Volume correction instrument error analysis method and system based on improved PE equation

Abstract

The invention discloses a volume correction instrument error analysis method and system based on an improved PE equation, and aims to improve the accuracy of fuel gas and biogas metering. According to the equipment, performance testing can be carried out under the conditions of different temperatures (20 DEG C to 40 DEG C) and pressures (101.3 kPa to 104.0 kPa) through an integrated fuzzy logic algorithm, and the average correction error is only 0.22%. A high-precision sensor and a data processing unit are used for monitoring and calibrating the gas volume in real time and compensating the influence of temperature and pressure changes. The fuzzy logic controller adjusts the reading of the gas meter according to real-time data, the accuracy of a metering result is ensured, algorithm optimization and intelligent equipment integration are focused, and the energy management efficiency is improved. The volume correction instrument provides help for a modern energy metering system and optimization of energy efficiency and cost.

Description

Technical Field

[0001] The invention relates to the technical field of measurement error analysis and calibration, and in particular to a volume corrector error analysis method and system based on an improved PE equation. Background Art

[0002] The volume corrector is a high-precision metering device, which is mainly used to measure the volume of gas or liquid and correct it according to temperature, pressure and humidity conditions. This technical field involves multiple disciplines such as metrology, sensor technology, data processing and automatic control. The volume corrector is widely used in natural gas metering, petrochemical industry, environmental monitoring and other industries, and is crucial to ensure the accuracy and fairness of energy trading. The national standard "Gas Flow Meter Volume Corrector" (GB / T 36242-2018) specifies the terms and definitions, classification and measurement principles, technical requirements, test methods, inspection rules, etc. of the gas flow meter volume corrector, and is applicable to gas flow meter correctors with temperature (T) conversion, temperature-pressure (PT) conversion, humidity or temperature-pressure compressibility factor (PTZ) conversion functions.

[0003] In the prior art, the means of optimizing and accurately determining data using optimization algorithms such as fuzzy logic algorithms are becoming increasingly mature. As a target to be measured with an unstable shape, it may be difficult to accurately determine the volume data of a gas for error analysis or resource allocation using conventional measurement methods alone. Therefore, through a series of methods, the precise optimization of the algorithm combined with volume correction instrument measurement and error analysis may further eliminate influencing factors and accurately determine the volume and error coefficient of the target to be measured, which is a direction worthy of research. Summary of the invention

[0004] Purpose of the invention: To solve the problems mentioned in the background technology, the present invention provides a volume correction instrument error analysis method and system based on the improved PE equation. By establishing fuzzy sets and constructing a fuzzy rule base, the volume correction coefficient is output and denoised using a fuzzy logic algorithm, the volume correction model based on the PE equation is improved and error evaluation is performed, so that the target volume to be measured can be further refined, providing data support and assistance for subsequent measurement research and resource allocation.

[0005] Technical solution:

[0006] The present invention discloses a volume corrector error analysis method based on an improved PE equation, the method comprising the following steps:

[0007] S1 obtains environmental parameter variables related to the target to be measured;

[0008] S2 defines the input fuzzy set based on the environmental variables, initializes the input environmental parameter variables and establishes the membership function;

[0009] S3 builds a fuzzy rule base based on fuzzy sets and membership functions;

[0010] S4 performs fuzzy reasoning on the membership function through the Mamdani method, obtains the output fuzzy set and performs membership aggregation, defuzzifies the fuzzy set aggregation using the centroid method, and obtains the volume detection correction coefficient of the target to be measured;

[0011] S5 replaces the physical quantities in the improved PE equation with the output of the membership function, optimizes and combines the volume detection correction coefficient, establishes a volume correction model, outputs the final value of the correction model and performs error analysis with the original volume data.

[0012] Furthermore, the environmental parameter variables are initialized into five groups, each group including temperature, pressure and humidity.

[0013] Furthermore, the fuzzy sets and membership functions of the input variables are defined according to the initialization of the five sets of input environmental parameter variables:

[0014] The fuzzy sets are as follows:

[0015] Temperature T: {L low, M medium, H high}

[0016] Pressure P: {Low L, Medium M, High H}

[0017] Humidity H: {Low L, Medium M, High H}

[0018] The corresponding membership functions include:

[0019] Membership function for temperature:

[0020] Low:

[0021]

[0022] Among them, T<T medium That is, when the temperature value is lower than the middle threshold, the Low membership function is effective;

[0023] Medium:

[0024]

[0025] Among them, T medium <T<T high When the temperature value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0026] High:

[0027]

[0028] Among them, T>Tmediurn When the temperature is higher than the middle threshold, the High membership function is valid;

[0029] T low is the minimum threshold of temperature, below which the membership function μL(T) applies;

[0030] T medium is the middle threshold of temperature, between low and high temperatures;

[0031] T high is the maximum threshold of temperature, above which the membership function μH(T) applies;

[0032] Membership function of pressure:

[0033] Assume that the pressure P of the volume corrector has a measurement range of 95 kPa to 105 kPa. This range is divided into three fuzzy sets: low, medium, and high. The membership function is simplified into a triangle, that is, when each membership function reaches a maximum value at a certain point and gradually decreases to zero on both sides:

[0034] Low:

[0035]

[0036] Among them, when P<P medium When the pressure value is lower than the middle threshold, the Low membership function is effective;

[0037] Medium:

[0038]

[0039] Among them, when P low ≤P≤P high When the pressure value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0040] High:

[0041]

[0042] Among them, when P>P medium When the pressure value is higher than the middle threshold, the High membership function is valid;

[0043] P low is the minimum threshold of humidity, below which the membership function μL(P) is applicable;

[0044] P medium It is the middle threshold of humidity, between low and high humidity;

[0045] P high is the maximum threshold of humidity, above which the membership function μH(P) applies;

[0046] Humidity membership function:

[0047] Low:

[0048]

[0049] Among them, H<H medium That is, when the humidity value is lower than the middle threshold, the Low membership function is valid;

[0050] Medium:

[0051]

[0052] Among them, H medium <H<H high That is, when the humidity value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0053] High:

[0054]

[0055] Among them, H>H medium That is, when the humidity value is higher than the middle threshold, the High membership function is valid;

[0056] H low is the minimum threshold of humidity, below which the membership function μL(H) is applicable;

[0057] H medium It is the middle threshold of humidity, between low and high humidity;

[0058] H high is the maximum threshold of humidity, above which the membership function μH(H) applies.

[0059] Furthermore, the fuzzy rule base is constructed as follows:

[0060] When the temperature T is low, the pressure P is low, and the humidity H is low, the corrected volume will decrease;

[0061] When the temperature T is high, the pressure P is medium, and the humidity H is low, the corrected volume remains unchanged;

[0062] When the temperature T is low, the pressure P is high, and the humidity H is medium, the corrected volume will decrease;

[0063] When the temperature T is medium, the pressure P is low, and the humidity H is high, the corrected volume remains unchanged;

[0064] When the temperature T is high, the pressure P is high, and the humidity H is high, the corrected volume will increase.

[0065] Furthermore, the detailed process of the Mamdani method described in S4 for fuzzy reasoning of the membership function is as follows:

[0066] For each fuzzy rule in the fuzzy rule base, the activation degree of the rule is evaluated according to the membership of the input fuzzy set. The activation degree of the rule is evaluated using the minimum operation of the Mamdani method. The membership of the output fuzzy set is determined based on the membership of the input fuzzy set and the activation degree of the rule. The membership generated by the output fuzzy set is aggregated using the maximum operation of the Mamdani method. For each output variable, we take the maximum membership value among all rule outputs and finally obtain the aggregated fuzzy set.

[0067] Furthermore, the fuzzy set aggregation is defuzzified using the centroid method:

[0068] Determine the fuzzy set: Determine the output fuzzy set of the modified volume and its membership degree. The set has three elements, corresponding to reducing D, keeping N unchanged, and increasing I.

[0069] Define output range: define the value range for each fuzzy set;

[0070] The centroid calculation formula is as follows:

[0071]

[0072] Among them, x i is the output fuzzy set definition value, μ i is the corresponding degree of membership; the centroid is used to verify or explain the calculation results of the correction coefficient U;

[0073] The calculation of the correction factor U involves the following formula:

[0074]

[0075] Where ci is the volume corrector parameter output by the i-th rule.

[0076] Furthermore, the process of improving the PE equation and establishing the volume correction model described in S5 is as follows:

[0077] By converting the actual physical quantity of the original Peukert equation into the membership value in fuzzy logic, calculating the corrected volume, dealing with uncertainty and fuzziness, and further accurately correcting the volume measurement value, the volume correction original equation is established:

[0078]

[0079] where μ V (x) is the membership function of the output variable;

[0080] The relationship equation between the corrected volume and the original volume is:

[0081] V corrected =V×U

[0082] Wherein, U is the volume detection correction coefficient of the target to be measured;

[0083] Combining the original volume correction equation and the correction coefficient equation, the physical quantities in the improved PE equation are replaced by the output of the membership function, and the final volume correction model is as follows:

[0084]

[0085] The error comparison equation before and after volume correction is established to perform error analysis. The equation is as follows:

[0086]

[0087] Among them, V corrected is the corrected volume, V is the measured volume, the subscript corrected indicates the corrected value, and P act为 Actual pressure value, P L ,P M ,P H is the reference value corresponding to the low, medium and high fuzzy sets of pressure, T act is the actual temperature value, T L ,T M ,T H is the reference value corresponding to the low, medium and high fuzzy sets of temperature, H act is the actual humidity value, H L ,H M ,H H is the reference value corresponding to the low, medium and high fuzzy sets of humidity, P ref is the reference pressure value, T re is the reference temperature, H ref is the reference humidity value, γ is the pressure-related correction index, and α is the temperature-related correction index.

[0088] Furthermore, the present invention also discloses a volume corrector error optimization system based on the improved PE equation, which can execute any step of the volume corrector error analysis method based on the improved PE equation, and the system includes a gas flow meter module, an input module, a temperature sensor module, a pressure sensor module, a humidity sensor module, a communication interface module, a display module, a feedback module, and a processing unit;

[0089] The input module is connected with the gas flow meter module, the temperature sensor module, the pressure sensor module and the humidity sensor module to receive flow pulse signals, temperature data, pressure data and humidity data respectively;

[0090] The processing unit is used to receive the data obtained by the input module and obtain the volume detection correction coefficient and the corrected volume data through the built-in volume detection correction coefficient acquisition method;

[0091] The output module is used to receive the volume detection correction data, fuzzy set and membership function data acquired by the processing unit and output them in the form of pulse signals, and input them into the feedback module;

[0092] The feedback module is used to integrate the output module data and perform volume error analysis and optimization self-calibration, and transmit the adjusted commands to the adjustment module;

[0093] The adjustment module is used to input commands to the actuator for execution;

[0094] The correction element is connected to the processing unit and is used to communicate the corrected pressure value with the processing unit;

[0095] The display unit is connected to the output module and is used for displaying the corrected volume data.

[0096] Beneficial effects:

[0097] 1. The design of the present invention takes into account the influence of various environmental factors, constructs fuzzy sets and fuzzy rule bases based on environmental factors, and then corrects the errors caused by volume detection. Therefore, it can meet the volume detection needs in various environments and effectively improve the environmental practicality of the system of the present invention.

[0098] 2. The present invention can monitor and calibrate the gas volume in real time, and timely compensate for the influence of temperature, pressure and humidity changes by optimizing the correction coefficient U, thereby ensuring the real-time and accuracy of the measurement results.

[0099] 3. The present invention realizes intelligent control and generates error analysis reports of volume detection through the establishment of a modular system, reduces errors caused by human contact operations, and improves the credibility of results. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 This is a schematic diagram of the operation of the system modules of the present invention;

[0101] Figure 2 It is a schematic flow chart of the error analysis method of the volume corrector of the present invention. DETAILED DESCRIPTION

[0102] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0103] like Figure 1 and Figure 2 As shown, this embodiment discloses a volume corrector error analysis method based on an improved PE equation, comprising the following steps:

[0104] S1 obtains environmental parameter variables related to the target to be measured. The environmental parameter variables are initialized into five groups, each group including temperature, pressure and humidity;

[0105] S2 defines the input fuzzy set based on the environmental variables, initializes the input environmental parameter variables and establishes the membership function;

[0106] The fuzzy sets are as follows:

[0107] Temperature T: {L low, M medium, H high}

[0108] Pressure P: {Low L, Medium M, High H}

[0109] Humidity H: {Low L, Medium M, High H}

[0110] The corresponding membership functions include:

[0111] Membership function for temperature:

[0112] Low:

[0113]

[0114] Among them, T<T medium That is, when the temperature value is lower than the middle threshold, the Low membership function is effective;

[0115] Medium:

[0116]

[0117] Among them, T mediun <T<T high When the temperature value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0118] High:

[0119]

[0120] Among them, T>T medium When the temperature is higher than the middle threshold, the High membership function is valid;

[0121] T low is the minimum threshold of temperature, below which the membership function μL(T) applies;

[0122] T medium is the middle threshold of temperature, between low and high temperatures;

[0123] T high is the maximum threshold of temperature, above which the membership function μH(T) applies;

[0124] Membership function of pressure:

[0125] Assume that the pressure P of the volume corrector has a measurement range of 95 kPa to 105 kPa. This range is divided into three fuzzy sets: low, medium, and high. The membership function is simplified into a triangle, that is, when each membership function reaches a maximum value at a certain point and gradually decreases to zero on both sides:

[0126] Low:

[0127]

[0128] Among them, when P<P medium When the pressure value is lower than the middle threshold, the Low membership function is effective;

[0129] Medium:

[0130]

[0131] Among them, when P low ≤P≤P high When the pressure value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0132] High:

[0133]

[0134] Among them, when P>P medium When the pressure value is higher than the middle threshold, the High membership function is valid;

[0135] P low is the minimum threshold of humidity, below which the membership function μL(P) is applicable;

[0136] P medium It is the middle threshold of humidity, between low and high humidity;

[0137] P high is the maximum threshold of humidity, above which the membership function μH(P) applies;

[0138] Humidity membership function:

[0139] Low:

[0140]

[0141] Among them, H<H medium That is, when the humidity value is lower than the middle threshold, the Low membership function is valid;

[0142] Medium:

[0143]

[0144] Among them, H medium <H<H high That is, when the humidity value is between the minimum threshold and the maximum threshold, the Medium membership function is valid;

[0145] High:

[0146]

[0147] Among them, H>H medium That is, when the humidity value is higher than the middle threshold, the High membership function is valid;

[0148] H low is the minimum threshold of humidity, below which the membership function μL(H) is applicable;

[0149] H medium It is the middle threshold of humidity, between low and high humidity;

[0150] H high is the maximum threshold of humidity, above which the membership function μH(H) applies.

[0151] S3 builds a fuzzy rule base based on fuzzy sets and membership functions;

[0152] The construction of fuzzy rule base is shown in Table 1:

[0153] Table 1

[0154]

[0155]

[0156] S4 performs fuzzy reasoning on the membership function through the Mamdani method, obtains the output fuzzy set and performs membership aggregation, defuzzifies the fuzzy set aggregation using the centroid method, and obtains the volume detection correction coefficient of the target to be measured;

[0157] For each fuzzy rule in the fuzzy rule base, the activation degree of the rule is evaluated according to the membership of the input fuzzy set. The activation degree of the rule is evaluated using the minimum operation of the Mamdani method. The membership of the output fuzzy set is determined based on the membership of the input fuzzy set and the activation degree of the rule. The membership generated by the output fuzzy set is aggregated using the maximum operation of the Mamdani method. For each output variable, we take the maximum membership value among all rule outputs and finally obtain the aggregated fuzzy set.

[0158] In this embodiment, rule 1: temperature is low (0.5), pressure is low (0.3), humidity is low (0.4), then the corrected volume decreases (degree of membership 0.3).

[0159] Rule 2: If the temperature is high (0.7), the pressure is medium (0.5), and the humidity is low (0.2), the corrected volume remains unchanged (degree of membership 0.5).

[0160] Rule 3: If the temperature is low (0.4), the pressure is high (0.6), and the humidity is medium (0.5), then the corrected volume is reduced (membership degree 0.4).

[0161] Aggregation of fuzzy sets:

[0162] Specific steps of fuzzy set aggregation:

[0163] 1). Fuzzification: Convert the actual values ​​of the input variables (temperature, pressure, humidity) into fuzzy values, which represent the membership of the input variables in each fuzzy set (low, medium, high).

[0164] 2). Fuzzy reasoning:

[0165] For each fuzzy rule, the output fuzzy set is calculated based on the input fuzzy value. Based on the input fuzzy value (membership of temperature, membership of pressure and membership of humidity), the membership of the modified volume can be calculated. The output of each rule is a fuzzy set.

[0166] 3). Aggregation of fuzzy sets:

[0167] Union operation: The output fuzzy sets of all rules are combined, that is, the maximum membership degree among the outputs of each rule is taken. This means that as long as any rule meets the conditions, its output is considered valid.

[0168] Intersection: Fuzzy sets are aggregated using intersection, which involves taking the minimum of the minimum membership among the outputs of the individual rules. This means that all rules must satisfy the condition to some extent.

[0169] The aggregated set in this embodiment = {max(0.3, 0.5, 0.4), max(0.7, 0.6, 0.5), max(0.2, 0.4, 0.5)} = {0.7, 0.7, 0.5}.

[0170] This aggregated set represents the final fuzzy output, where the maximum membership of 0.7 corresponds to decrease and no change, and 0.5 corresponds to increase, that is, under given input conditions, the corrected volume is more likely to decrease or remain unchanged.

[0171] Defuzzify fuzzy set aggregates using the centroid method:

[0172] Determine the fuzzy set: Determine the output fuzzy set of the modified volume and its membership degree. The set has three elements, corresponding to reducing D, keeping N unchanged, and increasing I.

[0173] Define output range: define the value range for each fuzzy set;

[0174] The centroid calculation formula is as follows:

[0175]

[0176] Among them, x i is the output fuzzy set definition value, μ i is the corresponding degree of membership; the centroid is used to verify or explain the calculation results of the correction coefficient U;

[0177] The calculation formula of the correction coefficient U is as follows:

[0178]

[0179] Among them, ci is the volume corrector parameter output by the i-th rule. The formula of the correction coefficient U is based on the working principle of the volume corrector and the output of the fuzzy logic controller. This formula involves the fuzzy sets and membership functions of temperature, pressure and humidity, as well as the fuzzy rules of the input variables. U directly considers the physical characteristics of the volume corrector and the design of the fuzzy logic controller. The calculation results provided by the centroid method can be used as a reference or verification to ensure the accuracy and reliability of the correction coefficient.

[0180] S5 replaces the physical quantities in the improved PE equation with the output of the membership function, optimizes and combines the volume detection correction coefficient, establishes a volume correction model, outputs the final value of the correction model and performs error analysis with the original volume data.

[0181] By converting the actual physical quantity of the original Peukert equation into the membership value in fuzzy logic, calculating the corrected volume, dealing with uncertainty and fuzziness, and further accurately correcting the volume measurement value, the volume correction original equation is established:

[0182]

[0183] where μ V (x) is the membership function of the output variable;

[0184] The relationship equation between the corrected volume and the original volume is:

[0185] V corrected =V×U

[0186] Wherein, U is the volume detection correction coefficient of the target to be measured;

[0187] Combining the original volume correction equation and the correction coefficient equation, the physical quantities in the improved PE equation are replaced by the output of the membership function, and the final volume correction model is as follows:

[0188]

[0189] The error comparison equation before and after volume correction is established to perform error analysis. The equation is as follows:

[0190]

[0191] Among them, V corrected is the corrected volume, V is the measured volume, the subscript corrected indicates the corrected value, and P act is the actual pressure value, P L , P M , P H is the reference value corresponding to the low, medium and high fuzzy sets of pressure, T act is the actual temperature value, T L , T M , T H is the reference value corresponding to the low, medium and high fuzzy sets of temperature, H act is the actual humidity value, H L , H M , H H is the reference value corresponding to the low, medium and high fuzzy sets of humidity, P ref is the reference pressure value, T re is the reference temperature, H ref is the reference humidity value, γ is the pressure-related correction index, and α is the temperature-related correction index.

[0192] This embodiment also proposes a volume corrector error optimization system based on the improved PE equation. The system executes any step proposed in the embodiment through modular composition. The system includes a gas flow meter module, an input module, a temperature sensor module, a pressure sensor module, a humidity sensor module, a communication interface module, a display module, a feedback module, and a processing unit.

[0193] The input module is connected with the gas flow meter module, the temperature sensor module, the pressure sensor module and the humidity sensor module to receive flow pulse signals, temperature data, pressure data and humidity data respectively;

[0194] The processing unit is used to receive the data obtained by the input module and obtain the volume detection correction coefficient and the corrected volume data through the built-in volume detection correction coefficient acquisition method;

[0195] The output module is used to receive the volume detection correction data, fuzzy set and membership function data acquired by the processing unit and output them in the form of pulse signals, and input them into the feedback module;

[0196] The feedback module is used to integrate the output module data and perform volume error analysis and optimization self-calibration, and transmit the adjusted commands to the adjustment module;

[0197] The adjustment module is used to input commands to the actuator for execution;

[0198] The correction element is connected to the processing unit and is used to communicate the corrected pressure value with the processing unit;

[0199] The display unit is connected to the output module and is used for displaying the corrected volume data.

[0200] The above description of the embodiments enables professionals and technicians in the field to implement or use the present invention. Various modifications to the embodiments will be apparent to professionals and technicians. The general principles of the present invention can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention should not be limited to the embodiments shown herein, but should cover the widest range consistent with the principles and novel features disclosed in the present invention.

Claims

1. A volume corrector error analysis method based on the improved PE equation, characterized in that: The method comprises the following steps: S1 obtains environmental parameter variables related to the target to be measured; S2 defines the input fuzzy set based on the environmental variables, initializes the input environmental parameter variables and establishes the membership function; S3 builds a fuzzy rule base based on fuzzy sets and membership functions; S4 performs fuzzy reasoning on the membership function through the Mamdani method, obtains the output fuzzy set and performs membership aggregation, defuzzifies the fuzzy set aggregation using the centroid method, and obtains the volume detection correction coefficient of the target to be measured; S5 replaces the physical quantities in the improved PE equation with the output of the membership function, optimizes and combines the volume detection correction coefficient, establishes a volume correction model, outputs the final value of the correction model and performs error analysis with the original volume data.

2. The volume corrector error analysis method based on the improved PE equation according to claim 1 is characterized in that: The environmental parameter variables are initialized into five groups, each group including temperature, pressure and humidity.

3. The volume corrector error analysis method based on the improved PE equation according to claim 1 or 2, characterized in that: Define the fuzzy sets and membership functions of the input variables based on the initialization of 5 sets of input environmental parameter variables: The fuzzy sets are as follows: Temperature T: {L low, M medium, H high} Pressure P: {Low L, Medium M, High H} Humidity H: {Low L, Medium M, High H} The corresponding membership functions include: Membership function for temperature: Low: Among them, T<T medium That is, when the temperature value is lower than the middle threshold, the Low membership function is effective; Medium: Among them, T medium <T<T high When the temperature value is between the minimum threshold and the maximum threshold, the Medium membership function is valid; High: Among them, T>T medium When the temperature is higher than the middle threshold, the High membership function is valid; T low is the minimum threshold of temperature, below which the membership function μL(T) applies; T medium is the middle threshold of temperature, between low and high temperatures; T high is the maximum threshold of temperature, above which the membership function μH(T) applies; Membership function of pressure: Assume that the pressure P of the volume corrector has a measurement range of 95 kPa to 105 kPa. This range is divided into three fuzzy sets: low, medium, and high. The membership function is simplified into a triangle, that is, when each membership function reaches a maximum value at a certain point and gradually decreases to zero on both sides: Low: Among them, when P <P medium When the pressure value is lower than the middle threshold, the Low membership function is effective; Medium: Among them, when P low ≤P≤P high When the pressure value is between the minimum threshold and the maximum threshold, the Medium membership function is valid; High: Among them, when P>P medium When the pressure value is higher than the middle threshold, the High membership function is valid; P low is the minimum threshold of humidity, below which the membership function μL(P) is applicable; P medium It is the middle threshold of humidity, between low and high humidity; P high is the maximum threshold of humidity, above which the membership function μH(P) applies; Humidity membership function: Low: Among them, H <H medium That is, when the humidity value is lower than the middle threshold, the Low membership function is valid; Medium: Among them, H medium <H<H high That is, when the humidity value is between the minimum threshold and the maximum threshold, the Medium membership function is valid; High: Among them, H>H medium That is, when the humidity value is higher than the middle threshold, the High membership function is valid; H low is the minimum threshold of humidity, below which the membership function μL(H) is applicable; H medium It is the middle threshold of humidity, between low and high humidity; H high is the maximum threshold of humidity, above which the membership function μH(H) applies.

4. The volume corrector error analysis method based on the improved PE equation according to claim 3 is characterized in that: The fuzzy rule base is constructed as follows: When the temperature T is low, the pressure P is low, and the humidity H is low, the corrected volume will decrease; When the temperature T is high, the pressure P is medium, and the humidity H is low, the corrected volume remains unchanged; When the temperature T is low, the pressure P is high, and the humidity H is medium, the corrected volume will decrease; When the temperature T is medium, the pressure P is low, and the humidity H is high, the corrected volume remains unchanged; When the temperature T is high, the pressure P is high, and the humidity H is high, the corrected volume will increase.

5. The volume corrector error analysis method based on the improved PE equation according to claim 1 is characterized in that: The detailed process of fuzzy reasoning of membership function by Mamdani method described in S4 is as follows: For each fuzzy rule in the fuzzy rule base, the activation degree of the rule is evaluated according to the membership of the input fuzzy set. The activation degree of the rule is evaluated using the minimum operation of the Mamdani method. The membership of the output fuzzy set is determined based on the membership of the input fuzzy set and the activation degree of the rule. The membership generated by the output fuzzy set is aggregated using the maximum operation of the Mamdani method. For each output variable, we take the maximum membership value among all rule outputs and finally obtain the aggregated fuzzy set.

6. The volume corrector error analysis method based on the improved PE equation according to claim 1 or 5, characterized in that: The fuzzy set aggregation is defuzzified using the centroid method: Determine the fuzzy set: Determine the output fuzzy set of the modified volume and its membership degree. The set has three elements, corresponding to reducing D, keeping N unchanged, and increasing I. Define output range: define the value range for each fuzzy set; The centroid calculation formula is as follows: Among them, x i is the output fuzzy set definition value, μ i is the corresponding degree of membership; the centroid is used to verify or explain the calculation results of the correction coefficient U; The calculation of the correction factor U involves the following formula: Where ci is the volume corrector parameter output by the i-th rule.

7. The volume corrector error analysis method based on the improved PE equation according to claim 1 is characterized in that: The process of improving the PE equation and establishing the volume correction model described in S5 is as follows: By converting the actual physical quantity of the original Peukert equation into the membership value in fuzzy logic, calculating the corrected volume, dealing with uncertainty and fuzziness, and further accurately correcting the volume measurement value, the volume correction original equation is established: where μ V (x) is the membership function of the output variable; The relationship equation between the corrected volume and the original volume is: In corrected =H×W Wherein, U is the volume detection correction coefficient of the target to be measured; Combining the original volume correction equation and the correction coefficient equation, the physical quantities in the improved PE equation are replaced by the output of the membership function, and the final volume correction model is as follows: The error comparison equation before and after volume correction is established to perform error analysis. The equation is as follows: Among them, V corrected is the corrected volume, V is the measured volume, the subscript corrected indicates the corrected value, and P act为 Actual pressure value, P L ,P M ,P H is the reference value corresponding to the low, medium and high fuzzy sets of pressure, T act is the actual temperature value, T L ,T M ,T H is the reference value corresponding to the low, medium and high fuzzy sets of temperature, H act is the actual humidity value, H L ,H M ,H H is the reference value corresponding to the low, medium and high fuzzy sets of humidity, P ref is the reference pressure value, T re is the reference temperature, H ref is the reference humidity value, γ is the pressure-related correction index, and α is the temperature-related correction index.

8. The optimization system of the volume corrector error analysis method based on the improved PE equation according to any one of claims 1 to 7, characterized in that: The system performs any of the above steps by modular composition, and the system includes a gas flow meter module, an input module, a temperature sensor module, a pressure sensor module, a humidity sensor module, a communication interface module, a display module, a feedback module, and a processing unit; The input module is connected with the gas flow meter module, the temperature sensor module, the pressure sensor module and the humidity sensor module to receive flow pulse signals, temperature data, pressure data and humidity data respectively; The processing unit is used to receive the data obtained by the input module and obtain the volume detection correction coefficient and the corrected volume data through the built-in volume detection correction coefficient acquisition method; The output module is used to receive the volume detection correction data, fuzzy set and membership function data acquired by the processing unit and output them in the form of pulse signals, and input them into the feedback module; The feedback module is used to integrate the output module data and perform volume error analysis and optimization self-calibration, and transmit the adjusted commands to the adjustment module; The adjustment module is used to input commands to the actuator for execution; The correction element is connected to the processing unit and is used to communicate the corrected pressure value with the processing unit; The display unit is connected to the output module and is used for displaying the corrected volume data.

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