Intelligent metering method and system for airport gas energy

Abstract

The invention provides an intelligent metering method and system for airport fuel gas energy. The method comprises the steps of preprocessing fuel gas data and extracting key characteristic parameters; and inputting the parameters into a gas consumption prediction model to obtain a predicted value. And formulating a gas supply strategy according to the predicted value and the airport operation plan. Actual consumption is monitored in real time and compared with a predicted value, and a supply strategy is optimized; according to the method, firstly, gas data are collected and preprocessed, noise is eliminated, missing values are filled, and data accuracy and completeness are ensured; and then key characteristic parameters are extracted to reflect a gas consumption rule. Based on the parameters, a prediction model is constructed, and future consumption is predicted by using historical data and auxiliary information. The model is trained and optimized through a machine learning algorithm to improve the accuracy. When a supply strategy is formulated, a supply priority and a distribution plan are determined by combining a predicted value and an airport operation plan evaluation demand. Actual consumption is monitored in real time and compared with a predicted value, a supply strategy is adjusted in time, management is optimized, and the intelligent level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent metering and energy management, and in particular to an intelligent metering method and system for airport gas energy. Background Art

[0002] With the rapid development of the global aviation industry, airports, as the core nodes of air transportation, have seen an increasing amount of energy consumption, of which gas energy accounts for an important proportion. Traditional gas metering methods mainly rely on mechanical gas meters. Although these devices can meet basic metering needs to a certain extent, they have obvious deficiencies in accuracy, real-time performance, intelligence, and data analysis.

[0003] First, the measurement accuracy of traditional gas meters is limited by their mechanical structure and working principle, and it is often difficult to meet the requirements of high-precision measurement. In large energy-consuming places such as airports, small differences in gas usage may lead to significant changes in energy costs. Therefore, improving measurement accuracy is of great significance for energy conservation, emission reduction and cost control. Secondly, traditional gas meters lack real-time monitoring and remote communication capabilities, which makes it difficult to obtain and analyze gas usage data in a timely manner. This not only limits the real-time and accuracy of the energy management system, but also increases the workload of manual meter reading and data analysis. In addition, traditional gas metering methods lack intelligence and data analysis capabilities. In airport gas energy management, it is not enough to just know the gas usage. In-depth analysis of usage data is also required to discover potential energy-saving opportunities and optimization strategies. However, traditional gas meters cannot provide sufficient data support, making energy management decisions lack scientific basis. Summary of the invention

[0004] The present invention aims to at least solve the technical problem in the prior art that traditional gas metering methods lack intelligence and data analysis capabilities, and in particular innovatively proposes an airport gas energy intelligent metering method and system.

[0005] In order to achieve the above-mentioned object of the present invention, the present invention provides an airport gas energy intelligent metering method, the method comprising: S1. Collecting gas data, preprocessing the gas data, and extracting key characteristic parameters based on the preprocessed gas data; S2. Inputting the extracted key characteristic parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; S3. Formulate a gas energy supply strategy based on the predicted gas energy consumption value and in combination with the airport operation plan; S4. Monitor the actual consumption of gas energy in real time, compare it with the predicted value of gas energy consumption, and optimize the gas energy supply strategy according to the comparison result.

[0006] As an optional embodiment of the present invention, optionally, extracting key feature parameters in step S1 includes: S101, setting a data collection time interval, acquiring a gas meter reading sequence based on an image collector, and completing missing values ​​of the gas meter reading sequence; S102, smoothing the gas meter reading sequence; S103, extracting gas consumption features using a feature extraction algorithm based on the smoothed gas meter reading sequence; S104: Screen the gas consumption characteristics to obtain key characteristic parameters.

[0007] As an optional embodiment of the present invention, optionally, the expression for extracting the gas consumption characteristics in step S103 is: in, Indicates the gas consumption characteristics, represents the length of the gas meter reading sequence, Indicates The difference between the gas reading at a time point and the gas reading at the previous time point, Indicates Gas readings at time points, represents the mean of the gas meter reading sequence, represents the standard deviation of the series of gas meter readings, represents the exponential function, represents the standard deviation of the temperature series, Indicates The temperature value at a time point, Represents the mean of the temperature series.

[0008] As an optional embodiment of the present invention, optionally, the expression for completing missing values ​​of the gas meter reading sequence in step S101 is: , , , , ; in, represents the first missing value, Indicates The valid value before the missing value position, Indicates Valid values ​​after missing value positions, represents the second missing value, express the number of valid values ​​in the set, Represents a sequence of gas meter readings The Readings, represents the third missing value, represents the pre-trained time series trend prediction function, Represents the set of missing value locations The The position index of the missing values, Indicates The completed readings at the missing value positions, , and Both represent the weights used in weighted average calculation.

[0009] As an optional embodiment of the present invention, optionally, constructing the gas energy consumption prediction model in step S2 includes: S201, collecting historical gas data and auxiliary data, wherein the auxiliary data includes weather data and airport operation data; S202, preprocessing and feature extraction are performed on the historical gas data and auxiliary data in sequence to obtain historical gas consumption features; S203, constructing a loss function, based on the historical gas consumption characteristics and the loss function, using a machine learning algorithm to construct a gas energy consumption prediction model, and using a back propagation algorithm to train the gas energy consumption prediction model until a preset training accuracy or number of iterations is reached.

[0010] As an optional embodiment of the present invention, optionally, formulating the gas energy supply strategy in step S3 includes: S301. Based on the predicted value of gas energy consumption, combined with the airport's flight take-off and landing plan, passenger flow forecast, and airport facility operation requirements, comprehensively evaluate the gas energy supply requirements to obtain an evaluation result; S302, based on the evaluation results, determining the gas supply priorities for different time periods or different facilities, and formulating a gas energy distribution plan and supply schedule based on the priorities; S303, adjusting the operating parameters of the gas supply equipment according to the gas energy supply strategy and supply schedule; S304: Regularly evaluate the execution efficiency of the gas energy supply strategy, and optimize the gas energy supply strategy based on the regular evaluation results.

[0011] As an optional embodiment of the present invention, optionally, determining the gas supply priorities of different time periods or different facilities in step S302 includes: S3021. Analyze the degree of dependence of different time periods and different facilities on gas energy based on the historical gas consumption data, flight take-off and landing density, passenger flow fluctuations, and operation characteristics of airport facilities, and obtain analysis results; S3022. Based on the analysis results, a priority analysis method is used to quantitatively score the gas supply demands of different time periods and different facilities, wherein a higher score indicates a higher priority for gas supply; S3023. Based on the quantitative scoring results, formulate a priority list for gas supply.

[0012] As an optional embodiment of the present invention, optionally, the priority analysis method in step S3022 includes: S30221. Obtaining the judgment matrix , ,in Representation scheme Relative to the plan degree of importance; S30222, normalizing the judgment matrix , ,in, , represents the normalized judgment matrix, Represents the elements in the normalized judgment matrix, which are the elements of the original judgment matrix After normalization, the result is represents the order of the judgment matrix, that is, the number of options, Representation scheme Relative to the plan degree of importance; S30223, based on the normalized judgment matrix Calculate the weight vector for each solution , , ,in Representation scheme The weight of S30224, using consistency indicators and random consistency ratio Perform consistency check, where represents the maximum eigenvalue of the judgment matrix, represents the random consistency index; like <0.1, the judgment matrix is ​​consistent; like >0.1, the judgment matrix is ​​not consistent and needs to be adjusted until the consistency requirement is met.

[0013] On the other hand, the present invention further provides an airport gas energy intelligent metering system, the system comprising the airport gas energy intelligent metering method; The system further comprises: Data acquisition module, used to collect gas data; Communication module and the data acquisition module; A preprocessing module, connected to the communication module, for preprocessing the gas data; A feature extraction module, connected to the preprocessing module, for extracting key feature parameters based on the preprocessed gas data; A prediction model module, connected to the feature extraction module, for inputting the extracted key feature parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; A strategy formulation module, connected to the prediction model module, for formulating a gas energy supply strategy based on the predicted value of gas energy consumption and in combination with the airport operation plan; The monitoring adaptive optimization module is connected to the strategy formulation module, and is used to monitor the actual consumption of gas energy in real time, and compare it with the predicted value of gas energy consumption, and adaptively optimize the gas energy supply strategy based on the comparison result.

[0014] Beneficial effects of the present invention: The present invention first collects gas data and pre-processes the data to eliminate noise, fill missing values, etc., so as to ensure the accuracy and integrity of the data. Then, the key characteristic parameters are extracted using the pre-processed data, and these characteristic parameters can reflect the main laws and trends of gas consumption. Then, based on the extracted key characteristic parameters, a gas energy consumption prediction model is constructed. This model can use historical data and auxiliary information (such as weather data, airport operation data, etc.) to predict future gas consumption. In the process of building the prediction model, machine learning algorithms are used for training, and the accuracy of the prediction is improved by optimization means such as back propagation. When formulating a gas energy supply strategy, the method will comprehensively evaluate the supply demand of gas energy based on the predicted gas consumption value and the airport's operating plan (such as flight take-off and landing plan, passenger flow forecast, etc.). Based on this evaluation result, the gas supply priority of different time periods or different facilities can be determined, and the gas energy distribution plan and supply schedule can be formulated accordingly. Finally, the method will also monitor the actual consumption of gas energy in real time and compare it with the predicted value. Based on the comparison results, deviations in the supply strategy can be discovered and corrected in a timely manner, thereby continuously optimizing the gas energy supply strategy and improving the level of intelligence. This solves the problem of the lack of intelligence and data analysis capabilities in traditional gas metering methods, and also achieves efficient management and utilization of gas energy through real-time monitoring and optimization of supply strategies.

[0015] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 This is a flow chart of an airport gas energy intelligent metering method in Embodiment 1 of the present invention; Figure 2 It is a structural diagram of an airport gas energy intelligent metering system in Example 2 of the present invention. DETAILED DESCRIPTION

[0017] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0018] Example 1 like Figure 1 As shown, a method for intelligent metering of airport gas energy, the method comprising: S1. Collecting gas data, preprocessing the gas data, and extracting key characteristic parameters based on the preprocessed gas data; It should be noted that in step S1, the gas data includes the reading of the gas meter, the flow data of the gas pipeline, the gas pressure data, and the gas temperature data. The preprocessing process includes but is not limited to data cleaning, data conversion, and data normalization operations, which are intended to improve data quality. Data cleaning can eliminate outliers and duplicates in the data to ensure the accuracy and consistency of the data; data conversion is to convert the data into a format suitable for model processing; data normalization helps to eliminate the dimensional differences between different features and improve the convergence speed and prediction accuracy of the model. When extracting key feature parameters, this embodiment not only considers the information of the gas meter reading sequence itself, but also incorporates time factors and external environmental factors (such as temperature, etc.). These features together constitute a comprehensive perspective reflecting the law of gas consumption. By deeply mining and analyzing these features, the inherent laws of gas consumption can be more accurately grasped, providing strong support for subsequent predictions and strategy formulation.

[0019] S2. Inputting the extracted key characteristic parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; It should be noted that in step S2, the gas energy consumption prediction model constructed is specifically a prediction model based on machine learning, which can receive key feature parameters extracted from gas data as input and output the predicted value of gas energy consumption. In order to build this model, a large amount of historical gas data will be collected as training samples, including gas meter readings, gas pipeline flow data, gas pressure data, gas temperature data, etc. At the same time, auxiliary data related to gas consumption will also be collected, such as weather data (such as temperature, humidity, wind speed, etc.) and airport operation data (such as flight take-off and landing plans, passenger flow, etc.). These data will together constitute the input feature set of the model.

[0020] During the model training phase, appropriate machine learning algorithms (such as support vector machines, random forests, neural networks, etc.) are used to build prediction models. These algorithms can learn the patterns and trends of gas consumption from the input feature set and predict future gas consumption based on these patterns and trends. In order to improve the prediction accuracy of the model, some optimization techniques are also used, such as feature selection, parameter tuning, cross-validation, etc.

[0021] After the model is built, it can be applied to actual gas energy metering. By inputting the real-time collected gas data into the model, the predicted value of gas energy consumption can be obtained. This predicted value can provide an important reference for the airport's gas energy management, help the airport formulate a reasonable gas energy supply strategy, optimize the distribution and utilization of gas energy, improve energy utilization efficiency, and reduce operating costs.

[0022] S3. Formulate a gas energy supply strategy based on the predicted gas energy consumption value and in combination with the airport operation plan; It should be noted that in step S3, when formulating the gas energy supply strategy, first, a rough estimate of the airport's future gas demand will be made based on the predicted value of gas energy consumption. Then, combined with the airport's operating plan, such as flight take-off and landing plans, passenger flow forecasts, and airport facility operation requirements, the gas energy demand distribution will be further refined. In this process, the degree of dependence on gas energy in different time periods and different facilities will be fully considered to ensure the pertinence and effectiveness of the supply strategy.

[0023] S4. Monitor the actual consumption of gas energy in real time, compare it with the predicted value of gas energy consumption, and optimize the gas energy supply strategy according to the comparison result.

[0024] It should be noted that in step S4, real-time monitoring is a key link to ensure the effectiveness of the gas energy supply strategy. Through real-time monitoring, the difference between the actual consumption of gas energy and the predicted value can be discovered in time. This difference may be caused by various factors, such as sudden weather changes, temporary adjustments to flight plans, or operational failures of airport facilities. Once a significant deviation is found between the actual consumption and the predicted value, the system will immediately trigger the early warning mechanism and notify relevant personnel to conduct verification and processing. In the process of comparing the actual consumption with the predicted value, the system will use data analysis technology to conduct a detailed analysis and attribution of the deviation. This not only helps to quickly locate the problem, but also provides a scientific basis for subsequent optimization. Based on the comparison results, the system will adaptively adjust the gas energy supply strategy to ensure a dynamic balance between supply and demand. This adaptive optimization mechanism can continuously improve the management efficiency of gas energy and achieve more refined and intelligent energy management.

[0025] In summary, this embodiment first collects gas data and preprocesses the data to eliminate noise, fill missing values, etc., so as to ensure the accuracy and completeness of the data. Then, the key characteristic parameters are extracted using the preprocessed data, and these characteristic parameters can reflect the main laws and trends of gas consumption. Then, based on the extracted key characteristic parameters, a gas energy consumption prediction model is constructed. This model can use historical data and auxiliary information (such as weather data, airport operation data, etc.) to predict future gas consumption. In the process of building the prediction model, machine learning algorithms are used for training, and optimization methods such as back propagation are used to improve the accuracy of the prediction. When formulating a gas energy supply strategy, the method will comprehensively evaluate the supply demand of gas energy based on the predicted gas consumption value and the airport's operating plan (such as flight take-off and landing plan, passenger flow forecast, etc.). Based on this evaluation result, the gas supply priority of different time periods or different facilities can be determined, and the gas energy distribution plan and supply schedule can be formulated accordingly. Finally, the method will also monitor the actual consumption of gas energy in real time and compare it with the predicted value. Based on the comparison results, deviations in the supply strategy can be discovered and corrected in a timely manner, thereby continuously optimizing the gas energy supply strategy and improving the level of intelligence. This solves the problem of the lack of intelligence and data analysis capabilities in traditional gas metering methods, and also achieves efficient management and utilization of gas energy through real-time monitoring and optimization of supply strategies.

[0026] As an optional embodiment of the present invention, optionally, extracting key feature parameters in step S1 includes: S101, setting a data collection time interval, acquiring a gas meter reading sequence based on an image collector, and completing missing values ​​of the gas meter reading sequence; It should be noted that in step S101, the setting of the data collection time interval is intended to balance data accuracy and system overhead, ensuring that the detailed changes in gas consumption can be captured without increasing the system burden due to overly frequent data collection. The high accuracy and stability of the image collector are crucial for accurately obtaining gas meter readings, which can effectively avoid the problem of inaccurate subsequent analysis caused by reading errors. For missing gas meter readings caused by equipment failure, data transmission interruption, etc., this embodiment will use an interpolation algorithm to fill in the missing values ​​to ensure the integrity and continuity of the data sequence.

[0027] S102, smoothing the gas meter reading sequence; It should be noted that in step S102, the smoothing process is intended to eliminate noise and fluctuations in the gas meter reading sequence and improve the accuracy and reliability of the data. By smoothing the reading sequence, abnormal fluctuations caused by reading errors or equipment failures can be reduced, making the data more stable and continuous. In this embodiment, an appropriate smoothing algorithm, such as a moving average method, an exponential smoothing method, etc., is used to process the gas meter reading sequence to obtain a smoother and more accurate data sequence.

[0028] S103, extracting gas consumption features using a feature extraction algorithm based on the smoothed gas meter reading sequence; It should be noted that in step S103, the feature extraction algorithm can deeply mine the potential information and patterns in the gas meter reading sequence, thereby extracting key indicators reflecting the characteristics of gas consumption. These characteristics may include the daily trend, weekly change cycle, seasonal fluctuations, etc. of gas consumption, as well as the correlation with other related factors (such as weather, flight schedule, etc.).

[0029] S104: Screen the gas consumption characteristics to obtain key characteristic parameters.

[0030] It should be noted that in step S104, this embodiment uses a variety of screening methods, such as correlation coefficient analysis, mutual information analysis, recursive feature elimination, etc., to comprehensively evaluate the extracted gas consumption features. By comprehensively considering the importance, stability and correlation degree between the features and the prediction target, the most representative key feature parameters are finally screened out. These parameters will be used as inputs to the prediction model for subsequent gas energy consumption prediction.

[0031] As an optional embodiment of the present invention, optionally, the expression for extracting the gas consumption characteristics in step S103 is: in, Indicates gas consumption characteristics; Indicates the length of the gas meter reading sequence; Indicates The difference between the gas reading at a certain time point and the gas reading at the previous time point; Indicates Gas readings at each time point; represents the mean of the series of gas meter readings; represents the standard deviation of the series of gas meter readings; represents the exponential function; represents the standard deviation of the temperature series; Indicates The temperature value at a time point; Represents the mean of the temperature series.

[0032] It should be noted that This section calculates the square root of the sum of the squares of the gas consumption change rate, reflecting the fluctuation of gas consumption. Larger values ​​indicate larger changes in gas consumption in a short period of time. This section calculates the cube root of the average of the cube of the absolute value of the deviation of gas consumption from the mean, which is used to measure the degree to which gas consumption deviates from normal levels. Larger values ​​indicate that gas consumption deviates more from the average level. This part is the inverse of a Gaussian function, which is used to measure the concentration of the temperature series. Smaller values ​​indicate larger temperature fluctuations, which may have a significant impact on gas consumption. If temperature has no significant impact on gas consumption, this item can be omitted or replaced with other relevant factors in specific implementation.

[0033] As an optional embodiment of the present invention, optionally, the expression for completing missing values ​​of the gas meter reading sequence in step S101 is: , , , , ; in, represents the first missing value; Indicates Valid values ​​before the missing value position; Indicates Valid values ​​after missing value positions; represents the second missing value; express The number of valid values ​​in the set; Represents a sequence of gas meter readings The readings; represents the third missing value; represents the pre-trained time series trend prediction function; Represents the set of missing value locations The The position index of the missing values; Indicates The completed readings at the missing value positions; , and Both represent the weights used in weighted average calculation.

[0034] As an optional embodiment of the present invention, optionally, constructing the gas energy consumption prediction model in step S2 includes: S201, collecting historical gas data and auxiliary data, wherein the auxiliary data includes weather data and airport operation data; S202, preprocessing and feature extraction are performed on the historical gas data and auxiliary data in sequence to obtain historical gas consumption features; S203, constructing a loss function, based on the historical gas consumption characteristics and the loss function, using a machine learning algorithm to construct a gas energy consumption prediction model, and using a back propagation algorithm to train the gas energy consumption prediction model until a preset training accuracy or number of iterations is reached.

[0035] It should be noted that in step S201, the collection of historical gas data and auxiliary data is the basis for building a prediction model. Historical gas data records the past gas consumption of the airport, while auxiliary data such as weather data and airport operation data provide information on external factors related to gas consumption. These data together constitute the input set for model training, providing a rich source of information for the model to learn the laws and trends of gas consumption.

[0036] In step S202, preprocessing and feature extraction of historical gas data and auxiliary data are key steps to ensure model quality. The preprocessing stage aims to eliminate noise and outliers in the data, fill in missing values, and perform necessary data conversion to ensure data accuracy and consistency. The feature extraction stage deeply mines the potential information and rules in the data to extract key indicators that can reflect the characteristics of gas consumption. These indicators will serve as input features for model training.

[0037] In step S203, the loss function defines the degree of difference between the model prediction value and the actual value. By minimizing the loss function, the parameters of the model can be continuously adjusted to improve the prediction accuracy of the model. In this embodiment, a gas energy consumption prediction model is constructed using a machine learning algorithm, and the model is trained using a back propagation algorithm. The back propagation algorithm continuously optimizes the prediction performance of the model by calculating the gradient of the loss function with respect to the model parameters and updating the model parameters in the opposite direction of the gradient. The training process will continue until the preset training accuracy or number of iterations is reached to ensure the stability and reliability of the model.

[0038] As an optional embodiment of the present invention, optionally, formulating the gas energy supply strategy in step S3 includes: S301, comprehensively evaluating the supply demand of gas energy according to the predicted value of gas energy consumption, combined with the airport's flight take-off and landing plan, passenger flow forecast, and airport facility operation requirements, to obtain an evaluation result; It should be noted that in step S301, the evaluation results not only take into account the predicted value of gas consumption, but also fully integrate the actual operation of the airport, such as flight take-off and landing plans, passenger flow forecasts, and the operation requirements of airport facilities, etc., to ensure the comprehensiveness and accuracy of the evaluation results. The specific evaluation process will adopt a variety of evaluation methods and models, such as time series analysis, regression analysis, machine learning algorithms, etc., to comprehensively consider the impact of various factors on the supply and demand of gas energy. By comprehensively analyzing these factors, a more accurate and reliable evaluation result can be obtained. Based on this evaluation result, the airport can more accurately grasp the supply and demand of gas energy.

[0039] S302, based on the evaluation results, determining the gas supply priorities for different time periods or different facilities, and formulating a gas energy distribution plan and supply schedule based on the priorities; It should be noted that in step S302, the determination of gas supply priority will take into account a variety of factors, such as the accuracy of the gas consumption forecast value, the importance of the airport operation plan, and the reliability and stability of the gas supply. For time periods or facilities with higher forecast values ​​and more critical airport operation plans, higher supply priorities will be given to ensure that their gas needs are met. At the same time, when formulating the gas energy allocation plan and supply schedule, the reliability and stability of the gas supply will also be fully considered to avoid adverse effects on airport operations due to insufficient or interrupted supply. By comprehensively considering these factors, a more reasonable and effective gas energy supply strategy can be formulated to achieve efficient management and utilization of gas energy.

[0040] S303, adjusting the operating parameters of the gas supply equipment according to the gas energy supply strategy and supply schedule; It should be noted that in step S303, the parameters include gas supply pressure, flow rate, temperature, etc. By accurately adjusting these parameters, it is possible to ensure that the gas supply matches the actual needs of the airport, meeting operational needs while avoiding energy waste. The adjustment process will be dynamically adjusted according to the supply strategy and supply schedule to adapt to changes in gas demand in different time periods and facilities. At the same time, the gas supply equipment will be regularly maintained and overhauled to ensure that it is in good operating condition and to avoid adverse effects on gas supply due to equipment failure.

[0041] S304: Regularly evaluate the execution efficiency of the gas energy supply strategy, and optimize the gas energy supply strategy based on the regular evaluation results.

[0042] It should be noted that in step S304, in this embodiment, the evaluation process will use a variety of indicators and methods, such as comparison of gas consumption, analysis of supply stability, evaluation of equipment operating efficiency, etc., to comprehensively measure the execution efficiency of the strategy. Based on the evaluation results, the gas energy supply strategy can be adjusted and optimized, such as adjusting the supply priority, optimizing the allocation plan, improving the equipment operating status, etc., to achieve more efficient and reliable gas energy management and utilization.

[0043] As an optional embodiment of the present invention, optionally, determining the gas supply priorities of different time periods or different facilities in step S302 includes: S3021. Analyze the degree of dependence of different time periods and different facilities on gas energy based on the historical gas consumption data, flight take-off and landing density, passenger flow fluctuations, and operation characteristics of airport facilities, and obtain analysis results; It should be noted that in step S3021, the analysis results can reveal the gas consumption patterns of the airport in different time periods and different facilities, thereby determining which time periods or facilities are more dependent on gas energy. For example, during periods of intensive flight takeoffs and landings, gas consumption may increase significantly, so these time periods should be given a higher supply priority. Similarly, facilities whose operating characteristics determine that they consume more gas should also be given a higher supply priority to ensure that their gas needs are met in a timely manner. By deeply analyzing these factors, it is possible to provide strong support for the formulation of more accurate and effective gas energy supply strategies.

[0044] S3022. Based on the analysis results, a priority analysis method is used to quantitatively score the gas supply demands of different time periods and different facilities, wherein a higher score indicates a higher priority for gas supply; It should be noted that in step S3022, the priority analysis method will comprehensively consider multiple factors, such as the accuracy of the gas consumption forecast value, the density of flight take-offs and landings, passenger flow fluctuations, the operating characteristics of airport facilities, and the reliability and stability of gas supply. By assigning different weights to these factors and combining historical data with actual conditions for quantitative scoring, the gas supply priorities of different time periods and different facilities can be obtained. A higher score means that the time period or facility has a higher priority in gas supply and needs more attention and resource investment.

[0045] S3023. Based on the quantitative scoring results, formulate a priority list for gas supply.

[0046] It should be noted that in step S3023, the priority list not only helps airport managers to intuitively understand the gas supply priorities of various time periods and facilities, but also serves as an important basis for formulating gas energy allocation plans and supply schedules. When formulating the priority list, the comprehensive impact of various factors will be fully considered to ensure the accuracy and rationality of the list. By following this list, the airport can manage and utilize gas energy more efficiently, meet the actual needs of airport operations, and reduce energy consumption and costs.

[0047] As an optional embodiment of the present invention, optionally, the priority analysis method in step S3022 includes: S30221. Obtaining the judgment matrix , ,in Representation scheme Relative to the plan degree of importance; It should be noted that in step S30221, to obtain the judgment matrix, it is first necessary to determine the objects of comparison. These objects can be different criteria (such as historical gas consumption data, flight take-off and landing density, etc.) or different solutions (such as different time periods or facilities). In order to quantify the relative importance between the comparison objects, a 1-9 scale is usually used. This scale reflects the comparative level and magnitude characteristics of people's judgment psychology, where 1 means that two objects are equally important, 9 means that one object is extremely more important than the other, and the middle values ​​represent different degrees of relative importance. Next, the comparison objects are compared pairwise. For each criterion in the criterion layer, a judgment matrix is ​​constructed to compare the relative importance of each solution under the criterion. For the solutions in the solution layer, pairwise comparisons are also required based on the criteria of the previous layer. Fill in the judgment matrix based on the results of the pairwise comparisons. Elements of the judgment matrix Representation Object Relative to the object The importance of. Since the judgment matrix is ​​a positive reciprocal matrix, that is =1 / , so only the upper or lower triangle of the matrix needs to be filled in, and the rest can be obtained through reciprocity. In practical applications, the data of the judgment matrix is ​​usually obtained through expert scoring. Experts make subjective judgments on the comparison objects based on their own knowledge and experience, and give corresponding scale values. Then, these scale values ​​are combined to obtain the elements of the judgment matrix.

[0048] S30222, normalizing the judgment matrix , ,in, , represents the normalized judgment matrix, Represents the elements in the normalized judgment matrix, which are the elements of the original judgment matrix After normalization, the result is represents the order of the judgment matrix, that is, the number of options, Representation scheme Relative to the plan degree of importance; S30223, based on the normalized judgment matrix Calculate the weight vector for each solution , , ,in Representation scheme The weight of S30224, using consistency indicators and random consistency ratio Perform consistency check, where represents the maximum eigenvalue of the judgment matrix, represents the random consistency index; like <0.1, the judgment matrix is ​​consistent; like >0.1, the judgment matrix is ​​not consistent and needs to be adjusted until the consistency requirement is met.

[0049] Example 2 like Figure 2 As shown, an airport gas energy intelligent metering system, the system includes the airport gas energy intelligent metering method; The system further comprises: The data acquisition module is used to collect gas data; specifically, the data acquisition module includes a sensor network and a data processing unit. The sensor network is deployed at key gas supply points and consumption points at the airport, and can monitor key parameters such as gas flow and pressure in real time. The data processing unit is responsible for receiving the data transmitted by the sensor network and performing preliminary processing and storage.

[0050] The communication module is connected to the data acquisition module and is responsible for transmitting the processed gas data and related information to the preprocessing module in real time. The communication module adopts an efficient and stable data transmission protocol to ensure the timeliness and accuracy of the data.

[0051] The preprocessing module is connected to the communication module and is used to preprocess the gas data. Specifically, the preprocessing module cleans, integrates and formats the received gas data. The cleaning stage removes outliers and duplicate data, fills in missing values, and ensures the accuracy and completeness of the data. The integration stage merges data from different sources to form a unified data format. The formatting stage performs necessary conversion and encoding on the data to meet the needs of subsequent analysis and processing. Through the processing of the preprocessing module, the gas data will be converted into a high-quality, standardized data set.

[0052] A feature extraction module, connected to the preprocessing module, for extracting key feature parameters based on the preprocessed gas data; A prediction model module, connected to the feature extraction module, for inputting the extracted key feature parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; The strategy formulation module is connected to the prediction model module and is used to formulate a gas energy supply strategy based on the predicted value of gas energy consumption and the airport operation plan; specifically, when formulating the gas energy supply strategy, the strategy formulation module will comprehensively consider multiple factors, such as the predicted value of gas consumption, flight take-off and landing plans, passenger flow forecasts, and the operation requirements of airport facilities. These factors together constitute the input set for strategy formulation, which provides strong support for formulating a gas energy supply strategy that meets actual needs and is efficient and energy-saving. The strategy formulation module will use advanced algorithms and models, such as time series analysis, regression analysis, machine learning algorithms, etc., to conduct a comprehensive analysis of the input set to derive the optimal gas energy supply strategy. At the same time, the strategy formulation module will also dynamically adjust and optimize the strategy according to the actual situation and operational needs of the airport to ensure that it always remains in the best state.

[0053] The monitoring adaptive optimization module is connected to the strategy formulation module, and is used to monitor the actual consumption of gas energy in real time, and compare it with the predicted value of gas energy consumption, and adaptively optimize the gas energy supply strategy based on the comparison result.

[0054] It should be noted that, in this embodiment, the data acquisition module is used to collect the airport's gas data in real time. These data include but are not limited to key parameters such as gas flow, pressure, and temperature, which provide a basis for subsequent preprocessing and feature extraction. The communication module is responsible for transmitting the collected data to the preprocessing module, ensuring the timeliness and accuracy of the data.

[0055] The preprocessing module cleans and organizes the received gas data, including removing outliers, filling missing values, and data smoothing to improve the quality and availability of the data. The preprocessed data will be passed to the feature extraction module.

[0056] Based on the preprocessed gas data, the feature extraction module uses the feature extraction algorithm to extract key feature parameters that can reflect the law and trend of gas consumption. These feature parameters not only contain information about historical gas consumption, but may also incorporate external factors such as weather and flight schedules, providing rich input features for the construction of the prediction model.

[0057] The prediction model module inputs the extracted key characteristic parameters into the pre-built gas energy consumption prediction model, and obtains the predicted value of gas consumption through complex calculation and analysis. This predicted value not only takes into account the rules of historical data, but also incorporates the changes in current and future external factors, and has high accuracy and reliability.

[0058] The strategy formulation module formulates a reasonable gas energy supply strategy based on the gas energy consumption forecast output by the prediction model, combined with the airport's operating plan and actual needs. This strategy not only includes the total amount and time schedule of gas supply, but may also involve the adjustment of supply priorities for different facilities or time periods to ensure the stability and efficiency of airport operations.

[0059] The monitoring adaptive optimization module monitors the actual consumption of gas energy in real time and compares it with the predicted value. Once a large deviation is found between the actual consumption and the predicted value, the module will immediately start the adaptive optimization mechanism to adjust and optimize the gas energy supply strategy to ensure the accuracy and effectiveness of the strategy. This real-time monitoring and adaptive optimization capability not only improves the management efficiency of gas energy, but also helps reduce energy consumption and costs.

[0060] As another optional embodiment of the present invention, optionally, the system further includes: An alarm module, connected to the monitoring adaptive optimization module, is used to send out an alarm signal when the actual consumption of gas energy exceeds a preset threshold or the gas energy supply strategy cannot meet the airport operation requirements; It should be noted that when the monitoring adaptive optimization module finds that the actual gas consumption exceeds the preset safety range, or the current gas energy supply strategy cannot meet the airport's operational needs, the alarm module will immediately start and send out a clear and unambiguous alarm signal. These signals can be conveyed to relevant personnel in a variety of ways, such as sound alarms, flashing lights, or sending SMS notifications, to ensure that relevant personnel can quickly learn about the situation and take corresponding countermeasures. The alarm module not only improves the safety of airport gas energy management, but also helps to enhance the airport's ability to respond to emergencies. Through real-time monitoring and timely alarms, airports can quickly discover and solve potential problems in gas supply and avoid adverse effects on airport operations due to gas shortages or interruptions. At the same time, the alarm module can also serve as an important part of the airport gas energy management system, working in conjunction with other modules to achieve efficient management and utilization of gas energy.

[0061] Historical data storage submodule, used to store historical gas data, auxiliary data, gas consumption characteristics, gas energy consumption prediction model parameters and optimized supply strategies; A user interaction interface submodule provides a user-friendly operation interface for providing gas energy consumption, gas energy consumption forecast values, supply strategies and alarm information. Users can set parameters and adjust strategies through the user interaction interface submodule; It should be noted that the user interaction interface submodule provides an intuitive and easy-to-use operating platform, allowing airport managers to easily view the consumption, forecast values ​​and current supply strategies of gas energy. Through this interface, managers can not only grasp the dynamic information of gas energy in real time, but also set parameters and adjust strategies according to actual needs. This flexibility and convenience greatly improves the efficiency and accuracy of gas energy management. At the same time, the historical data storage submodule is responsible for storing a large amount of historical data and optimized supply strategies, providing a solid foundation for subsequent data analysis and strategy optimization. By making full use of these data and strategies, airports can continuously optimize their gas energy management processes and achieve more efficient and sustainable development.

[0062] The adaptive optimization module comprises: The comparison and analysis submodule is used to compare the actual consumption of gas energy with the predicted value of gas energy consumption and generate a comparison and analysis report; The strategy adjustment submodule is connected to the comparison and analysis submodule, and based on the differences in the comparison and analysis report, automatically adjusts the parameters in the gas energy supply strategy using a machine learning algorithm.

[0063] It should be noted that the adaptive optimization module includes a comparative analysis submodule and a strategy adjustment submodule. These two submodules work together to achieve continuous optimization of the gas energy supply strategy. The comparative analysis submodule is responsible for carefully comparing the actual gas energy consumption monitored in real time with the predicted value, so as to identify the difference between the actual consumption and the predicted value. This comparison not only focuses on the deviation of the total amount, but also deeply analyzes the consumption differences in different time periods and facilities, providing detailed data support for subsequent optimization. The strategy adjustment submodule uses advanced machine learning algorithms based on the report generated by the comparative analysis submodule to automatically adjust the key parameters in the gas energy supply strategy; specifically, such as adjusting the time interval of gas supply, optimizing the allocation ratio, and improving the operating parameters of the equipment, so as to achieve the refinement and intelligence of gas energy supply. Through the continuous learning and optimization of the machine learning algorithm, the airport's gas energy supply strategy can gradually adapt to various complex situations and improve energy utilization efficiency and supply stability. This adaptive optimization mechanism not only reduces the workload of managers, but also greatly improves the intelligence level of gas energy management. At the same time, the adaptive optimization module can also promptly feedback the optimized supply strategy to the user interaction interface submodule for managers to view and adjust, thus forming a complete closed-loop management system.

[0064] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. An intelligent metering method for airport gas energy, characterized in that: The method comprises: S1. Collecting gas data, preprocessing the gas data, and extracting key characteristic parameters based on the preprocessed gas data; S2. Inputting the extracted key characteristic parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; S3. Formulate a gas energy supply strategy based on the predicted gas energy consumption value and in combination with the airport operation plan; S4. Monitor the actual consumption of gas energy in real time, compare it with the predicted value of gas energy consumption, and optimize the gas energy supply strategy according to the comparison result.

2. The intelligent metering method for airport gas energy according to claim 1, characterized in that: Extracting key feature parameters in step S1 includes: S101, setting a data collection time interval, acquiring a gas meter reading sequence based on an image collector, and completing missing values ​​of the gas meter reading sequence; S102, smoothing the gas meter reading sequence; S103, extracting gas consumption features using a feature extraction algorithm based on the smoothed gas meter reading sequence; S104: Screen the gas consumption characteristics to obtain key characteristic parameters.

3. The intelligent metering method for airport gas energy according to claim 2, characterized in that: The expression for extracting the gas consumption characteristics in step S103 is: in, Indicates the gas consumption characteristics, represents the length of the gas meter reading sequence, Indicates The difference between the gas reading at a time point and the gas reading at the previous time point, Indicates Gas readings at time points, represents the mean of the gas meter reading sequence, represents the standard deviation of the series of gas meter readings, represents the exponential function, represents the standard deviation of the temperature series, Indicates The temperature value at a time point, Represents the mean of the temperature series.

4. The intelligent metering method for airport gas energy according to claim 2, characterized in that: In step S101, the expression for completing missing values ​​of the gas meter reading sequence is: ， ， ， ， ； in, represents the first missing value, Indicates The valid value before the missing value position, Indicates Valid values ​​after missing value positions, represents the second missing value, express the number of valid values ​​in the set, Represents a sequence of gas meter readings The Readings, represents the third missing value, represents the pre-trained time series trend prediction function, Represents the set of missing value locations The The position index of the missing values, Indicates The completed readings at the missing value positions, , and Both represent the weights used in weighted average calculation.

5. The intelligent metering method for airport gas energy according to claim 2, characterized in that: Constructing the gas energy consumption prediction model in step S2 includes: S201, collecting historical gas data and auxiliary data, wherein the auxiliary data includes weather data and airport operation data; S202, preprocessing and feature extraction are performed on the historical gas data and auxiliary data in sequence to obtain historical gas consumption features; S203, constructing a loss function, based on the historical gas consumption characteristics and the loss function, using a machine learning algorithm to construct a gas energy consumption prediction model, and using a back propagation algorithm to train the gas energy consumption prediction model until a preset training accuracy or number of iterations is reached.

6. The intelligent metering method for airport gas energy according to claim 5, characterized in that: Formulating the gas energy supply strategy in step S3 includes: S301, comprehensively evaluating the supply demand of gas energy according to the predicted value of gas energy consumption, combined with the airport's flight take-off and landing plan, passenger flow forecast, and airport facility operation requirements, to obtain an evaluation result; S302, based on the evaluation results, determining the gas supply priorities for different time periods or different facilities, and formulating a gas energy distribution plan and supply schedule based on the priorities; S303, adjusting the operating parameters of the gas supply equipment according to the gas energy supply strategy and supply schedule; S304: Regularly evaluate the execution efficiency of the gas energy supply strategy, and optimize the gas energy supply strategy based on the regular evaluation results.

7. The intelligent metering method for airport gas energy according to claim 6, characterized in that: Determining the gas supply priorities of different time periods or different facilities in step S302 includes: S3021. Analyze the degree of dependence of different facilities on gas energy in different time periods and according to historical gas consumption data, flight take-off and landing density, passenger flow fluctuations and the operating characteristics of airport facilities, and obtain analysis results; S3022. Based on the analysis results, a priority analysis method is used to quantitatively score the gas supply demands of different time periods and different facilities, wherein a higher score indicates a higher priority for gas supply; S3023. Based on the quantitative scoring results, formulate a priority list for gas supply.

8. The intelligent metering method for airport gas energy according to claim 7, characterized in that: In step S3022, the priority analysis method includes: S30221. Obtaining the judgment matrix , ,in Representation scheme Relative to the plan degree of importance; S30222, normalizing the judgment matrix , ,in, , represents the normalized judgment matrix, Represents the elements in the normalized judgment matrix, which are the elements of the original judgment matrix After normalization, the result is represents the order of the judgment matrix, that is, the number of options, Representation scheme Relative to the plan degree of importance; S30223, based on the normalized judgment matrix Calculate the weight vector for each solution , , ,in Representation scheme The weight of S30224, using consistency indicators and random consistency ratio Perform consistency check, where represents the maximum eigenvalue of the judgment matrix, represents the random consistency index; like <0.1, the judgment matrix is ​​consistent; like >0.1, the judgment matrix is ​​not consistent and needs to be adjusted until the consistency requirement is met.

9. An airport gas energy intelligent metering system, characterized in that: The system comprises an intelligent metering method for airport gas energy according to any one of claims 1 to 7; The system further comprises: Data acquisition module, used to collect gas data; Communication module and the data acquisition module; A preprocessing module, connected to the communication module, for preprocessing the gas data; A feature extraction module, connected to the preprocessing module, for extracting key feature parameters based on the preprocessed gas data; A prediction model module, connected to the feature extraction module, for inputting the extracted key feature parameters into a pre-built gas energy consumption prediction model to obtain a gas energy consumption prediction value; A strategy formulation module, connected to the prediction model module, for formulating a gas energy supply strategy based on the predicted value of gas energy consumption and in combination with the airport operation plan; The monitoring adaptive optimization module is connected to the strategy formulation module, and is used to monitor the actual consumption of gas energy in real time, and compare it with the predicted value of gas energy consumption, and adaptively optimize the gas energy supply strategy based on the comparison result.

10. The airport gas energy intelligent metering system according to claim 9, characterized in that: The system further comprises: An alarm module, connected to the monitoring adaptive optimization module, is used to send out an alarm signal when the actual consumption of gas energy exceeds a preset threshold or the gas energy supply strategy cannot meet the airport operation requirements; Historical data storage submodule, used to store historical gas data, auxiliary data, gas consumption characteristics, gas energy consumption prediction model parameters and optimized supply strategies; A user interaction interface submodule provides a user-friendly operation interface for providing gas energy consumption, gas energy consumption forecast values, supply strategies and alarm information. Users can set parameters and adjust strategies through the user interaction interface submodule; The adaptive optimization module comprises: The comparison and analysis submodule is used to compare the actual consumption of gas energy with the predicted value of gas energy consumption and generate a comparison and analysis report; The strategy adjustment submodule is connected to the comparison and analysis submodule, and based on the differences in the comparison and analysis report, automatically adjusts the parameters in the gas energy supply strategy using a machine learning algorithm.

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