A method and system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank

Abstract

The present application provides a method and system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank. Among them, a pressure sensor array is used to monitor the air pressure in the buffer tank in real time, preprocess the collected data, remove noise and enhance signal clarity, and generate an air pressure adjustment start signal when the air pressure fluctuation exceeds a threshold; according to the air pressure adjustment start signal, an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization are used to optimize the gas replenishment or discharge rate, and a preliminary air pressure adjustment strategy is generated; based on the preliminary air pressure adjustment strategy, a multi-objective genetic algorithm is used to automatically adjust the gas replenishment or discharge rate to generate a final air pressure adjustment plan; based on the final air pressure adjustment plan, the data of the pressure sensors in the buffer tank is analyzed to continuously optimize the air pressure adjustment strategy. The technical solution provided by the present application can achieve precise adjustment of air pressure.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of buffer tanks, and in particular to a method and system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank. Background Art

[0002] In the chemical, petroleum, and natural gas industries, buffer tanks are important storage and transmission equipment. The stability of the internal air pressure directly affects production safety, efficiency, and product quality. Especially under special working conditions such as high pressure, flammable and explosive, it is necessary to monitor and accurately control the air pressure in the tank in real time to prevent safety accidents and economic losses caused by air pressure fluctuations. Therefore, it is particularly important to develop a technical solution that can monitor, respond quickly, and accurately control air pressure fluctuations in real time.

[0003] At present, the common air pressure control method is mainly based on switch control with fixed thresholds. This method usually relies on a single or a small number of pressure sensors for monitoring, and uses simple logical judgment to decide whether to start gas replenishment or discharge operations. Although this method can meet basic air pressure control needs to a certain extent, it often exhibits problems such as slow response speed and low control accuracy when dealing with air pressure fluctuations under complex working conditions.

[0004] Traditional air pressure control methods have the following major defects: they are unable to capture small changes in air pressure in time, resulting in the inability to quickly suppress air pressure fluctuations; due to the lack of advanced signal processing technology and intelligent control algorithms, it is difficult to achieve precise regulation of air pressure, and over- or under-regulation is prone to occur; fixed thresholds and simple logical judgments cannot adapt to complex working conditions, especially when external conditions or gas components in the tank change, the control effect will be greatly reduced; in addition, these methods often ignore the optimization of system energy efficiency and equipment service life, resulting in energy waste and increased equipment loss. Summary of the invention

[0005] The embodiments of the present application provide a method and system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank, so as to solve the problem that it is difficult to achieve accurate regulation of air pressure in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for balancing gas pressure fluctuations and stabilizing pressure in a buffer tank, comprising:

[0007] Use a high-precision pressure sensor array to monitor the air pressure in the buffer tank in real time, pre-process the collected data, remove noise and enhance signal clarity, and generate an air pressure regulation start signal when it is detected that the air pressure fluctuation exceeds the preset threshold;

[0008] According to the air pressure regulation start signal, use the nonlinear control algorithm of adaptive neuro-fuzzy inference system and particle swarm optimization to dynamically adjust and optimize the gas replenishment or emission rate, ensure that the air pressure fluctuation is effectively cancelled out, and generate a preliminary air pressure regulation strategy;

[0009] When the air pressure fluctuation tends to be gentle and reaches the preset stable interval, based on the preliminary air pressure regulation strategy, use the multi-objective genetic algorithm to automatically adjust the gas replenishment or emission rate to the lowest level required to maintain stability, and at the same time consider the system energy efficiency ratio and the service life of the equipment to generate the final air pressure regulation plan;

[0010] Based on the final air pressure regulation plan, analyze the pressure sensor data at different positions of the buffer tank, combine the changes in external conditions and the gas components in the tank, continuously optimize the air pressure regulation strategy, ensure that the internal air pressure of the buffer tank is within the ideal range, and record the key parameters of each regulation to generate data support for performance evaluation and system improvement.

[0011] In a second aspect, an embodiment of the present application provides a system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank, including:

[0012] A processing module for using a high-precision pressure sensor array to real-time monitor the air pressure in the buffer tank, preprocess the collected data, remove noise and enhance signal clarity, and generate an air pressure regulation start signal when it detects that the air pressure fluctuation exceeds the preset threshold;

[0013] An adjustment module for using the nonlinear control algorithm of adaptive neuro-fuzzy inference system and particle swarm optimization according to the air pressure regulation start signal to dynamically adjust and optimize the gas replenishment or emission rate, ensure that the air pressure fluctuation is effectively cancelled out, and generate a preliminary air pressure regulation strategy;

[0014] An adjustment module, when the air pressure fluctuation tends to be gentle and reaches the preset stable interval, based on the preliminary air pressure regulation strategy, uses the multi-objective genetic algorithm to automatically adjust the gas replenishment or emission rate to the lowest level required to maintain stability, and at the same time consider the system energy efficiency ratio and the service life of the equipment to generate the final air pressure regulation plan;

[0015] An optimization module, based on the final air pressure regulation plan, analyzes the pressure sensor data at different positions of the buffer tank, combines the changes in external conditions and the gas components in the tank, continuously optimizes the air pressure regulation strategy, ensures that the internal air pressure of the buffer tank is within the ideal range, and records the key parameters of each regulation to generate data support for performance evaluation and system improvement.

[0016] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank as described in the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which when executed by a computer, implements a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank as described in the first aspect.

[0018] In the embodiments of the present application, a high-precision pressure sensor array is used to monitor the air pressure in the buffer tank in real time, preprocess the collected data to remove noise and enhance signal clarity, and generate an air pressure regulation start signal when it is detected that the air pressure fluctuation exceeds a preset threshold; according to the air pressure regulation start signal, an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization are used to dynamically adjust and optimize the gas replenishment or discharge rate to ensure that the air pressure fluctuation is effectively canceled and a preliminary air pressure regulation strategy is generated; when the air pressure fluctuation tends to be gentle and reaches a preset stable interval, based on the preliminary air pressure regulation strategy, a multi-objective genetic algorithm is used to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, and a final air pressure regulation plan is generated; based on the final air pressure regulation plan, the pressure sensor data at different positions of the buffer tank is analyzed, combined with the changes in external conditions and the gas composition in the tank, and the air pressure regulation strategy is continuously optimized to ensure that the internal air pressure of the buffer tank is within the ideal range, and the key parameters of each adjustment are recorded to generate data support for performance evaluation and system improvement; the method for balancing air pressure fluctuation and stabilizing pressure in the buffer tank provided by the present application has the following beneficial effects: by using a high-precision pressure sensor array to monitor air pressure changes in real time, it can quickly and accurately capture air pressure fluctuations, respond in a timely manner, and effectively avoid inaccurate air pressure control caused by response delay; by using a non-linear control algorithm combining an adaptive neuro-fuzzy inference system and particle swarm optimization, it not only improves the adaptability to air pressure fluctuations under complex working conditions, but also can automatically adjust the optimal control strategy under different environmental conditions, enhancing the robustness and flexibility of the system; by further optimizing the air pressure regulation process with a multi-objective genetic algorithm, on the basis of ensuring air pressure stability, the frequency and magnitude of gas replenishment or discharge are reduced as much as possible, energy consumption is reduced, the service life of the equipment is extended, and the energy efficiency ratio of the whole system is improved; continuously monitoring and optimizing the air pressure regulation strategy to ensure that even in the face of changes in external conditions or changes in the gas composition in the tank, the internal air pressure of the buffer tank can be maintained within the ideal range, which helps to maintain the long-term stability and reliability of the system; recording the key parameters in each adjustment process provides detailed data support for subsequent performance evaluation, is conducive to discovering potential problems and implementing targeted improvement measures, and continuously optimizing the system performance.

[0019] Using a non-linear control algorithm based on an adaptive neuro-fuzzy inference system and particle swarm optimization, analyze the air pressure regulation start signal, predict the future trend of air pressure changes, formulate a preliminary gas replenishment or emission rate adjustment plan, and continuously optimize the adjustment plan through the particle swarm optimization algorithm to generate the final gas replenishment or emission rate change curve, thereby generating a preliminary air pressure regulation strategy to ensure that air pressure fluctuations are effectively controlled; The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank provided by this application has the following beneficial effects: By combining the adaptive neuro-fuzzy inference system and the particle swarm optimization algorithm, it can quickly and accurately analyze air pressure fluctuations and generate an optimized regulation strategy, effectively improving the response speed and control accuracy of the system; Using historical air pressure data and prediction models, it can better predict the future trend of air pressure changes, formulate reasonable regulation plans, and enhance the adaptability and robustness of the system under complex working conditions; By continuously updating and optimizing the gas replenishment or emission rate change curve, while ensuring that air pressure fluctuations are effectively controlled, it minimizes energy consumption and equipment wear to the greatest extent, improves the energy efficiency ratio of the system and the service life of the equipment; The entire process realizes full automation from air pressure fluctuation detection to air pressure regulation strategy generation, ensuring that the system can respond to air pressure changes in real time, achieve intelligent management, and improve the overall performance and economic benefits.

[0020] When the air pressure fluctuations tend to be gentle and reach the preset stable interval, based on the preliminary air pressure regulation strategy, use the multi-objective genetic algorithm to automatically adjust the gas replenishment or emission rate to the lowest level required to maintain stability, while considering the energy efficiency ratio of the system and the service life of the equipment, and generate the final air pressure regulation plan; The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank provided by this application has the following beneficial effects: Optimize the gas replenishment or emission rate through the multi-objective genetic algorithm to ensure that while maintaining air pressure stability, the energy efficiency ratio of the system is maximized and energy waste is reduced; Considering the service life of the equipment comprehensively, optimize the gas replenishment or emission rate, reduce the frequent start and stop and excessive wear of the equipment, extend the service life of the equipment, and reduce the maintenance cost; The multi-objective genetic algorithm can find the optimal gas replenishment or emission rate combination to ensure that the internal air pressure of the buffer tank remains within the ideal range for a long time, improving the stability and reliability of the system; By comprehensively considering the energy efficiency ratio of the system, the service life of the equipment, and air pressure stability, generate the final air pressure regulation plan to optimize the overall performance of the system and improve production efficiency and economic benefits.

[0021] These aspects or other aspects of this application will be more clearly understood in the following description of the embodiments. Brief Description of the Drawings

[0022] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0023] Figure 1 It is a flowchart of a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank provided by an embodiment of the present application;

[0024] Figure 2 It is a schematic structural diagram of a system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank provided by an embodiment of the present application;

[0025] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present application. Detailed implementation manners

[0026] In order to enable those skilled in the art of the present technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application.

[0027] In some processes described in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The operation numbers such as 101 and 102 are only used to distinguish different operations, and the numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0029] Figure 1 A flowchart of a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank is provided for an embodiment of the present application. As Figure 1 shown, the method includes:

[0030] 101. Use a high-precision pressure sensor array to monitor the air pressure in the buffer tank in real time, preprocess the collected data, remove noise and enhance signal clarity, and generate an air pressure adjustment start signal when it is detected that the air pressure fluctuation exceeds a preset threshold;

[0031] Through a high-precision pressure sensor array deployed inside the buffer tank, the air pressure data inside the tank can be continuously collected; preprocess the collected data, mainly to remove noise (such as incorrect readings caused by external interference) and enhance signal clarity, so as to more accurately reflect the true air pressure state inside the tank; when the monitored air pressure fluctuation exceeds the pre-set safety threshold, the system will automatically generate an air pressure adjustment start signal, indicating that the system needs to take action to adjust the air pressure.

[0032] 102. According to the air pressure adjustment start signal, use an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization to dynamically adjust and optimize the gas replenishment or discharge rate, ensure that the air pressure fluctuation is effectively canceled, and generate a preliminary air pressure adjustment strategy;

[0033] This is an advanced control technology that combines the learning ability of neural networks and the fuzzy logic to handle uncertainties. It can generate a prediction of the future air pressure change trend based on the characteristics of the current air pressure fluctuation (such as amplitude, direction, and change rate), combined with historical data and prediction models; using this algorithm, the system can automatically search for the optimal gas replenishment or discharge rate change curve to cancel the air pressure fluctuation with the fastest speed and minimum energy consumption. This process will generate a preliminary air pressure adjustment strategy.

[0034] Optionally, the step 102 of using an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization according to the air pressure adjustment start signal to dynamically adjust and optimize the gas replenishment or discharge rate, ensure that the air pressure fluctuation is effectively canceled, and generate a preliminary air pressure adjustment strategy specifically includes the following steps:

[0035] According to the air pressure regulation start signal, using an adaptive neuro-fuzzy inference system, analyze and process the amplitude, direction, and change rate of the current air pressure fluctuation. Combine historical air pressure data and a prediction model to obtain a prediction result of the future air pressure change trend. Based on the prediction result, formulate a preliminary gas replenishment or emission rate adjustment plan to quickly respond to air pressure fluctuations. Based on the preliminary gas replenishment or emission rate adjustment plan, adopt a non-linear control algorithm based on particle swarm optimization. By simulating the group behavior in nature, automatically search for the optimal gas replenishment or emission rate change curve to ensure that air pressure fluctuations are effectively cancelled out, and generate optimization suggestions. Based on the optimization suggestions, continuously update and optimize the gas replenishment or emission rate change curve until the optimal solution is found, and generate an optimized gas replenishment or emission rate change curve. Based on the optimized gas replenishment or emission rate change curve, generate a preliminary air pressure regulation strategy to ensure that air pressure fluctuations are effectively controlled.

[0036] Suppose there is a buffer tank with a high-precision pressure sensor array installed inside. At a certain moment, the system detects that the air pressure fluctuation exceeds the preset threshold, triggering an air pressure regulation start signal; the system collects that the amplitude of the current air pressure fluctuation is 10 kPa, the direction is rising, and the change rate is 2 kPa / s; use ANFIS to analyze this data, combine the historical air pressure data and prediction model of the past month, and predict that the air pressure will continue to rise within the next 5 minutes, and the maximum fluctuation amplitude may reach 15 kPa; within the next 5 minutes, the air pressure will continue to rise, and the maximum fluctuation amplitude may reach 15 kPa; the system decides to gradually increase the gas emission rate within the next 5 minutes, from the current 10 m 3 / min to 15 m 3 / min to offset the rising trend of the air pressure; the gas emission rate increases from 10 m 3 / min to 15 m 3 / min; use the PSO algorithm to initialize the particle swarm, and each particle represents a possible gas emission rate change curve. Through multiple iterations, continuously update the position and velocity of the particles, and finally find the optimal gas emission rate change curve to effectively cancel out the air pressure fluctuation; after the optimization of the PSO algorithm, the system finds the optimal gas emission rate change curve, which is as follows: increase the emission rate from 10 m 3 / min to 12 m 3 / min within the first minute, increase to 14 m 3 / min within the second minute, and finally increase to 15 m 3 / min within the third minute; the system continuously updates the gas emission rate change curve according to the optimization suggestions to ensure that the air pressure fluctuation is effectively controlled; the gas emission rate increases from 10 m 3 / min to 12 m 3 / min, increasing to 14m within the second minute 3 / min, and finally increasing to 15m within the third minute 3 / min; Based on the optimized gas emission rate change curve, the system generates a preliminary air pressure regulation strategy to ensure that air pressure fluctuations are effectively controlled.

[0037] Through the above steps, the system can effectively respond to air pressure fluctuations and ensure that the air pressure in the buffer tank is stable within the ideal range.

[0038] Optionally, based on the preliminary gas replenishment or emission rate adjustment plan, a non - linear control algorithm based on particle swarm optimization is adopted. By simulating the group behavior in nature, it automatically searches for the optimal gas replenishment or emission rate change curve to ensure that air pressure fluctuations are effectively canceled out, and generates optimization suggestions, which specifically include the following steps:

[0039] Based on the preliminary gas replenishment or emission rate adjustment plan, determine the search range of the particle swarm optimization algorithm, covering all possible gas replenishment or emission rate change curves, and construct an optimized basic framework; Based on the optimized basic framework, initialize the particle swarm in the particle swarm optimization algorithm. Each particle represents a potential gas replenishment or emission rate change curve to generate an initial particle swarm; Based on the initial particle swarm, set a fitness function for each particle. The fitness function evaluates the quality of the particle according to the ability to cancel out air pressure fluctuations to generate a fitness evaluation criterion; Based on the fitness evaluation criterion, simulate the group behavior in nature, let the particle swarm move within the search range, and each particle adjusts its flight direction and speed according to its own historical optimal position and the global optimal position of the group, continuously updating the position and speed of the particle to generate the optimal gas replenishment or emission rate change curve; Based on the optimal gas replenishment or emission rate change curve, ensure that air pressure fluctuations are effectively canceled out and generate optimization suggestions.

[0040] Suppose there is a buffer tank with a high - precision pressure sensor array installed inside. At a certain moment, the system detects that the air pressure fluctuation exceeds the preset threshold, triggering an air pressure regulation start signal. According to the preliminary gas replenishment or emission rate adjustment plan, the system decides to use the particle swarm optimization (PSO) algorithm to optimize the gas emission rate; The gas emission rate increases from 10m 3 / min to 15m 3 / min; Determine the search range to be all possible gas emission rate change curves between 10m 3 / min and 15m 3 / min, and construct an optimized basic framework; The search range is all possible gas emission rate change curves between 10m 3 / min and 15m 3 / min; Initialize the particle swarm in the particle swarm optimization algorithm. Assume the initial particle swarm contains 100 particles, and each particle represents a possible gas emission rate change curve. For example, particle 1 may represent an increase from 10 m 3 / min to 11 m 3 / min in the first minute, an increase to 13 m 3 / min in the second minute, and an increase to 15 m 3 / min in the third minute; 100 particles, each representing a gas emission rate change curve; Set a fitness function for each particle, and the fitness function evaluates the quality of the particle according to the cancellation effect of the air pressure fluctuation. For example, the fitness function can be defined as:

[0041] f(x) = - amplitude of air pressure fluctuation + α · system energy efficiency ratio + β · equipment service life

[0042] where α and β are weight coefficients used to adjust the importance of each objective; the fitness evaluation criterion is the fitness value of each particle; Simulate the group behavior in nature and let the particle swarm move within the search range. Each particle adjusts its flight direction and speed according to its own historical best position and the global best position of the group. The specific update formula is as follows:

[0043]

[0044] where is the new velocity of the i-th particle in the d-th dimension, w(t) is the inertia weight that changes dynamically with the number of iterations, c1(t) and c2(t) are the acceleration constants that change dynamically with the number of iterations, r1 and r2 are two independent random numbers, usually drawn from the uniform distribution [0, 1], is the personal historical best position of the i-th particle in the d-th dimension, is the current position of the i-th particle in the d-th dimension, is the global best position in the d-th dimension; Optimal gas replenishment or emission rate change curve: After multiple rounds of iteration, the particle swarm optimization algorithm finds the optimal gas emission rate change curve. For example, the optimal gas emission rate change curve may be an increase from 10 m 3 / min to 12 m 3 / min in the first minute, an increase to 14 m 3 / min in the second minute, and finally an increase to 15 m 3 / min in the third minute; Based on the optimal gas replenishment or emission rate change curve, generate optimization suggestions to ensure that the air pressure fluctuation is effectively cancelled.

[0045] Through the above steps, the system can automatically search for and find the optimal gas replenishment or emission rate change curve, ensuring that the air pressure fluctuation is effectively controlled. This not only improves the response speed and accuracy of the system, but also optimizes the energy efficiency ratio of the system and the service life of the equipment.

[0046] This application takes into account that in the air pressure fluctuation balance and pressure stabilization system of the buffer tank, it is necessary to dynamically adjust the gas replenishment or emission rate to ensure that the air pressure fluctuation is effectively canceled. The particle swarm optimization (PSO) algorithm is widely used in such optimization problems due to its simplicity, high efficiency and easy implementation. PSO simulates the behavior of bird flocks or fish schools in nature and uses swarm intelligence to find the optimal solution.

[0047] Optionally, based on the preliminary gas replenishment or emission rate adjustment plan, a non-linear control algorithm based on particle swarm optimization is adopted. By simulating the group behavior in nature, it automatically searches for the optimal gas replenishment or emission rate change curve to ensure that the air pressure fluctuation is effectively canceled, including:

[0048] Initialize the particle swarm, define the initial position and velocity of each particle, and the position of each particle represents a set of gas replenishment or emission rate change curves;

[0049] Define the fitness function, which is used to evaluate the quality of the gas replenishment or emission rate change curve;

[0050] Update the velocity of the particle through the following calculation formula to obtain the new velocity of the particle:

[0051]

[0052] where, is the new velocity of the i-th particle in the d-th dimension; is the inertia weight that changes dynamically with the iteration number t, w max and w min are the maximum and minimum inertia weights respectively, T is the maximum number of iterations; t is the current iteration number; δ is a small perturbation coefficient used to introduce periodic fluctuations; is the current velocity of the i-th particle in the d-th dimension; c1(t) and c2(t) are the acceleration constants that change dynamically with the iteration number t, c 1,max 、c 1,min 、c 2,max and c 2,min are the maximum and minimum values of the acceleration constants respectively, ∈1 and ∈2 are small perturbation coefficients used to introduce logarithmically growing dynamic adjustments; r1 and r2 are two independent random numbers, usually drawn from the uniform distribution [0,1], used to increase the randomness of the search; is the personal historical optimal position of the i-th particle in the d-th dimension; is the current position of the i-th particle in the d-th dimension; is the global optimal position in the d-th dimension; is the historical optimal position in the d-th dimension, that is, the historical best position of all particles in this dimension; α and β are coefficients that control the influence of the additional term and are used to introduce the influence of the historical optimal position; and are the fitness values of the current particle position and the global optimal position respectively;

[0053] Update the position of the particle through the following calculation formula to obtain the new position of the particle:

[0054]

[0055] where, represents the new position of the i-th particle in the d-th dimension; represents the current position of the i-th particle in the d-th dimension; γ is a coefficient that controls the influence of the personal historical optimal position; λ is an exponent that controls the influence of the personal historical optimal position; represents the personal historical optimal position of the i-th particle in the d-th dimension; η is a coefficient that controls the influence of the historical optimal position; μ is an exponent that controls the influence of the historical optimal position; represents the historical optimal position in the d-th dimension; and are the fitness values of the current particle position and the personal historical optimal position respectively; and are the fitness values of the current particle position and the historical optimal position respectively; v is a coefficient that controls the influence of periodic fluctuations; rand() is a random number drawn from the uniform distribution [0, 1]; represents the average position of all particles in the d-th dimension;

[0056] Generate a new gas replenishment or emission rate change curve based on the new velocity and new position of each particle, and calculate the corresponding fitness value. Through the following calculation formula, calculate the fitness function of the particle:

[0057] f(x) = α·f stability (x) η +β·f cfficiency (x) θ +γ·f lifetime (x) φ +δ·log(1 + f stability (x)+f efficiency (x)+f lifetime (x))

[0058] Among them, f(x) represents the fitness value of particle x; f stability (x) is the evaluation index of air pressure stability; f cfficiency (x) is the evaluation index of system energy efficiency ratio; f lifetime (x) is the evaluation index of equipment service life; α, β, γ, and δ are the weight coefficients of air pressure stability, system energy efficiency ratio, equipment service life, and comprehensive item respectively; η, θ, and φ are the corresponding exponents used to adjust the importance of each objective; update the personal historical optimal position of each particle and the global optimal position Repeat updating the velocity and position of the particles until a predetermined number of iterations is reached or the convergence condition is satisfied to obtain the optimal gas replenishment or emission rate change curve.

[0059] Suppose there is a buffer tank that needs to adjust the air pressure from 100 kPa to 120 kPa within 5 minutes. The preliminary gas replenishment or emission rate adjustment plan is to increase from 10 m 3 / min to 15 m 3 / min. Use the PSO algorithm to optimize this process; the number of particles is 100; randomly generate 100 particles, and each particle represents a gas replenishment or emission rate change curve; randomly generate the velocities of 100 particles; α = 0.5, β = 0.3, γ = 0.2, δ = 0.1; η = 2, θ = 1.5, φ = 1; w max = 0.9, w min = 0.4; c 1,max = 2.5, c 1,min = 0.5; c 2,max = 2.5, c 2,min = 0.5; ∈1 = 0.01, ∈2 = 0.01; δ = 0.05; α = 0.5, β = 0.5; γ = 0.5, λ = 2; η = 0.5, μ = 2; v = 0.1; the maximum number of iterations T = 100; assume the initial position of the first particle Initial velocity Calculate the inertia weight Calculate the acceleration constant Calculate the acceleration constant Suppose r1 = 0.7, r2 = 0.8, Calculate the new velocity

[0060] Calculate the new position Calculate the fitness value Update the personal historical optimal position Update the global optimal position Continue to perform multiple rounds of iteration until the maximum iteration number of 100 is reached or the convergence condition is satisfied.

[0061] Through the above steps, the system can automatically search and find the optimal gas replenishment or emission rate change curve to ensure that the air pressure fluctuation is effectively controlled. This not only improves the response speed and accuracy of the system, but also optimizes the energy efficiency ratio of the system and the service life of the equipment.

[0062] Optionally, according to the air pressure regulation start signal in step 102, use an adaptive neuro-fuzzy inference system to analyze and process the amplitude, direction, and change rate of the current air pressure fluctuation, and combine historical air pressure data and a prediction model to obtain a prediction result of the future air pressure change trend, which specifically includes the following steps:

[0063] According to the air pressure regulation start signal, use an adaptive neuro-fuzzy inference system to perform real-time analysis and processing on the amplitude, direction, and change rate of the current air pressure fluctuation to generate air pressure fluctuation characteristic parameters; based on the air pressure fluctuation characteristic parameters, combine historical air pressure data to evaluate the trend and development direction of the current air pressure fluctuation to generate an air pressure fluctuation trend evaluation report; based on the air pressure fluctuation trend evaluation report, adjust the prediction model parameters to optimize the accuracy and reliability of the prediction model to generate an optimized prediction model; based on the optimized prediction model, predict the future air pressure change trend to ensure that it accurately reflects the future air pressure fluctuation situation to generate a preliminary prediction result of the future air pressure change trend; based on the preliminary prediction result of the future air pressure change trend, perform verification and correction to ensure the accuracy of the prediction to obtain a prediction result of the future air pressure change trend.

[0064] Suppose there is a buffer tank with a high-precision pressure sensor array installed inside. At a certain moment, the system detects that the air pressure fluctuation exceeds the preset threshold, triggering an air pressure regulation start signal; for the air pressure regulation start signal, the current amplitude of the air pressure fluctuation is 10 kPa, the direction is upward, and the change rate is 2 kPa / s; use ANFIS to perform real-time analysis and processing on these data to generate air pressure fluctuation characteristic parameters. For example, the amplitude is 10 kPa; the direction is upward; the change rate is 2 kPa / s; the air pressure fluctuation characteristic parameters, the historical air pressure data of the past month; combine the historical air pressure data to evaluate the current trend and development direction of the air pressure fluctuation, and generate an air pressure fluctuation trend evaluation report. For example, the report indicates that the current air pressure fluctuation trend is a continuous upward trend, and it is expected that the air pressure will continue to rise within the next 5 minutes, and the maximum fluctuation amplitude may reach 15 kPa; the air pressure fluctuation trend evaluation report; adjust the parameters of the prediction model according to the evaluation report to optimize the accuracy and reliability of the prediction model. For example, adjust the weight coefficient of the model to make it better fit the historical data and generate an optimized prediction model; input the optimized prediction model; based on the optimized prediction model, predict the future air pressure change trend and generate a preliminary prediction result of the future air pressure change trend. For example, the prediction result shows that the air pressure will continue to rise within the next 5 minutes, and the maximum fluctuation amplitude may reach 15 kPa; input the preliminary prediction result; verify and correct the preliminary prediction result to ensure the accuracy of the prediction. For example, by comparing the actual air pressure change situation with the prediction result, it is found that the prediction result is relatively accurate, and finally generate a prediction result of the future air pressure change trend, confirming that the air pressure will continue to rise within the next 5 minutes, and the maximum fluctuation amplitude may reach 15 kPa.

[0065] Through the above steps, the system can accurately predict the future air pressure change trend, provide a scientific basis for subsequent air pressure regulation, and ensure that the air pressure fluctuation is effectively controlled. This not only improves the response speed and accuracy of the system, but also optimizes the energy efficiency ratio of the system and the service life of the equipment.

[0066] 103. When the air pressure fluctuation tends to be gentle and reaches the preset stable interval, based on the preliminary air pressure regulation strategy, use a multi-objective genetic algorithm to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, and generate a final air pressure regulation plan;

[0067] After the air pressure fluctuation has become gentle and entered the preset stable range, in order to ensure long-term air pressure stability, and taking into account the energy efficiency of the system and the service life of the equipment, a multi-objective genetic algorithm is used to further optimize the gas replenishment or discharge rate. This process aims to find the best solution that can not only maintain air pressure stability but also save energy and reduce equipment wear; based on the preliminary air pressure regulation strategy, through the optimization of the multi-objective genetic algorithm, a set of air pressure regulation solutions that can ensure air pressure stability and take into account system efficiency and equipment life is finally generated.

[0068] Optionally, after the air pressure fluctuation becomes gentle and reaches the preset stable range in step 103, based on the preliminary air pressure regulation strategy, a multi-objective genetic algorithm is used to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, and a final air pressure regulation solution is generated, which specifically includes the following steps:

[0069] After the air pressure fluctuation becomes gentle and reaches the preset stable range, based on the preliminary air pressure regulation strategy, a preliminary setting of the current gas replenishment or discharge rate is made to ensure that the air pressure fluctuation is effectively controlled, and a preliminarily set gas replenishment or discharge rate is generated; based on the preliminarily set gas replenishment or discharge rate, the search space of the multi-objective genetic algorithm is defined, including all possible combinations of gas replenishment or discharge rates, and a search space for optimization is generated; based on the search space for optimization, a fitness function is set for the multi-objective genetic algorithm, and the fitness function comprehensively considers the requirements of system energy efficiency ratio, equipment service life, and air pressure stability, evaluates the quality of each gas replenishment or discharge rate combination, and generates a fitness evaluation criterion; based on the fitness evaluation criterion, the multi-objective genetic algorithm is run, and by simulating the process of natural selection and genetic variation, the optimal gas replenishment or discharge rate combination is automatically searched to ensure air pressure stability while maximizing the system energy efficiency ratio and extending the equipment service life, and a multi-objective optimization result is generated; based on the multi-objective optimization result, the final gas replenishment or discharge rate is determined to ensure that the internal air pressure of the buffer tank is maintained within the ideal range for a long time, while optimizing the system performance and equipment maintenance cost, and a final air pressure regulation solution is generated.

[0070] Suppose there is a buffer tank with a high-precision pressure sensor array installed inside. At a certain moment, the system detects that the air pressure fluctuation becomes gentle and reaches the preset stable range, triggering an air pressure regulation start signal; the preliminary air pressure regulation strategy is input; after the air pressure fluctuation becomes gentle and reaches the preset stable range, based on the preliminary air pressure regulation strategy, a preliminary setting of the current gas replenishment or discharge rate is made to ensure that the air pressure fluctuation is effectively controlled. For example, the preliminarily set gas discharge rate is 12m 3 / min; the preliminarily set gas replenishment or discharge rate (12m 3 / min); Define the search space of the multi-objective genetic algorithm based on the preliminarily set gas replenishment or emission rate, including all possible combinations of gas replenishment or emission rates. For example, the search space is all possible combinations of gas emission rates between 10 m 3 / min and 15 m 3 / min; Input the search space for optimization; Set the fitness function for the multi-objective genetic algorithm. The fitness function comprehensively considers the requirements of system energy efficiency ratio, equipment service life, and air pressure stability, and evaluates the quality of each gas replenishment or emission rate combination. For example, the fitness function can be defined as:

[0071] f(x) = α·f stability (x) + β·f efficiency (x) + γ·f lifetime (x)

[0072] where α, β, and γ are weight coefficients used to adjust the importance of each objective. Assume α = 0.5, β = 0.3, γ = 0.2; Input the fitness evaluation criteria; Based on the fitness evaluation criteria, run the multi-objective genetic algorithm, and automatically search for the optimal gas replenishment or emission rate combination by simulating the processes of natural selection and genetic variation. The specific steps include: Generate 100 initial chromosomes, each chromosome representing a possible gas emission rate combination; Calculate the fitness value of each chromosome; Select high-quality chromosomes as parents according to the fitness value; Perform crossover operations on the parent chromosomes to generate new offspring chromosomes; Perform mutation operations on the offspring chromosomes to increase the diversity of the population; Repeat the above steps until a predetermined number of iterations is reached or the convergence condition is met, and generate the result of multi-objective optimization; The result of multi-objective optimization; Based on the result of multi-objective optimization, determine the final gas replenishment or emission rate to ensure that the internal air pressure of the buffer tank is maintained within the ideal range for a long time, while optimizing the system performance and equipment maintenance cost. For example, the result of multi-objective optimization shows that the optimal gas emission rate is 13 m 3 / min, at this time the air pressure is stable, the system energy efficiency ratio is the highest, and the equipment service life is the longest.

[0073] Through the above steps, the system can automatically adjust the gas replenishment or emission rate, ensuring stable air pressure while maximizing the system energy efficiency ratio and extending the equipment service life. This not only improves the response speed and accuracy of the system but also optimizes the overall performance of the system and the maintenance cost of the equipment.

[0074] Optionally, based on the fitness evaluation criteria, run the multi-objective genetic algorithm, and automatically search for the optimal gas replenishment or emission rate combination by simulating the processes of natural selection and genetic variation, ensuring stable air pressure while maximizing the system energy efficiency ratio and extending the equipment service life, and generate the result of multi-objective optimization, which specifically includes the following steps:

[0075] Initialize the population of the multi - objective genetic algorithm based on the fitness evaluation criteria. Each individual represents a combination of gas replenishment or emission rates to generate the initial population.

[0076] Based on the initial population, evaluate the impact of each combination of gas replenishment or emission rates on the system energy efficiency ratio, equipment service life, and air pressure stability, and generate the fitness value of each individual.

[0077] Based on the fitness value of each individual, simulate the natural selection process, select high - quality individuals as parents, perform crossover operations to generate new offspring individuals, and at the same time introduce random mutation operations to increase the diversity of the population, generating a new generation of population.

[0078] Based on the new generation of population, repeatedly execute the natural selection, crossover, and mutation operations, continuously iterate the population until the termination conditions are met. The termination conditions include reaching a predetermined number of iterations or the fitness value no longer improving significantly, and generate the optimal solution during the iteration process.

[0079] Based on the optimal solution during the iteration process, determine the optimal combination of gas replenishment or emission rates, ensure stable air pressure while maximizing the system energy efficiency ratio and extending the equipment service life, and generate the result of multi - objective optimization.

[0080] This application considers that in the buffer tank air pressure fluctuation balance and pressure stability system, it is necessary to dynamically adjust the gas replenishment or emission rate to ensure that the air pressure fluctuation is effectively canceled. The multi - objective genetic algorithm (MOGA) is widely used in such optimization tasks due to its powerful global search ability and the ability to handle multi - objective optimization problems. MOGA can find the optimal solution in a complex and changing environment by simulating the processes of natural selection and genetic variation.

[0081] Optionally, based on the fitness evaluation criteria, run the multi - objective genetic algorithm, automatically search for the optimal combination of gas replenishment or emission rates by simulating the processes of natural selection and genetic variation, ensure stable air pressure while maximizing the system energy efficiency ratio and extending the equipment service life, and generate the result of multi - objective optimization, including:

[0082] Determine the objective function of multi - objective optimization. The objective function is used to search for the optimal combination of gas replenishment or emission rates. The objective function includes multiple objectives, where the multiple objectives include minimizing air pressure fluctuation, maximizing the system energy efficiency ratio, and extending the equipment service life.

[0083] Create an initial population, where each individual in the initial population represents a set of gas replenishment or emission rates.

[0084] Calculate the fitness value corresponding to each individual in the population according to the objective function, where the fitness value represents the degree to which an individual meets all the objectives;

[0085] Select individuals from the population as the parental individuals of the next generation through the selection probabilities of natural selection and genetic variation. The selection probability reflects the probability that an individual is selected as a parent. Among them, the selection probabilities of natural selection and genetic variation are calculated through the following calculation formula:

[0086]

[0087] where p i is the probability that the i-th individual is selected; f(x i ) is the fitness value of the i-th individual; is an exponent that changes dynamically with the iteration number t. λ max and λ min are the maximum and minimum exponents respectively, T is the maximum number of iterations, t is the current iteration number, ∈ is a small perturbation coefficient used to introduce periodic fluctuations; μ is a small perturbation coefficient used to introduce additional periodic fluctuations; λ max and λ min are the maximum and minimum exponents respectively, T is the maximum number of iterations; ∈ is a small perturbation coefficient used to introduce periodic fluctuations; μ is a small perturbation coefficient used to introduce additional periodic fluctuations;

[0088] Adopt a multi-point crossover strategy to generate the offspring individuals of the parental individuals. Among them, the multi-point crossover strategy is defined as:

[0089] Calculate the multi-point crossover of natural selection and genetic variation through the following calculation formula:

[0090] y1 = [x a (1), x a (2), …, x a (k1), x b (k1 + 1), …, x b (k2), x a (k2 + 1), …, x a (D)]

[0091] y2 = [x b (1), x b (2), …, x b (k1), x a (k1 + 1), …, x a (k2), x b (k2 + 1), …, x b (D)]

[0092] Among them, y1 and y2 are the generated offspring individuals; x a and x b are two parent individuals; k1, k2, …, k m are randomly selected crossover point positions; m is the number of crossover points; D is the dimension of the individual;

[0093] Apply the Gaussian mutation formula to perform mutation operation on the offspring individuals to obtain new individuals; among them, the Gaussian mutation formula includes:

[0094]

[0095] Among them, y′(k) is the value of the mutated individual on the k-th dimension; y(k) is the value of the original individual on the k-th dimension; σ is a fixed standard deviation used to control the amplitude of mutation; randn is a random number drawn from the standard normal distribution; δ is a small perturbation coefficient used to introduce additional periodic fluctuations; rand() is a random number drawn from the uniform distribution [0, 1];

[0096] Add the new individuals to the population and update the population according to the fitness values;

[0097] Judge whether the population meets the preset population diversity threshold. If so, output the optimal solution and use the optimal solution as the result of multi-objective optimization.

[0098] Suppose there is a buffer tank with a high-precision pressure sensor array installed inside. At a certain moment, the system detects that the air pressure fluctuation tends to be gentle and reaches the preset stable interval, triggering the air pressure regulation start signal.

[0099] Objective function:

[0100] f(x) = 0.5·f stability (x) + 0.3·f efficiency (x) + 0.2·f lifetime (x)

[0101] Generate 100 initial individuals, and each individual represents a set of gas replenishment or discharge rates. For example, the gas discharge rate of individual 1 is 10m 3 / min, the gas discharge rate of individual 2 is 11m 3 / min, and so on; evaluate the fitness value of each individual. For example, the fitness value of individual 1 is 0.8, the fitness value of individual 2 is 0.7, and so on; Parameter setting: λ max = 2; λ min = 1; ∈ = 0.05; μ = 0.05; T = 100; t = 1

[0102] Calculating the selection probability:

[0103]

[0104] Select parent individuals according to the selection probability. For example, select individuals 1 and 2 with higher fitness values as parents; randomly select two crossover points, for example, k1 = 3 and k2 = 6;

[0105] y1 = [x1(1), x1(2), x1(3), x2(4), x2(5), x2(6), x1(7), x1(8), x1(9), x1(10)]

[0106] y2 = [x2(1), x2(2), x2(3), x1(4), x1(5), x1(6), x2(7), x2(8), x2(9), x2(10)]

[0107] Parameter setting: σ = 0.1; δ = 0.05

[0108] y′1(3) = y1(3) + 0.1·randn·(1 + 0.05·sin(2π·rand() / 10))

[0109] y ′ y2(6) = y2(6) + 0.1·randn·(1 + 0.05·sin(2π·rand() / 10))

[0110] Add the mutated offspring individuals y′1 and y′2 to the population, and update the population according to the fitness value; determine whether the population meets the preset population diversity threshold. For example, if the population diversity threshold is 0.05 and the diversity of the current population meets the threshold, output the optimal solution.

[0111] Through the above steps, the system can automatically adjust the gas replenishment or emission rate, ensuring stable air pressure while maximizing the system energy efficiency ratio and extending the equipment service life. This not only improves the system response speed and accuracy but also optimizes the overall performance of the system and the maintenance cost of the equipment.

[0112] 104. Based on the final air pressure regulation scheme, analyze the pressure sensor data at different positions of the buffer tank, combine the external conditions and the changes in the gas components inside the tank, continuously optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank is within the ideal range, and record the key parameters of each adjustment to generate data support for performance evaluation and system improvement.

[0113] According to the final air pressure regulation plan, the system will continue to monitor the pressure changes at different positions in the buffer tank. Considering external environmental factors (such as temperature, humidity) and changes in the gas composition inside the tank, it will continuously adjust and optimize the air pressure regulation strategy to ensure that the air pressure inside the tank always remains within the ideal range. During each adjustment process, the system will record key parameters (such as air pressure values before and after adjustment, environmental conditions, gas composition changes, etc.). These data are very important for subsequent performance evaluation and continuous improvement of the system. They can be used to analyze the effectiveness of the air pressure regulation strategy, identify potential problem points, and put forward improvement suggestions.

[0114] Optionally, based on the final air pressure regulation plan in step 104, analyze the pressure sensor data at different positions in the buffer tank, combine with real-time external conditions such as environmental temperature and humidity and changes in the gas components inside the tank, continuously optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank remains within the ideal range for a long time, and record the key parameters of each adjustment to generate data support for performance evaluation and system improvement. The specific steps are as follows:

[0115] Based on the final air pressure regulation plan, use a high-precision pressure sensor array to monitor the pressure data at different positions in the buffer tank in real time, remove noise and enhance signal clarity to obtain real-time pressure data. Based on the real-time pressure data, combine with external conditions such as environmental temperature and humidity and changes in the gas components inside the tank, evaluate the effect of the current air pressure regulation strategy, and generate an air pressure regulation effect evaluation report. Based on the air pressure regulation effect evaluation report, continuously adjust and optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank remains within the ideal range for a long time, and generate an optimized air pressure regulation strategy. Based on the optimized air pressure regulation strategy, record key parameters during the adjustment process, including the time of air pressure regulation, air pressure values before and after adjustment, environmental conditions, gas component changes, etc., to generate a detailed adjustment log. Use the detailed adjustment log to conduct performance evaluation regularly, analyze the effectiveness of the air pressure regulation strategy, identify potential problems and improvement points, and generate data support for performance evaluation and system improvement.

[0116] Suppose there is an industrial buffer tank with a high-precision pressure sensor array installed inside to monitor the pressure changes at different positions. The system needs to continuously optimize the air pressure regulation strategy according to the final air pressure regulation plan to ensure that the air pressure inside the tank remains within the ideal range for a long time; the final air pressure regulation plan; use the high-precision pressure sensor array to monitor the pressure data at different positions of the buffer tank in real time. For example, the pressure value monitored by sensor 1 is 100 kPa, the pressure value monitored by sensor 2 is 102 kPa, and so on. Through signal processing technology, remove noise and enhance signal clarity to obtain real-time pressure data; real-time pressure data, ambient temperature 25 °C, humidity 50%, and the gas composition inside the tank is 80% nitrogen and 20% oxygen; combine the real-time pressure data, ambient temperature, humidity, and changes in the gas composition inside the tank to evaluate the effect of the current air pressure regulation strategy. For example, the evaluation report shows that the current air pressure fluctuation is within the range of ±1 kPa, the system energy efficiency ratio is 85%, and the service life of the equipment is 10 years; the air pressure regulation effect evaluation report; based on the air pressure regulation effect evaluation report, continuously adjust and optimize the air pressure regulation strategy. For example, according to the evaluation report, the system decides to appropriately increase the gas discharge rate when the ambient temperature is high to reduce air pressure fluctuations. Generate an optimized air pressure regulation strategy to ensure that the air pressure inside the buffer tank remains within the range of 100 kPa ± 1 kPa for a long time; the optimized air pressure regulation strategy; during each air pressure regulation process, record key parameters, including the time of air pressure regulation, the air pressure values before and after regulation, environmental conditions, changes in gas composition, etc. For example, record the time of an air pressure regulation as 14:30, the air pressure before regulation as 101 kPa, the air pressure after regulation as 100 kPa, the ambient temperature as 25 °C, the humidity as 50%, and the gas composition inside the tank as 80% nitrogen and 20% oxygen. Generate a detailed regulation log; input the detailed regulation log; use the detailed regulation log to conduct a performance evaluation regularly. For example, conduct a performance evaluation once a month, analyze the effectiveness of the air pressure regulation strategy, and identify potential problems and improvement points. Generate data support for performance evaluation and system improvement. For example, it is found that the air pressure fluctuates greatly under certain specific environments, and it is recommended to add more sensor points to improve the monitoring accuracy.

[0117] Through the above steps, the system can continuously optimize the air pressure regulation strategy, ensure that the air pressure inside the buffer tank remains within the ideal range for a long time, record key parameters at the same time, and generate data support for performance evaluation and system improvement. This not only improves the response speed and accuracy of the system, but also optimizes the overall performance of the system and the maintenance cost of the equipment.

[0118] Figure 2 The accompanying drawing 1 is a schematic structural diagram of a system for balancing air pressure fluctuations and stabilizing pressure in a buffer tank provided by an embodiment of the present application. As Figure 2 shown, the device includes:

[0119] A processing module 21 is configured to monitor the air pressure in the buffer tank in real time using a high-precision pressure sensor array, preprocess the collected data to remove noise and enhance signal clarity, and generate an air pressure regulation start signal when it detects that the air pressure fluctuation exceeds a preset threshold.

[0120] An adjustment module 22 is configured to, according to the air pressure regulation start signal, use a non-linear control algorithm based on an adaptive neuro-fuzzy inference system and particle swarm optimization to dynamically adjust and optimize the gas replenishment or discharge rate, ensure that the air pressure fluctuation is effectively cancelled, and generate a preliminary air pressure regulation strategy.

[0121] An adjustment module 23, after the air pressure fluctuation tends to be gentle and reaches a preset stable range, based on the preliminary air pressure regulation strategy, uses a multi-objective genetic algorithm to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, and generates a final air pressure regulation plan.

[0122] An optimization module 24, based on the final air pressure regulation plan, analyzes the data of the pressure sensors at different positions in the buffer tank, combines the external conditions and the changes in the gas components in the tank, continuously optimizes the air pressure regulation strategy, ensures that the internal air pressure of the buffer tank is within an ideal range, and records the key parameters of each adjustment to generate data support for performance evaluation and system improvement.

[0123] Figure 2 The described air pressure fluctuation balance and pressure stability system in a buffer tank can execute Figure 1 The described air pressure fluctuation balance and pressure stability method in a buffer tank in the illustrated embodiment, the implementation principle and technical effects will not be elaborated further. For the air pressure fluctuation balance and pressure stability system in a buffer tank in the above embodiment, the specific ways for each module and unit to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0124] In a possible design, Figure 2 The air pressure fluctuation balance and pressure stability system in a buffer tank in the illustrated embodiment can be implemented as a computing device, as Figure 3 shown, and this computing device can include a storage component 31 and a processing component 32;

[0125] The storage component 31 stores one or more computer instructions, and among them, the one or more computer instructions are called and executed by the processing component 32.

[0126] The processing component 32 is configured to: monitor the air pressure in the buffer tank in real time using a high-precision pressure sensor array, preprocess the collected data to remove noise and enhance signal clarity, and generate an air pressure regulation start signal when it detects that the air pressure fluctuation exceeds a preset threshold; according to the air pressure regulation start signal, use an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization to dynamically adjust and optimize the gas replenishment or discharge rate to ensure that the air pressure fluctuation is effectively canceled and generate a preliminary air pressure regulation strategy; when the air pressure fluctuation tends to be gentle and reaches a preset stable range, based on the preliminary air pressure regulation strategy, use a multi-objective genetic algorithm to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, to generate a final air pressure regulation plan; based on the final air pressure regulation plan, analyze the pressure sensor data at different positions of the buffer tank, combine external conditions and changes in the gas composition in the tank, continuously optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank is within an ideal range, and record the key parameters of each adjustment to generate data support for performance evaluation and system improvement.

[0127] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0128] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0129] Of course, the computing device may necessarily also include other components, such as input / output interfaces, display components, communication components, etc.

[0130] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0131] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0132] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server. The above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from a cloud computing platform.

[0133] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above-mentioned Figure 1 method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank shown in the embodiment.

[0134] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0135] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0136] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank, characterized in that, Including: Using a high-precision pressure sensor array to monitor the air pressure in the buffer tank in real time, preprocessing the collected data, removing noise and enhancing signal clarity, and generating an air pressure adjustment start signal when it is detected that the air pressure fluctuation exceeds a preset threshold; According to the air pressure adjustment start signal, using an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization, dynamically adjusting and optimizing the gas replenishment or discharge rate to ensure that the air pressure fluctuation is effectively cancelled out and generating a preliminary air pressure adjustment strategy; When the air pressure fluctuation tends to be gentle and reaches a preset stable range, based on the preliminary air pressure adjustment strategy, using a multi-objective genetic algorithm to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability, while considering the system energy efficiency ratio and the service life of the equipment, generating a final air pressure adjustment plan; Based on the final air pressure adjustment plan, analyzing the pressure sensor data at different positions of the buffer tank, combining external conditions and changes in the gas components inside the tank, continuously optimizing the air pressure adjustment strategy to ensure that the internal air pressure of the buffer tank is within an ideal range, and recording the key parameters of each adjustment to generate data support for performance evaluation and system improvement; Among them, the non-linear control algorithm optimized by particle swarm optimization automatically searches for the optimal gas replenishment or discharge rate change curve by simulating the group behavior in nature. The specific process includes: initializing the particle swarm, defining the initial position and velocity of each particle, and the position of each particle represents a set of gas replenishment or discharge rate change curves; Defining a fitness function, which is used to evaluate the quality of the gas replenishment or discharge rate change curve; Updating the velocity of the particle through the following calculation formula to obtain the new velocity of the particle: wherein, is the new velocity of the i-th particle in the d-th dimension; is the inertia weight that varies dynamically with the iteration number t, w max and w min are the maximum and minimum inertia weights respectively, T is the maximum number of iterations; t is the current iteration number; δ is a small perturbation coefficient used to introduce periodic fluctuations; is the current velocity of the i-th particle in the d-th dimension; c1(t) and c2(t) are acceleration constants that vary dynamically with the iteration number t; r1 and r2 are two independent random numbers drawn from the uniform distribution [0, 1] and used to increase the randomness of the search; is the personal historical best position of the i-th particle in the d-th dimension; is the current position of the i-th particle in the d-th dimension; is the global best position in the d-th dimension; is the historical best position in the d-th dimension, that is, the historical best position of all particles in this dimension; α and β are coefficients that control the influence of the additional term and are used to introduce the influence of the historical best position; and are the fitness values of the current particle position and the global best position respectively; Updating the position of the particle through the following calculation formula to obtain the new position of the particle: Among them, represents the new position of the i-th particle in the d-th dimension; represents the current position of the i-th particle in the d-th dimension; γ is a coefficient that controls the influence of the personal historical best position; λ is an exponent that controls the influence of the personal historical best position; represents the personal historical best position of the i-th particle in the d-th dimension; η is a coefficient that controls the influence of the historical best position; μ is an exponent that controls the influence of the historical best position; represents the historical best position in the d-th dimension; and represent the fitness values of the current particle position and the personal historical best position respectively; and represent the fitness values of the current particle position and the historical best position respectively; ν is a coefficient that controls the influence of periodic fluctuations; rand() is a random number drawn from the uniform distribution [0, 1]; represents the average position of all particles in the d-th dimension; Generating a new gas replenishment or discharge rate change curve based on the new velocity and new position of each particle, calculating the corresponding fitness value, and calculating the fitness function of the particle through the following calculation formula: f(x) = α·f stability (x) η + β·f cfficiency (x) θ + γ·f lifetime (x) φ + δ ·log(1 + f stability (x) + f efficiency (x) + f lifetime (x)) Among them, f(x) represents the fitness value of particle x; f stability (x) is an evaluation index of air pressure stability; f cfficiency (x) is an evaluation index of system energy efficiency ratio; f lifetime (x) is an evaluation index of equipment service life; α, β, γ, and δ are the weight coefficients of air pressure stability, system energy efficiency ratio, equipment service life, and comprehensive item respectively; η, θ, and φ are the corresponding exponents used to adjust the importance of each objective; Update the personal historical optimal position of each particle and the global optimal position Repeating the update of the particle velocity and position until a predetermined number of iterations is reached or the convergence condition is satisfied to obtain the optimal gas replenishment or discharge rate change curve.

2. The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank according to claim 1, characterized in that, According to the air pressure adjustment start signal, using an adaptive neuro-fuzzy inference system and a non-linear control algorithm optimized by particle swarm optimization, dynamically adjusting and optimizing the gas replenishment or discharge rate to ensure that the air pressure fluctuation is effectively cancelled out and generating a preliminary air pressure adjustment strategy, which specifically includes the following steps: According to the air pressure adjustment start signal, using an adaptive neuro-fuzzy inference system to analyze and process the amplitude, direction and change rate of the current air pressure fluctuation, and combining historical air pressure data and a prediction model to obtain a prediction result of the future air pressure change trend; Based on the prediction result, formulating a preliminary gas replenishment or discharge rate adjustment plan to quickly respond to the air pressure fluctuation; Based on the preliminary gas replenishment or discharge rate adjustment plan, using a non-linear control algorithm optimized by particle swarm optimization to automatically search for the optimal gas replenishment or discharge rate change curve by simulating the group behavior in nature, ensuring that the air pressure fluctuation is effectively cancelled out and generating an optimization suggestion; Based on the optimization suggestions, continuously update and optimize the change curve of the gas replenishment or emission rate until the optimal solution is found, and generate the optimized change curve of the gas replenishment or emission rate; Based on the optimized change curve of the gas replenishment or emission rate, generate a preliminary air pressure regulation strategy to ensure that the air pressure fluctuation is effectively controlled.

3. The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank according to claim 2, wherein Based on the preliminary gas replenishment or emission rate adjustment plan, adopt a non-linear control algorithm based on particle swarm optimization. By simulating the group behavior in nature, automatically search for the optimal change curve of the gas replenishment or emission rate to ensure that the air pressure fluctuation is effectively cancelled out, and generate optimization suggestions, which specifically include the following steps: Based on the preliminary gas replenishment or emission rate adjustment plan, determine the search range of the particle swarm optimization algorithm, covering all possible change curves of the gas replenishment or emission rate, and construct an optimized basic framework; Based on the optimized basic framework, initialize the particle swarm in the particle swarm optimization algorithm. Each particle represents a potential change curve of the gas replenishment or emission rate, and generate an initial particle swarm; Based on the initial particle swarm, set a fitness function for each particle. The fitness function evaluates the quality of the particle according to the ability to cancel out the air pressure fluctuation, and generate a fitness evaluation criterion; Based on the fitness evaluation criterion, simulate the group behavior in nature, let the particle swarm move within the search range, and each particle adjusts its flight direction and speed according to its own historical optimal position and the global optimal position of the group, continuously update the position and speed of the particle, and generate the optimal change curve of the gas replenishment or emission rate; Based on the optimal change curve of the gas replenishment or emission rate, ensure that the air pressure fluctuation is effectively cancelled out, and generate optimization suggestions.

4. The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank according to claim 2, characterized in that, According to the air pressure regulation start signal, use an adaptive neuro-fuzzy inference system to analyze and process the amplitude, direction and change rate of the current air pressure fluctuation, and combine the historical air pressure data and the prediction model to obtain the prediction result of the future air pressure change trend, which specifically includes the following steps: According to the air pressure regulation start signal, use an adaptive neuro-fuzzy inference system to perform real-time analysis and processing on the amplitude, direction and change rate of the current air pressure fluctuation, and generate air pressure fluctuation characteristic parameters; Based on the air pressure fluctuation characteristic parameters, combine the historical air pressure data to evaluate the trend and development direction of the current air pressure fluctuation, and generate an air pressure fluctuation trend evaluation report; Based on the air pressure fluctuation trend evaluation report, adjust the prediction model parameters to optimize the accuracy and reliability of the prediction model, and generate an optimized prediction model; Based on the optimized prediction model, predict the future air pressure change trend to ensure that it accurately reflects the future air pressure fluctuation situation, and generate a preliminary prediction result of the future air pressure change trend; Based on the verification and correction of the preliminary prediction result of the future air pressure change trend, ensure the prediction accuracy, and obtain the prediction result of the future air pressure change trend.

5. The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank according to claim 1, wherein After the air pressure fluctuation tends to be gentle and reaches the preset stable range, based on the preliminary air pressure regulation strategy, the multi-objective genetic algorithm is used to automatically adjust the gas replenishment or discharge rate to the lowest level required to maintain stability. At the same time, considering the system energy efficiency ratio and the service life of the equipment, a final air pressure regulation plan is generated, which specifically includes the following steps: After the air pressure fluctuation tends to be gentle and reaches the preset stable range, based on the preliminary air pressure regulation strategy, the current gas replenishment or discharge rate is preliminarily set to ensure that the air pressure fluctuation is effectively controlled, and a preliminarily set gas replenishment or discharge rate is generated; Based on the preliminarily set gas replenishment or discharge rate, the search space of the multi-objective genetic algorithm is defined, including all possible gas replenishment or discharge rate combinations, and a search space for optimization is generated; Based on the search space for optimization, a fitness function is set for the multi-objective genetic algorithm. The fitness function comprehensively considers the requirements of system energy efficiency ratio, equipment service life, and air pressure stability, evaluates the quality of each gas replenishment or discharge rate combination, and generates a fitness evaluation criterion; Based on the fitness evaluation criterion, the multi-objective genetic algorithm is run. By simulating the process of natural selection and genetic variation, the optimal gas replenishment or discharge rate combination is automatically searched to ensure air pressure stability while maximizing the system energy efficiency ratio and extending the equipment service life, and a multi-objective optimization result is generated; Based on the multi-objective optimization result, the final gas replenishment or discharge rate is determined to ensure that the internal air pressure of the buffer tank is maintained within the ideal range for a long time, while optimizing the system performance and equipment maintenance cost, and a final air pressure regulation plan is generated.

6. The method for balancing air pressure fluctuations and stabilizing pressure in the buffer tank according to claim 1, characterized in that, Based on the final air pressure regulation plan, analyze the pressure sensor data at different positions of the buffer tank, combine the real-time external conditions such as environmental temperature and humidity and the changes in the gas components in the tank, continuously optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank is maintained within the ideal range for a long time, and record the key parameters of each adjustment to generate data support for performance evaluation and system improvement, which specifically includes the following steps: Based on the final air pressure regulation plan, use a high-precision pressure sensor array to monitor the pressure data at different positions of the buffer tank in real time, remove noise and enhance signal clarity, and obtain real-time pressure data; Based on the real-time pressure data, combined with external conditions such as environmental temperature and humidity and the changes in the gas components in the tank, evaluate the effect of the current air pressure regulation strategy, and generate an air pressure regulation effect evaluation report; Based on the air pressure regulation effect evaluation report, continuously adjust and optimize the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank is maintained within the ideal range for a long time, and generate an optimized air pressure regulation strategy; Based on the optimized air pressure regulation strategy, record the key parameters during the adjustment process, including the time of air pressure regulation, the air pressure values before and after adjustment, environmental conditions, gas component changes, etc., and generate a detailed adjustment log; Use the detailed adjustment log to conduct performance evaluation regularly, analyze the effectiveness of the air pressure regulation strategy, identify potential problems and improvement points, and generate data support for performance evaluation and system improvement.

7. A gas pressure fluctuation balance and pressure stabilization system in a buffer tank, characterized in that, Including: A processing module for real-time monitoring of the air pressure in the buffer tank using a high-precision pressure sensor array, preprocessing the collected data to remove noise and enhance signal clarity, and generating an air pressure regulation start signal when it detects that the air pressure fluctuation exceeds a preset threshold; An adjustment module for dynamically adjusting and optimizing the gas replenishment or discharge rate according to the air pressure regulation start signal, using a non-linear control algorithm based on an adaptive neuro-fuzzy inference system and particle swarm optimization to ensure that the air pressure fluctuation is effectively cancelled out, and generating a preliminary air pressure regulation strategy; An adjustment module, after the air pressure fluctuation tends to be gentle and reaches a preset stable range, automatically adjusts the gas replenishment or discharge rate to the lowest level required to maintain stability based on the preliminary air pressure regulation strategy, while considering the system energy efficiency ratio and the service life of the equipment, and generating a final air pressure regulation plan; An optimization module, based on the final air pressure regulation plan, analyzes the data of the pressure sensors at different positions in the buffer tank, combines the changes in external conditions and the gas components in the tank, continuously optimizes the air pressure regulation strategy to ensure that the internal air pressure of the buffer tank is within an ideal range, records the key parameters of each adjustment, and generates data support for performance evaluation and system improvement; Among them, the non-linear control algorithm based on particle swarm optimization automatically searches for the optimal gas replenishment or discharge rate change curve by simulating the group behavior in nature. The specific process includes: Initializing the particle swarm, defining the initial position and velocity of each particle, and the position of each particle represents a set of gas replenishment or discharge rate change curves; Defining a fitness function, which is used to evaluate the quality of the gas replenishment or discharge rate change curve; Updating the velocity of the particle through the following calculation formula to obtain the new velocity of the particle: Among them, is the new velocity of the i-th particle in the d-th dimension; is the inertia weight that changes dynamically with the iteration number t, w max and w min are the maximum and minimum inertia weights respectively, T is the maximum number of iterations; t is the current iteration number; δ is a small perturbation coefficient used to introduce periodic fluctuations; is the current velocity of the i-th particle in the d-th dimension; c1(t) and c2(t) are acceleration constants that change dynamically with the iteration number t; r1 and r2 are two independent random numbers drawn from the uniform distribution [0, 1] to increase the randomness of the search; is the personal historical best position of the i-th particle in the d-th dimension; is the current position of the i-th particle in the d-th dimension; is the global best position in the d-th dimension; is the historical best position in the d-th dimension, that is, the historical best position of all particles in this dimension; α and β are coefficients that control the influence of the additional term and are used to introduce the influence of the historical best position; and are the fitness values of the current particle position and the global best position respectively; Updating the position of the particle through the following calculation formula to obtain the new position of the particle: Among them, represents the new position of the \(i\)-th particle in the \(d\)-th dimension; represents the current position of the \(i\)-th particle in the \(d\)-th dimension; \(\gamma\) is a coefficient controlling the influence of the personal historical best position; \(\lambda\) is an exponent controlling the influence of the personal historical best position; represents the personal historical best position of the \(i\)-th particle in the \(d\)-th dimension; \(\eta\) is a coefficient controlling the influence of the historical best position; \(\mu\) is an exponent controlling the influence of the historical best position; represents the historical best position in the \(d\)-th dimension; and represent the fitness values of the current particle position and the personal historical best position respectively; and represent the fitness values of the current particle position and the historical best position respectively; \(\nu\) is a coefficient controlling the influence of periodic fluctuations; rand() is a random number drawn from the uniform distribution \([0, 1]\); represents the average position of all particles in the \(d\)-th dimension; Generating a new gas replenishment or discharge rate change curve based on the new velocity and new position of each particle, calculating the corresponding fitness value, and calculating the fitness function of the particle through the following calculation formula: f(x) = α·f stability (x) η + β·f cfficiency (x) θ + γ·f lifetime (x) φ + δ ·log(1 + f stability (x) + f efficiency (x) + f lifetime (x)) Among them, f(x) represents the fitness value of particle x; f stability (x) is an evaluation index of air pressure stability; f cfficiency (x) is an evaluation index of the system energy efficiency ratio; f lifetime (x) is an evaluation index of the equipment service life; α, β, γ, and δ are the weight coefficients of air pressure stability, system energy efficiency ratio, equipment service life, and the comprehensive term, respectively; η, θ, and φ are the corresponding exponents used to adjust the importance of each objective; Update the personal historical optimal position of each particle and the global optimal position Repeating the update of the velocity and position of the particle until a predetermined number of iterations is reached or the convergence condition is met, to obtain the optimal gas replenishment or discharge rate change curve.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by the computer, it implements a method for balancing air pressure fluctuations and stabilizing pressure in a buffer tank as described in any one of claims 1 to 6.

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