral parameter or derivative parameter initially set by the system, which is used as the starting reference for the adjustment of the controller parameters. In the case where the current adjustment is not the first adjustment, the target parameter determined in the previous adjustment refers to the parameter value calculated and updated in the previous control cycle, which is used to ensure the continuity and stability of parameter adjustment.

[0091] For example, in the initial control cycle, the terminal first acquires the change in the corresponding target parameter and linearly superimposes it with the preset initial value of the target parameter according to a predetermined weighting ratio to form the target parameter for the current control cycle. This method ensures that the control parameter starts from a reasonable initial benchmark, avoiding excessive initial adjustment amplitude that could lead to system oscillation. In subsequent control cycles beyond the initial adjustment, the terminal, based on the target parameter value calculated in the previous control cycle, also performs a weighted linear superposition with the change calculated in the current cycle. This progressive update method makes the parameter adjustment process smoother, reduces the instability risk caused by sudden changes, and enables rapid response to changes in system state. The terminal can adjust the weighting coefficients according to system operation feedback and control objectives, flexibly balancing the stability and response speed of parameter adjustment. In addition, the terminal can also set threshold limits based on parameter change trends to prevent the adjusted parameters from exceeding a reasonable range, ensuring the safety and reliability of airbag pressure control.

[0092] In a specific example,

[0093]

[0094] in, , , These are the proportional parameter, integral parameter, and derivative parameter currently being adjusted. , , These are the preset initial values ​​for the proportional, integral, and derivative parameters, respectively, and the parameters determined in the previous adjustment. , , These represent the changes in the proportional parameter, integral parameter, and derivative parameter, respectively. , , These are the weighting coefficients corresponding to the proportional parameter, integral parameter, and derivative parameter, respectively.

[0095] In this embodiment, by weighted superposition of the target parameter changes and preset initial values ​​or previous adjustment parameters during the initial and subsequent adjustments, smooth updates and dynamic adaptive adjustments of the control parameters are achieved. This effectively avoids abrupt changes in control parameters and improves the stability and robustness of the system response. By precisely adjusting the proportional, integral, and derivative parameters, the internal pressure of the airbag and its changing trends can be reflected more accurately, enhancing the sensitivity and precision of the intake valve opening adjustment. Ultimately, this significantly suppresses instantaneous overshoot and fluctuations in the internal pressure of the airbag during the gas storage phase, improving the safety of the airbag structure and the overall operational reliability of the underwater compressed air energy storage system.

[0096] In an exemplary embodiment, step S104 above, which obtains the valve opening change based on the proportional parameter, integral parameter, and derivative parameter, further includes: calculating the valve opening change by using the proportional parameter, integral parameter, and derivative parameter as input parameters based on the proportional-integral-derivative control method.

[0097] For example, in each control cycle, the terminal inputs the proportional, integral, and derivative parameters obtained in the previous cycle to the PID (Proportional-Integral-Derivative) control calculation module. This module first calculates the proportional, integral, and derivative terms by multiplying the real-time pressure deviation, cumulative deviation, and deviation change rate by the corresponding parameters, and then sums them to obtain the valve opening change for the current control cycle. Based on the calculation results, the terminal controls the air intake valve at the bottom of the airbag, adjusting the valve opening in real time to ensure control sensitivity while suppressing pressure overshoot. The terminal can also set upper and lower limits for the valve opening change to avoid excessive adjustment interfering with system stability.

[0098] In this embodiment, a proportional-integral-derivative control method combined with real-time adjustment parameters is used to achieve precise dynamic control of the valve opening, improve system response performance, suppress instantaneous overpressure and periodic fluctuations caused by rapid gas injection during the gas storage process, and ensure the stability and safety of the underwater compressed air energy storage system.

[0099] In an exemplary embodiment, the above step S101, which involves real-time acquisition of the current internal air pressure data of the airbag, further includes: acquiring multiple pressure sensor detection data in real time through a plurality of pressure sensors; the pressure sensors include at least a sensor located at the top of the airbag, a sensor located at the equatorial plane of the airbag, and a sensor located at the bottom of the airbag; and obtaining the current internal air pressure data based on the multiple pressure sensor detection data.

[0100] The current internal air pressure data refers to the calculated results reflecting the overall pressure state inside the airbag, serving as the input basis for subsequent deviation judgment and control adjustment. The pressure sensor detection data refers to the instantaneous pressure values ​​at different spatial locations of the airbag collected by various distributed sensors; the top sensor mainly senses the maximum pressure at the gas accumulation point, the equatorial sensor reflects the circumferential pressure distribution in the middle, and the bottom sensor monitors instantaneous pressure disturbances near the air inlet. These three types of sensors together constitute a space pressure detection network.

[0101] For example, the terminal establishes a communication connection with multiple pressure sensors installed on the airbag structure through a data acquisition interface, and acquires the pressure data collected by the sensors in each control cycle. The top sensor is located at the highest point of the airbag to reflect the local high pressure after the gas accumulates; the equatorial sensor has multiple measuring points evenly distributed along the horizontal direction of the airbag, for example, four at 90° intervals, to collect the circumferential pressure in the middle; and the bottom sensor is installed near the air intake valve to detect pressure fluctuations at the beginning of air injection.

[0102] For example, after receiving various pressure sensor data, the terminal can select different processing strategies according to the specific application scenario. For instance, it can directly take the arithmetic mean, use a weighted average method (assigning different weights to the top, middle, and bottom), or use a maximum-minimum envelope strategy for data fusion. The weighting coefficients can be flexibly set according to the pressure distribution characteristics inside the airbag, the sensor distribution density, or historical data fluctuations to improve the representativeness and stability of the current internal air pressure data.

[0103] In a specific example, the current pressure deviation is ;in, , The internal air pressure data at the current time t is collected in real time by pressure sensors at the top, middle, and bottom of the airbag (the average value or weighted according to the position can be taken). To preset reference pressure data, it can be based on water density ρ, gravitational acceleration g, the depth of the airbag underwater h, and the atmospheric pressure of the sea area where the airbag is located. Sure.

[0104] The current pressure deviation change rate is:

[0105]

[0106] Where Δt is the sampling time interval, which is determined by the system's real-time requirements.

[0107] In this embodiment, by deploying multi-point pressure sensors and using a fusion calculation strategy, the internal pressure state of the airbag can be comprehensively reflected, significantly improving the accuracy and timeliness of the current internal pressure data, avoiding local distortion caused by single-point sampling, providing reliable data support for subsequent pressure deviation identification and air intake regulation control, and enhancing the overall control accuracy and stable operation capability of the system.

[0108] In another exemplary embodiment, such as Figure 3 As shown, an underwater compressed air energy storage device is provided that employs the above-mentioned method for suppressing pressure fluctuations during the airbag storage stage. The setup process for this device is as follows:

[0109] 1) Airbag placement:

[0110] The airbag is made of multi-layered composite flexible material, possessing high pressure resistance and low permeability. Its teardrop shape is reinforced with ribs to resist underwater pressure gradients. The airbag is secured to a pre-set base on the seabed by high-strength anchor chains. A titanium alloy flange is used at the connection between the anchor chain and the airbag to prevent stress concentration.

[0111] 2) Sensor arrangement:

[0112] The system should employ sensors with high precision, strong anti-interference capabilities, and high resolution. These sensors should be installed at the top, middle, and bottom of the airbag to ensure more accurate pressure data. Figure 4 As shown.

[0113] Top sensor: Located at the highest point of the airbag, it monitors the dynamic pressure in the gas accumulation area during inflation. The sensor is embedded in the inner wall of the airbag.

[0114] Central sensor: Four measuring points are evenly arranged along the equatorial plane of the airbag, with a spacing of 90°, to monitor the circumferential pressure distribution. The sensor is fixed to the outside of the reinforcing rib by clamps.

[0115] Bottom sensor: Installed near the valve at the bottom of the airbag, it monitors local pressure changes when air enters the airbag. The sensor is welded to the airbag base.

[0116] 3) Valve arrangement:

[0117] An adjustable pneumatic valve is used, with a valve body material that is resistant to seawater corrosion and offers high precision and rapid response. The valve is installed at the bottom of the air bladder, and the intake valve opening control signal is generated using a fuzzy PID control algorithm.

[0118] In this embodiment, a stable and responsive underwater compressed air energy storage device is constructed through the rational arrangement of the airbag, pressure sensor, and intake valve. The multi-layered composite flexible airbag, combined with reinforcing ribs, effectively enhances its pressure resistance and durability; multi-point distributed pressure sensors enable precise monitoring of the internal pressure of the airbag; and adjustable pneumatic valves, combined with a fuzzy PID (proportional-integral-derivative) control algorithm, improve the accuracy and real-time performance of intake regulation. Overall, this device improves the reliability and safety of pressure control during the air storage phase and enhances the system's stable operation in complex underwater environments.

[0119] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0120] Based on the same inventive concept, this application also provides an underwater compressed air energy storage airbag pressure fluctuation suppression device for implementing the above-mentioned method for suppressing pressure fluctuations during the airbag storage stage of underwater compressed air energy storage. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the underwater compressed air energy storage airbag pressure fluctuation suppression device provided below can be found in the limitations of the underwater compressed air energy storage airbag pressure fluctuation suppression method described above, and will not be repeated here.

[0121] In one exemplary embodiment, such as Figure 5 As shown, a pressure fluctuation suppression device for underwater compressed air energy storage during the air bladder storage stage is provided, comprising: a pressure acquisition module 501, a deviation determination module 502, a parameter acquisition module 503, and a valve adjustment module 504, wherein:

[0122] The air pressure acquisition module 501 is used to collect the current internal air pressure data of the airbag in real time during the air storage stage of the airbag for underwater compressed air energy storage.

[0123] The deviation determination module 502 is used to determine the current pressure deviation and the rate of change of the current pressure deviation of the airbag based on the current internal air pressure data and the preset reference pressure data.

[0124] The parameter acquisition module 503 is used to determine the proportional parameter, integral parameter, and derivative parameter of the valve opening change of the air intake valve used to adjust the airbag based on the current pressure deviation and the rate of change of the current pressure deviation.

[0125] The valve adjustment module 504 is used to obtain the valve opening change based on the proportional parameter, integral parameter and derivative parameter, and adjust the opening and closing of the air inlet valve of the airbag based on the valve opening change, so as to suppress the internal pressure fluctuation that occurs during the airbag storage stage of underwater compressed air energy storage.

[0126] In one embodiment, the parameter acquisition module 503 is further configured to determine, based on preset fuzzy level membership information, a plurality of first fuzzy levels corresponding to the current pressure deviation and a first matching membership degree corresponding to each first fuzzy level, and a plurality of second fuzzy levels corresponding to the current pressure deviation change rate and a second matching membership degree corresponding to each second fuzzy level; determine, based on each first fuzzy level and each second fuzzy level, a plurality of third fuzzy levels corresponding to each target parameter; the target parameter is a proportional parameter, an integral parameter, and a differential parameter; and calculate the target parameter based on each third fuzzy level, the first matching membership degree, and the second matching membership degree.

[0127] In one embodiment, the parameter acquisition module 503 is further configured to determine the third matching membership degree corresponding to each third fuzzy level based on the first matching membership degree and the second matching membership degree; for any target parameter, perform a weighted average calculation on the fuzzy output value corresponding to each third fuzzy level of the target parameter and the corresponding third matching membership degree to obtain the change amount of the target parameter; and obtain the target parameter based on the change amount of the target parameter.

[0128] In one embodiment, the parameter acquisition module 503 is further configured to, when the current adjustment is the first adjustment, weight and superimpose the change of any target parameter with the preset initial value corresponding to any target parameter to obtain the target parameter of the current adjustment; when the current adjustment is not the first adjustment, weight and superimpose the change of any target parameter with the target parameter determined in the previous adjustment to obtain the target parameter of the current adjustment.

[0129] In one embodiment, the valve regulating module 504 is further configured to calculate the valve opening change by using proportional, integral and derivative parameters as input parameters based on a proportional-integral-derivative control method.

[0130] In one embodiment, the pressure acquisition module 501 is further configured to acquire multiple pressure sensor detection data in real time through a plurality of pressure sensors; the pressure sensors include at least a sensor located at the top of the airbag, a sensor located at the equatorial plane of the airbag, and a sensor located at the bottom of the airbag; and obtain the current internal air pressure data based on the multiple pressure sensor detection data.

[0131] The various modules in the pressure fluctuation suppression device during the gas storage stage of the aforementioned underwater compressed air energy storage system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0132] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for suppressing pressure fluctuations during the gas storage stage of an underwater compressed air energy storage system. The display unit is used to generate a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0133] Those skilled in the art will understand that Figure 6The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0134] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0135] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0136] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0137] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0138] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0139] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0140] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for suppressing pressure fluctuation in the air storage stage of an underwater compressed air energy storage airbag, characterized by, The method comprises: In the air bag storage phase of the underwater compressed air energy storage, current internal air pressure data of the air bag is collected in real time; According to the current internal air pressure data and preset reference pressure data, a current pressure deviation and a current pressure deviation change rate of the air bag are determined; According to preset fuzzy level membership information, a plurality of first fuzzy levels corresponding to the current pressure deviation and first matching membership degrees corresponding to each first fuzzy level are determined, and a plurality of second fuzzy levels corresponding to the current pressure deviation change rate and second matching membership degrees corresponding to each second fuzzy level are determined; According to the first fuzzy levels and the second fuzzy levels, a plurality of third fuzzy levels corresponding to each target parameter are determined; the target parameters are proportional parameters, integral parameters and differential parameters; According to the first matching membership degrees and the second matching membership degrees, third matching membership degrees corresponding to each third fuzzy level are determined; For any target parameter, fuzzy output values corresponding to the third fuzzy levels of the any target parameter are weighted and averaged with the corresponding third matching membership degrees to obtain a change amount of the any target parameter, and the target parameter is obtained according to the change amount of the any target parameter; According to the proportional parameters, the integral parameters and the differential parameters, a valve opening change amount is obtained, and the opening and closing of the air inlet valve of the air bag is adjusted based on the valve opening change amount to suppress the internal pressure fluctuation of the underwater compressed air energy storage in the air bag storage phase.

2. The method of claim 1, wherein, The target parameter is obtained according to the change amount of the any target parameter, which comprises: In the case of current adjustment being the first adjustment, the change amount of the any target parameter is weighted and superimposed with a preset initial value corresponding to the any target parameter to obtain the target parameter of the current adjustment; In the case of current adjustment not being the first adjustment, the change amount of the any target parameter is weighted and superimposed with the target parameter determined in the last adjustment to obtain the target parameter of the current adjustment.

3. The method of claim 1, wherein, The valve opening change amount is obtained according to the proportional parameters, the integral parameters and the differential parameters, which comprises: The proportional parameters, the integral parameters and the differential parameters are taken as input parameters based on a proportional-integral-differential control mode to calculate the valve opening change amount.

4. The method according to any one of claims 1 to 3, characterized in that, The current internal air pressure data of the air bag is collected in real time, which comprises: A plurality of pressure sensor detection data are collected in real time by a plurality of pressure sensors; the pressure sensors at least include a sensor at the top of the air bag, a sensor at the equatorial plane of the air bag and a sensor at the bottom of the air bag; The current internal air pressure data is obtained according to the plurality of pressure sensor detection data.

5. An airbag pressure fluctuation suppression device for the air storage stage of an underwater compressed air energy storage system, characterized by, The device comprises: An air pressure acquisition module is configured to collect current internal air pressure data of an air bag in real time in an air bag storage phase of underwater compressed air energy storage; A deviation determination module is configured to determine a current pressure deviation and a current pressure deviation change rate of the air bag according to the current internal air pressure data and preset reference pressure data; The parameter acquisition module is configured to determine a plurality of first fuzzy levels corresponding to the current pressure deviation and a first matching membership degree corresponding to each of the first fuzzy levels according to preset fuzzy level membership information, and determine a plurality of second fuzzy levels corresponding to the current pressure deviation change rate and a second matching membership degree corresponding to each of the second fuzzy levels; determine a plurality of third fuzzy levels corresponding to each target parameter according to the first fuzzy levels and the second fuzzy levels; the target parameter is a proportional parameter, an integral parameter and a differential parameter; determine a third matching membership degree corresponding to each third fuzzy level according to the first matching membership degree and the second matching membership degree; for any target parameter, perform weighted average calculation on a fuzzy output value corresponding to each third fuzzy level of the any target parameter and the corresponding third matching membership degree to obtain a change amount of the any target parameter, and obtain the target parameter according to the change amount of the any target parameter. The valve adjustment module is configured to obtain a valve opening change amount according to the proportional parameter, the integral parameter and the differential parameter, and adjust opening and closing of an air inlet valve of the air bag based on the valve opening change amount to suppress internal pressure fluctuation of the underwater compressed air energy storage during an air bag gas storage stage.

6. The apparatus of claim 5, wherein, The parameter acquisition module is further configured to, in a case where the current adjustment is the first adjustment, perform weighted superposition on the change amount of the any target parameter and a preset initial value corresponding to the any target parameter to obtain the target parameter of the current adjustment. In a case where the current adjustment is not the first adjustment, perform weighted superposition on the change amount of the any target parameter and the target parameter determined in the last adjustment to obtain the target parameter of the current adjustment.

7. The apparatus of claim 5, wherein, The valve adjustment module is further configured to calculate the valve opening change amount based on a proportional-integral-differential control mode and taking the proportional parameter, the integral parameter and the differential parameter as input parameters.

8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4. The computer program, when executed by the processor, implements the steps of the method of any one of claims 1 to 4.

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