CN118582662A - A method and system for intelligent management of gas storage tanks based on air pressure monitoring - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C13/00—Details of vessels or of the filling or discharging of vessels
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- F17C13/025—Special adaptations of indicating, measuring, or monitoring equipment having the pressure as the parameter
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C13/00—Details of vessels or of the filling or discharging of vessels
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C13/00—Details of vessels or of the filling or discharging of vessels
- F17C13/04—Arrangement or mounting of valves
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C2223/00—Handled fluid before transfer, i.e. state of fluid when stored in the vessel or before transfer from the vessel
- F17C2223/01—Handled fluid before transfer, i.e. state of fluid when stored in the vessel or before transfer from the vessel characterised by the phase
- F17C2223/0107—Single phase
- F17C2223/0123—Single phase gaseous, e.g. CNG, GNC
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C2250/00—Accessories; Control means; Indicating, measuring or monitoring of parameters
- F17C2250/04—Indicating or measuring of parameters as input values
- F17C2250/0404—Parameters indicated or measured
- F17C2250/043—Pressure
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C2260/00—Purposes of gas storage and gas handling
- F17C2260/04—Reducing risks and environmental impact
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Abstract
Description
技术领域Technical Field
本发明涉及储气罐阀门自动控制技术领域，具体涉及一种基于气压监测的储气罐智能管理方法及系统。The present invention relates to the technical field of automatic control of gas tank valves, and in particular to an intelligent management method and system for gas tanks based on air pressure monitoring.
背景技术Background Art
储气罐作为工业生产和储存过程中的重要的储存设备，广泛应用于各种气体的储存和输送系统中。储气罐内的压力是储气罐安全的重要参数。储气罐的气压管理也是关系到工业生产和存储安全运行的重要手段，例如，在天然气输送过程中，储气罐内的气压必须保持在一定范围内，以确保输送管道的正常工作和防止气体泄漏。As an important storage equipment in the process of industrial production and storage, gas tanks are widely used in various gas storage and transportation systems. The pressure inside the gas tank is an important parameter for the safety of the gas tank. The gas pressure management of the gas tank is also an important means related to the safe operation of industrial production and storage. For example, during the natural gas transportation process, the gas pressure in the gas tank must be maintained within a certain range to ensure the normal operation of the transportation pipeline and prevent gas leakage.
随着工业自动化和智能化的发展，对储气罐气压监测和管理的要求也越来越高。传统的手动监测和调节方法已经无法满足现代工业的需求。为了提高系统的安全性、可靠性和效率，迫切需要一种能够实时监测储气罐内气压，并根据监测数据进行自动化管理的智能系统。With the development of industrial automation and intelligence, the requirements for gas tank pressure monitoring and management are becoming higher and higher. Traditional manual monitoring and adjustment methods can no longer meet the needs of modern industry. In order to improve the safety, reliability and efficiency of the system, there is an urgent need for an intelligent system that can monitor the gas pressure in the gas tank in real time and perform automatic management based on the monitoring data.
发明内容Summary of the invention
本发明的目的克服现有技术的不足，提供一种基于气压监测的储气罐智能管理方法及系统，通过预处理对采集的气压数据进行去噪，提高数据的准确性，基于监测的气压数据进行智能控制获得对阀门的实际控制量，实现对阀门的精确控制，确保储气罐系统的运行稳定。The purpose of the present invention is to overcome the shortcomings of the prior art and provide a method and system for intelligent management of gas tanks based on air pressure monitoring. The collected air pressure data is denoised through preprocessing to improve the accuracy of the data. Intelligent control is performed based on the monitored air pressure data to obtain the actual control amount of the valve, thereby achieving precise control of the valve and ensuring the stable operation of the gas tank system.
本发明的目的是通过以下技术措施达到的：一种基于气压监测的储气罐智能管理方法，包括以下步骤：The purpose of the present invention is achieved through the following technical measures: A method for intelligent management of gas storage tanks based on air pressure monitoring, comprising the following steps:
S1、预设气压采集周期T，实时采集周期T内储气罐的气压数据；S1, preset the air pressure collection period T, and collect the air pressure data of the air storage tank in real time within the period T;
S2、对采集到的气压数据进行预处理，获得去噪后的气压数据；S2, preprocessing the collected air pressure data to obtain denoised air pressure data;
S3、针对去噪后的气压数据，建立多维模糊逻辑控制算法模型，并利用算法模型获得储气罐阀门的实际控制指令，所述多维模糊逻辑控制算法模型包括多维模糊化过程、多维模糊推理过程和多维解模糊化过程，所述多维模糊化过程用于将气压数据以及气压数据的变化趋势数据转化为模糊集合，所述多维模糊推理过程用于实现基于模糊集合根据模糊规则库进行推理以生成中间控制指令，所述多维解模糊化过程用于将中间控制指令转换为实际控制指令；S3. For the denoised air pressure data, a multidimensional fuzzy logic control algorithm model is established, and the actual control instructions of the air tank valve are obtained by using the algorithm model. The multidimensional fuzzy logic control algorithm model includes a multidimensional fuzzification process, a multidimensional fuzzy reasoning process and a multidimensional defuzzification process. The multidimensional fuzzification process is used to convert the air pressure data and the change trend data of the air pressure data into a fuzzy set. The multidimensional fuzzy reasoning process is used to realize reasoning based on the fuzzy set according to the fuzzy rule library to generate intermediate control instructions. The multidimensional defuzzification process is used to convert the intermediate control instructions into actual control instructions.
S4、根据实际控制指令调节储气罐阀门，并重复S1。S4. Adjust the gas tank valve according to the actual control instruction and repeat S1.
进一步地，S2中采用多层动态自适应滤波算法模型对采集到的气压数据进行预处理，所述多层动态自适应滤波算法模型包括频域处理层、时间域处理层和自适应调节层，所述频域处理层用于将采集的气压数据的时域信号转换成频域信号以去除高频噪声，频域处理层处理后的频域信号经逆变换转换成时域信号后进入时间域处理层，所述时间域处理层用于对时域信号进行平滑处理以减少随机噪声，所述自适应调节层用于对经时间域处理层处理后的时域信号进行动态调整以获得气压数据。Furthermore, S2 uses a multi-layer dynamic adaptive filtering algorithm model to pre-process the collected air pressure data. The multi-layer dynamic adaptive filtering algorithm model includes a frequency domain processing layer, a time domain processing layer and an adaptive adjustment layer. The frequency domain processing layer is used to convert the time domain signal of the collected air pressure data into a frequency domain signal to remove high-frequency noise. The frequency domain signal processed by the frequency domain processing layer is converted into a time domain signal through inverse transformation and then enters the time domain processing layer. The time domain processing layer is used to smooth the time domain signal to reduce random noise. The adaptive adjustment layer is used to dynamically adjust the time domain signal processed by the time domain processing layer to obtain air pressure data.
进一步地，所述频域处理层采用复合波形变换算法模型将时域信号转换成频域信号，所述复合波形变换算法模型为，其中，为气压数据的频域信号，为频率变量，为时域信号，在时刻的起亚数据，是时域采样点，第k个时间点，为时间，为时域信号的总采样点数，是复数指数函数，为虚数单位，为波形函数，波形函数为高斯函数或小波函数，是积分变量，与相关的变量。Furthermore, the frequency domain processing layer uses a composite waveform transformation algorithm model to convert the time domain signal into a frequency domain signal. The composite waveform transformation algorithm model is ,in, is the frequency domain signal of the air pressure data, is the frequency variable, is the time domain signal, Kia data at the moment, is the time domain sampling point, the kth time point, For time, is the total number of sampling points of the time domain signal, is the complex exponential function, is an imaginary unit, is a waveform function, a waveform function is a Gaussian function or a wavelet function, is the integration variable, and Related variables.
进一步地，所述时间域处理层采用时变加权平滑算法模型对时域信号进行平滑处理，所述时变加权平滑算法模型为，其中，为平滑后的气压估计值，为时变加权系数，在时间t和窗口位置处的权重，满足，是平滑窗口长度，权重根据距离中心点的远近设定。Furthermore, the time domain processing layer uses a time-varying weighted smoothing algorithm model to smooth the time domain signal. The time-varying weighted smoothing algorithm model is ,in, is the smoothed estimated pressure value, is the time-varying weighting coefficient, at time t and window position The weight at which , is the length of the smoothing window, and the weight is set according to the distance from the center point.
进一步地，所述自适应调整层采用自适应噪声抑制算法模型对经时间域处理层处理后的时域信号进行动态调整，所述自适应噪声抑制算法模型为，其中，为自适应噪声抑制后的气压数据，为自适应权重，随着时间动态调整，自适应权重动态调整方式采用噪声方差与噪声方差和信号方差之和的比值。Furthermore, the adaptive adjustment layer uses an adaptive noise suppression algorithm model to dynamically adjust the time domain signal processed by the time domain processing layer. The adaptive noise suppression algorithm model is ,in, is the pressure data after adaptive noise suppression, The adaptive weights are dynamically adjusted over time, and the adaptive weight dynamic adjustment method uses the ratio of the noise variance to the sum of the noise variance and the signal variance.
进一步地，所述多维模糊化过程包括将气压数据以及气压数据的变化趋势数据分别划分出三个数据集合，并针对每个数据集合采用隶属函数模型将实际数据集合转换为模糊集合，所述隶属函数模型为，其中，为隶属度函数，为调节参数，为输入的气压数据或气压数据的变化趋势数据，为中心值，表示模糊集合的中间位置。Furthermore, the multidimensional fuzzification process includes dividing the air pressure data and the change trend data of the air pressure data into three data sets respectively, and using a membership function model for each data set to convert the actual data set into a fuzzy set. The membership function model is ,in, is the membership function, To adjust the parameters, The input air pressure data Or the changing trend data of air pressure data , is the center value, indicating the middle position of the fuzzy set.
进一步地，所述多维模糊推理过程包括建立气压数据和气压数据的变化趋势数据与储气罐阀门开度之间的模糊规则库，基于模糊规则库获得中间控制指令，，其中，为中间控制指令，为气压数据或气压数据的变化趋势数据基于隶属函数模型计算的隶属度函数的最小值，为基于模糊规则库的储气罐阀门控制动作的权重，表示气压数据的模糊集合，表示气压数据的变化趋势数据的模糊集合。Furthermore, the multi-dimensional fuzzy reasoning process includes establishing a fuzzy rule base between the air pressure data and the change trend data of the air pressure data and the opening of the gas tank valve, and obtaining intermediate control instructions based on the fuzzy rule base. ,in, is the intermediate control instruction, For air pressure data Or the changing trend data of air pressure data The minimum value of the membership function calculated based on the membership function model, is the weight of the gas tank valve control action based on the fuzzy rule base, represents the fuzzy set of air pressure data, A fuzzy set representing the changing trend data of air pressure data.
进一步地，所述多维解模糊化过程通过加权平均的方式将中间控制指令转换为实际控制指令，，其中，为实际控制量，为中间控制指令，为气压数据或气压数据的变化趋势数据基于隶属函数模型计算的隶属度函数的最小值，表示气压数据的模糊集合，表示气压数据的变化趋势数据的模糊集合。Furthermore, the multi-dimensional defuzzification process converts the intermediate control instructions into actual control instructions by weighted averaging. ,in, is the actual control quantity, is the intermediate control instruction, For air pressure data Or the changing trend data of air pressure data The minimum value of the membership function calculated based on the membership function model, represents the fuzzy set of air pressure data, A fuzzy set representing the changing trend data of air pressure data.
一种基于气压监测的储气罐智能管理系统，基于所述的基于气压监测的储气罐智能管理方法，包括气压采集模块、数据处理模块、智能控制模块和通信模块，所述气压采集模块用于采集气压数据并将气压数据传输至数据处理模块，所述数据处理模块用于对气压数据进行预处理，并将处理后的气压数据传输至智能控制模块和通信模块，所述智能控制模块用于计算储气罐的实际控制量，所述通信模块用于实现储气罐智能管理系统内各模块的通讯，并实现与远程监控平台通讯。A gas tank intelligent management system based on air pressure monitoring, based on the gas tank intelligent management method based on air pressure monitoring, includes an air pressure acquisition module, a data processing module, an intelligent control module and a communication module, the air pressure acquisition module is used to collect air pressure data and transmit the air pressure data to the data processing module, the data processing module is used to pre-process the air pressure data and transmit the processed air pressure data to the intelligent control module and the communication module, the intelligent control module is used to calculate the actual control amount of the gas tank, and the communication module is used to realize the communication of each module in the gas tank intelligent management system and realize communication with a remote monitoring platform.
进一步地，还包括报警模块，所述报警模块与数据处理模块连接，所述报警模块用于发出报警信息。Furthermore, it also includes an alarm module, which is connected to the data processing module and is used to issue an alarm message.
与现有技术相比，本发明的有益效果是：Compared with the prior art, the present invention has the following beneficial effects:
1、利用多层动态自适应滤波算法模型通过频域处理层、时间域处理层和自适应调整层对气压数据进行预处理，能够有效消除高频噪声和随机噪声；频域处理层使用复合波形变换算法模型将时域信号转换到频域，去除高频成分；时间域处理层使用时变加权平滑算法模型对信号进行平滑处理，减少随机噪声；自适应调整层使用自适应噪声抑制算法模型对数据进行动态调整，进一步提高数据的准确性。1. The multi-layer dynamic adaptive filtering algorithm model is used to pre-process the air pressure data through the frequency domain processing layer, time domain processing layer and adaptive adjustment layer, which can effectively eliminate high-frequency noise and random noise; the frequency domain processing layer uses a composite waveform transformation algorithm model to convert the time domain signal to the frequency domain to remove the high-frequency component; the time domain processing layer uses a time-varying weighted smoothing algorithm model to smooth the signal and reduce random noise; the adaptive adjustment layer uses an adaptive noise suppression algorithm model to dynamically adjust the data to further improve the accuracy of the data.
2、利用多维模糊逻辑控制算法模型对储气罐的进出气阀门进行自动调节；多维模糊逻辑控制算法模型包括多维模糊化过程、多维模糊推理过程和多维解模糊化过程；通过将自适应噪声抑制后的气压数据和气压数据的变化趋势数据转化为模糊集合，并根据模糊规则库进行推理，生成中间控制指令，再通过解模糊化过程得到具体的实际控制量，实现对阀门的精确控制，确保储气罐系统的运行稳定。2. Use a multidimensional fuzzy logic control algorithm model to automatically adjust the inlet and outlet valves of the gas tank; the multidimensional fuzzy logic control algorithm model includes a multidimensional fuzzification process, a multidimensional fuzzy reasoning process and a multidimensional defuzzification process; by converting the air pressure data after adaptive noise suppression and the change trend data of the air pressure data into a fuzzy set, and reasoning based on the fuzzy rule base to generate intermediate control instructions, and then obtaining the specific actual control quantity through the defuzzification process, the precise control of the valve is achieved to ensure the stable operation of the gas tank system.
3、当气压超出预设的安全范围时，系统的报警模块可通过声光报警、短信通知以及远程监控平台报警等多种方式，及时通知相关人员进行处理，确保系统的安全运行。3. When the air pressure exceeds the preset safety range, the system's alarm module can promptly notify relevant personnel to handle the situation through various means such as sound and light alarms, SMS notifications, and remote monitoring platform alarms to ensure the safe operation of the system.
下面结合附图和具体实施方式对本发明作详细说明。The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1是本发明的结构示意图。FIG. 1 is a schematic structural diagram of the present invention.
具体实施方式DETAILED DESCRIPTION
S1、预设气压采集周期T，实时采集周期T内储气罐的气压数据。在储气罐内安装压力传感器，用于实时采集气压数据，传感器能够在微秒级别内采集气压数据，确保数据的准确性和实时性。S1. Preset the air pressure collection period T, and collect the air pressure data of the air tank in real time within the period T. Install a pressure sensor in the air tank to collect air pressure data in real time. The sensor can collect air pressure data within microseconds to ensure the accuracy and real-time nature of the data.
S2、采用多层动态自适应滤波算法模型对采集到的气压数据进行预处理，所述多层动态自适应滤波算法模型包括频域处理层、时间域处理层和自适应调节层，所述频域处理层用于将采集的气压数据的时域信号转换成频域信号以去除高频噪声。S2. Preprocess the collected air pressure data using a multi-layer dynamic adaptive filtering algorithm model. The multi-layer dynamic adaptive filtering algorithm model includes a frequency domain processing layer, a time domain processing layer and an adaptive adjustment layer. The frequency domain processing layer is used to convert the time domain signal of the collected air pressure data into a frequency domain signal to remove high-frequency noise.
在频域处理层采用复合波形变换算法模型将时域信号转换成频域信号，所述复合波形变换算法模型为：In the frequency domain processing layer, a composite waveform transformation algorithm model is used to convert the time domain signal into a frequency domain signal. The composite waveform transformation algorithm model is:
， ,
其中，为气压数据的频域信号，为频率变量，为时域信号，在时刻的起亚数据，是时域采样点，第k个时间点，为时间，为时域信号的总采样点数，是复数指数函数，将时域信号转换到频域信号，为虚数单位，为波形函数，表示对时域信号进行平滑和滤波的函数，波形函数为高斯函数或小波函数，是积分变量，与相关的变量。通过设定频率阈值去除高频成分，减少噪声干扰。in, is the frequency domain signal of the air pressure data, is the frequency variable, is the time domain signal, Kia data at the moment, is the time domain sampling point, the kth time point, For time, is the total number of sampling points of the time domain signal, is a complex exponential function that converts the time domain signal to the frequency domain signal. is an imaginary unit, is a waveform function, which represents the function of smoothing and filtering the time domain signal. is a Gaussian function or a wavelet function, is the integration variable, and By setting the frequency threshold, high-frequency components can be removed to reduce noise interference.
在频域处理过程中，首先选择适当的波形函数，例如高斯函数或小波函数。利用数值积分和复数指数运算，将时域信号转换为频域信号。通过设定频率阈值，去除频域中的高频成分。高于阈值频率的信号成分将被设为零，得到滤波后的频域信号。In the frequency domain processing, first select the appropriate waveform function , such as Gaussian function or wavelet function. Use numerical integration and complex exponential operation to convert the time domain signal into a frequency domain signal. By setting a frequency threshold, the high-frequency components in the frequency domain are removed. Signal components above the threshold frequency will be set to zero, and the filtered frequency domain signal is obtained.
频域处理层处理后的频域信号经逆变换转换成时域信号后进入时间域处理层，使用逆变换将处理后的频域信号转换回时域信号，得到初步滤波的时域信号。逆变换公式为：The frequency domain signal processed by the frequency domain processing layer is converted into a time domain signal through inverse transformation and then enters the time domain processing layer. Convert back to the time domain signal to obtain the preliminary filtered time domain signal The inverse transformation formula is:
。 .
逆变换后，时域信号被传输至时间域处理层，通过时间域处理层对时域信号进行平滑处理以减少随机噪声。所述时变加权平滑算法模型为：After the inverse transformation, the time domain signal is transmitted to the time domain processing layer, and the time domain signal is smoothed by the time domain processing layer to reduce random noise. The time-varying weighted smoothing algorithm model is:
， ,
其中，为平滑后的气压估计值，为时变加权系数，在时间t和窗口位置处的权重，满足，是平滑窗口长度，决定加权平均的范围。权重根据距离中心点的远近设定，如采用高斯分布。通过时变加权平滑，实现对数据的平滑处理，减少随机噪声。in, is the smoothed estimated pressure value, is the time-varying weighting coefficient, at time t and window position The weight at which , It is the length of the smoothing window, which determines the range of weighted averaging. The weight is set according to the distance from the center point, such as using Gaussian distribution. Through time-varying weighted smoothing, data smoothing is achieved to reduce random noise.
所述自适应调节层采用自适应噪声抑制算法模型对经时间域处理层处理后的时域信号进行动态调整以获得气压数据。所述自适应噪声抑制算法模型为：The adaptive adjustment layer uses an adaptive noise suppression algorithm model to dynamically adjust the time domain signal processed by the time domain processing layer to obtain air pressure data. The adaptive noise suppression algorithm model is:
， ,
其中，为自适应噪声抑制后的气压数据，为自适应权重，随着时间动态调整，自适应权重动态调整方式采用噪声方差与噪声方差和信号方差之和的比值。in, is the pressure data after adaptive noise suppression, The adaptive weights are dynamically adjusted over time, and the adaptive weight dynamic adjustment method uses the ratio of the noise variance to the sum of the noise variance and the signal variance.
S3、针对去噪后的气压数据，建立多维模糊逻辑控制算法模型，并利用算法模型获得储气罐阀门的实际控制指令，所述多维模糊逻辑控制算法模型包括多维模糊化过程、多维模糊推理过程和多维解模糊化过程，所述多维模糊化过程用于将气压数据以及气压数据的变化趋势数据转化为模糊集合，所述多维模糊化过程包括将气压数据以及气压数据的变化趋势数据分别划分出三个数据集合，例如，将气压数据划分为：低（Low）、中（Medium）、高（High）三个数据集合，将气压数据的变化趋势数据划分为：下降（Decreasing）、平稳（Stable）、上升（Increasing）三个数据集合，例如，气压数据：低（Low）为0-30 kPa，中（Medium）为20-50 kPa，高（High）为40-70 kPa。类似地，定义变化趋势数据的模糊集合时，可以选择以下边界：下降（Decreasing）为-10- -2 kPa/s，稳定（Stable）为-3-3 kPa/s，上升（Increasing）为2-10 kPa/s。S3. For the denoised air pressure data, a multidimensional fuzzy logic control algorithm model is established, and the actual control instructions of the gas tank valve are obtained by using the algorithm model. The multidimensional fuzzy logic control algorithm model includes a multidimensional fuzzification process, a multidimensional fuzzy reasoning process and a multidimensional defuzzification process. The multidimensional fuzzification process is used to convert the air pressure data and the change trend data of the air pressure data into fuzzy sets. The multidimensional fuzzification process includes dividing the air pressure data and the change trend data of the air pressure data into three data sets, for example, dividing the air pressure data into three data sets: low (Low), medium (Medium), and high (High), and dividing the change trend data of the air pressure data into three data sets: decreasing (Decreasing), stable (Stable), and increasing (Increasing). For example, the air pressure data: low (Low) is 0-30 kPa, medium (Medium) is 20-50 kPa, and high (High) is 40-70 kPa. Similarly, when defining fuzzy sets for trend data, the following boundaries can be selected: Decreasing is -10- -2 kPa/s, Stable is -3-3 kPa/s, and Increasing is 2-10 kPa/s.
所述气压数据的变化数据即为时间t时采集的气压数据对应的变化率，具体的，可将气压数据绘制成气压曲线，时间t对应的曲线上的点的切线的斜率即可作为气压数据的变化趋势数据。并针对每个数据集合采用隶属函数模型将实际数据集合转换为模糊集合，所述隶属函数模型为：The change data of the air pressure data is the change rate corresponding to the air pressure data collected at time t. Specifically, the air pressure data can be plotted into an air pressure curve, and the slope of the tangent line of the point on the curve corresponding to time t can be used as the change trend data of the air pressure data. And for each data set, a membership function model is used to convert the actual data set into a fuzzy set, and the membership function model is:
， ,
其中，为隶属度函数，为调节参数，为输入的气压数据或气压数据的变化趋势数据，为中心值，表示模糊集合的中间位置。in, is the membership function, To adjust the parameters, The input air pressure data Or the changing trend data of air pressure data , is the center value, indicating the middle position of the fuzzy set.
使用隶属函数模型计算每个输入数据属于各个模糊集合的隶属度，对于气压数据和气压数据的变化趋势数据，其隶属度分别计算为：The membership function model is used to calculate the membership degree of each input data to each fuzzy set. For the pressure data and atmospheric pressure data trend data , and their membership degrees are calculated as:
， ,
， ,
其中，和分别为气压数据和气压数据的变化趋势数据的隶属度，和为调节参数，和为气压数据和气压数据的变化趋势数据的中心值。in, and are the membership of air pressure data and the change trend data of air pressure data, respectively. and To adjust the parameters, and It is the central value of the air pressure data and the changing trend data of the air pressure data.
调节参数和的确定方法如下：Adjustment parameters and The method of determining is as follows:
根据经验给定和的初始值，初始值可以选择较小的正数，例如0.1，初始值的选择不需要特别精确，但应足够小以避免初始误差过大。利用初始值计算初步的隶属函数，观察气压数据和气压数据变化趋势数据的模糊化效果。使用优化算法调整和的值，以最小化控制误差和系统响应时间。常用的优化算法包括遗传算法（GA）、粒子群优化算法（PSO）和梯度下降法。优化步骤如下：Given by experience and The initial value can be a small positive number, such as 0.1. The initial value does not need to be particularly accurate, but it should be small enough to avoid excessive initial error. Use the initial value to calculate the preliminary membership function and observe the pressure data. and atmospheric pressure data change trend data Use optimization algorithm to adjust and The value of is used to minimize the control error and system response time. Common optimization algorithms include genetic algorithm (GA), particle swarm optimization algorithm (PSO) and gradient descent method. The optimization steps are as follows:
定义目标函数：目标函数可以是控制误差的平方和或系统响应时间。例如：Define the objective function: The objective function can be the sum of squares of the control error or the system response time. For example:
， ,
其中，是目标函数，和为气压数据和气压数据的变化趋势数据的设定值，和为气压数据和气压数据的变化趋势数据的实际输出值，为权重因子。in, is the objective function, and It is the setting value of the air pressure data and the change trend data of the air pressure data. and The actual output value of the air pressure data and the change trend data of the air pressure data. is the weight factor.
选择适合的优化算法，例如遗传算法，初始化种群，设定交叉率和变异率，运行算法，直到目标函数收敛。根据优化结果调整和的值，重新计算隶属函数，验证模糊控制效果。Select a suitable optimization algorithm, such as a genetic algorithm, initialize the population, set the crossover rate and mutation rate, and run the algorithm until the objective function converges. Adjust according to the optimization results and , recalculate the membership function and verify the fuzzy control effect.
优化后的参数和需要在实际系统中进行验证，观察其对控制效果的影响。如果控制效果不理想，可以进行二次优化，进一步调整参数，例如，经过优化后，得到的调节参数：，。Optimized parameters and It is necessary to verify it in the actual system to observe its impact on the control effect. If the control effect is not ideal, you can perform secondary optimization and further adjust the parameters. For example, after optimization, the adjustment parameters obtained are: , .
所述多维模糊推理过程用于实现基于模糊集合根据模糊规则库进行推理以生成中间控制指令，所述多维模糊推理过程包括建立气压数据和气压数据的变化趋势数据与储气罐阀门开度之间的模糊规则库，例如，根据经验或实验数据建立模糊规则库：The multidimensional fuzzy reasoning process is used to realize reasoning based on fuzzy sets according to a fuzzy rule base to generate intermediate control instructions. The multidimensional fuzzy reasoning process includes establishing a fuzzy rule base between air pressure data and the change trend data of the air pressure data and the opening of the gas tank valve. For example, the fuzzy rule base is established based on experience or experimental data:
规则1：如果低且在下降，则阀门开大。当气压数据低且变化趋势在下降时，意味着气压进一步下降的可能性很大，因此需要快速增加气压，阀门应开大。Rule 1: If Low and When the air pressure data is low and the trend is decreasing, it means that there is a high possibility that the air pressure will drop further, so the air pressure needs to be increased quickly and the valve should be opened wide.
规则2：如果低且在平稳，则阀门开大。当气压数据低且变化趋势平稳时，虽然气压较低，但没有显著的下降趋势，因此阀门仍应开大以提升气压。Rule 2: If Low and When the air pressure data is low and the change trend is stable, although the air pressure is low, there is no significant downward trend, so the valve should still be opened to increase the air pressure.
规则3：如果低且在上升，则阀门开中。当气压数据低且变化趋势在上升时，气压虽然低但有上升趋势，因此阀门可以适当开中以平稳增加气压。Rule 3: If Low and When the air pressure data is low and the change trend is rising, the air pressure is low but has an upward trend, so the valve can be opened appropriately to increase the air pressure steadily.
规则4：如果中且在下降，则阀门开大。当气压数据中且变化趋势在下降时，气压在下降，需适当增加气压，阀门开大。Rule 4: If Zhongqie When the air pressure data is normal and the change trend is decreasing, the air pressure is decreasing, and the air pressure needs to be appropriately increased and the valve opened wider.
规则5：如果中且在平稳，则阀门开中。当气压数据中且变化趋势平稳时，保持当前气压即可，阀门开中。Rule 5: If Zhongqie When the air pressure data is in the middle and the change trend is stable, just maintain the current air pressure and the valve will be in the middle.
规则6：如果中且在上升，则阀门关小。当气压数据中且变化趋势在上升时，气压在上升，为防止过高，阀门应关小。Rule 6: If Zhongqie When the air pressure data is normal and the change trend is rising, the air pressure is rising. To prevent it from being too high, the valve should be closed.
规则7：如果高且在下降，则阀门开中。当气压数据高且变化趋势在下降时，气压高但有下降趋势，阀门开中以适当减少气压。Rule 7: If High and When the air pressure data is high and the change trend is decreasing, the valve is opened to appropriately reduce the air pressure.
规则8：如果高且在平稳，则阀门关小。当气压数据高且变化趋势平稳时，保持当前气压即可，阀门关小。Rule 8: If High and When the air pressure data is high and the change trend is stable, just maintain the current air pressure and close the valve.
规则9：如果高且在上升，则阀门关小。当气压数据高且变化趋势在上升时，气压高且继续上升，为防止超压，阀门应关小。Rule 9: If High and When the air pressure data is high and the trend is rising, the valve should be closed to prevent overpressure.
基于模糊规则库获得中间控制指令：Obtain intermediate control instructions based on the fuzzy rule base:
， ,
其中，为中间控制指令，为气压数据或气压数据的变化趋势数据基于隶属函数模型计算的隶属度函数的最小值，即，其中，为气压数据和气压数据的变化趋势数据的隶属度，表示气压数据的模糊集合，表示气压数据的变化趋势数据的模糊集合。为基于模糊规则库的储气罐阀门控制动作的权重，每条规则均对应一个控制动作，例如规则中的开大、开中、关小等，所述控制动作对应具体的控制量，基于具体的控制量设定控制动作的权重，例如：in, is the intermediate control instruction, For air pressure data Or the changing trend data of air pressure data The minimum value of the membership function calculated based on the membership function model, that is, ,in, is the membership degree of the air pressure data and the changing trend data of the air pressure data, represents the fuzzy set of air pressure data, A fuzzy set representing the changing trend data of air pressure data. is the weight of the control action of the gas tank valve based on the fuzzy rule base. Each rule corresponds to a control action, such as opening wide, opening medium, closing small, etc. The control action corresponds to a specific control amount. The weight of the control action is set based on the specific control amount. For example:
全开：；Fully open: ;
开大：；Open Large: ;
开中：；Open in: ;
关小：；Guan Xiao: ;
关闭：。closure: .
通过多维模糊规则库将输入的多维模糊集合映射到中间控制输出，实现多维模糊推理。The input multidimensional fuzzy set is mapped to the intermediate control output through the multidimensional fuzzy rule base to realize multidimensional fuzzy reasoning.
所述多维解模糊化过程通过加权平均的方式将中间控制指令转换为实际控制指令：The multi-dimensional defuzzification process converts the intermediate control instructions into actual control instructions by weighted averaging:
， ,
其中，为实际控制量，为中间控制指令，为气压数据或气压数据的变化趋势数据基于隶属函数模型计算的隶属度函数的最小值，表示气压数据的模糊集合，表示气压数据的变化趋势数据的模糊集合。in, is the actual control quantity, is the intermediate control instruction, For air pressure data Or the changing trend data of air pressure data The minimum value of the membership function calculated based on the membership function model, represents the fuzzy set of air pressure data, A fuzzy set representing the changing trend data of air pressure data.
通过多维加权平均的方式，将多维模糊控制指令转换为实际操作量，实现对阀门的精确控制。Through the multi-dimensional weighted average method, the multi-dimensional fuzzy control instructions are converted into actual operation quantities to achieve precise control of the valve.
S4、根据实际控制指令调节储气罐阀门，并重复S1。S4. Adjust the gas tank valve according to the actual control instruction and repeat S1.
如图1所示，一种基于气压监测的储气罐智能管理系统，基于所述的基于气压监测的储气罐智能管理方法，包括气压采集模块、数据处理模块、智能控制模块和通信模块，所述气压采集模块用于采集气压数据并将气压数据传输至数据处理模块，所述数据处理模块用于对气压数据进行预处理，并将处理后的气压数据传输至智能控制模块和通信模块，所述智能控制模块用于计算储气罐的实际控制量，所述通信模块用于实现储气罐智能管理系统内各模块的通讯，并实现与远程监控平台通讯。As shown in FIG1 , an intelligent management system for gas tanks based on air pressure monitoring, based on the intelligent management method for gas tanks based on air pressure monitoring, includes an air pressure acquisition module, a data processing module, an intelligent control module and a communication module. The air pressure acquisition module is used to collect air pressure data and transmit the air pressure data to the data processing module. The data processing module is used to pre-process the air pressure data and transmit the processed air pressure data to the intelligent control module and the communication module. The intelligent control module is used to calculate the actual control amount of the gas tank. The communication module is used to realize the communication of each module in the intelligent management system of the gas tank and realize the communication with the remote monitoring platform.
还包括报警模块，所述报警模块与数据处理模块连接，所述报警模块用于发出报警信息。当气压超出预设的安全范围时，触发报警；报警模块通过声光报警、短信通知以及远程监控平台报警等多种方式，及时通知相关人员进行处理。It also includes an alarm module, which is connected to the data processing module and is used to send out alarm information. When the air pressure exceeds the preset safety range, an alarm is triggered; the alarm module notifies relevant personnel in time for processing through various methods such as sound and light alarm, SMS notification, and remote monitoring platform alarm.
在本发明的描述中，需要理解的是，术语“上”、“中”、“外”、“内”等指示方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的组件或元件必须具有特定的方位，以特定的方位构造和操作，因此不能理解为对本发明的限制。In the description of the present invention, it should be understood that the terms "upper", "middle", "outer", "inner" and the like indicating directions or positional relationships are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the components or elements referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.
本行业的技术人员应该了解，本发明不受上述实施例的限制，上述实施例和说明书中描述的只是说明本发明的原理，在不脱离本发明精神和范围的前提下，本发明还会有各种变化和改进，这些变化和改进都落入要求保护的本发明范围内。本发明要求保护范围由所附的权利要求书及其等效物界定。Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.
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