Intelligent scheduling management method for fuel gas

Abstract

The invention discloses an intelligent scheduling management method for fuel gas, and belongs to the technical field of fuel gas management systems, and the method comprises the steps: receiving scheduling information which comprises data features of a scheduling target point; according to the data characteristics of the scheduling target points, utilizing a neural network model to predict and analyze the dynamic change of the gas demand, and determining the priority of each scheduling target point; distributing a corresponding scheduling time slice to each scheduling target point according to the priority of the scheduling target points; gas pipe network operation information is obtained; according to the gas pipe network operation information, the priority of the scheduling target point and the scheduling time slice, a gas emergency scheduling rule is made, and dynamic optimization and adjustment of gas scheduling are achieved. According to the method, the scheduling priority can be dynamically adjusted according to the real-time condition, and it is ensured that each scheduling target point can obtain timely and reasonable fuel gas distribution.

Description

Technical Field

[0001] This application relates to the technical field of gas management systems, and particularly to an intelligent scheduling management method for gas. Background Art

[0002] In recent years, the scale of the urban gas market in China has continued to expand. With the acceleration of the urbanization process and the improvement of residents' living standards, natural gas, as a clean and efficient energy source, has been increasing in urban supply and consumption. The industrial chain of gas management mainly includes upstream gas source exploration and production, midstream storage and transportation and distribution systems, and downstream distribution systems. With the development of smart gas, products such as smart gas meters and remote meter reading systems have emerged in an endless stream.

[0003] However, at present, gas scheduling mostly relies on fixed scheduling strategies or simple queue management, such as the multi-level feedback queue scheduling algorithm. When dealing with emergency scheduling requirements, such methods may cause some target points in urgent need of gas to be unable to be served for a long time due to fixed rules, affecting the emergency response speed and overall scheduling efficiency. Summary of the Invention

[0004] In order to dynamically adjust the scheduling priority according to real-time situations and ensure that each scheduling target point can obtain timely and reasonable gas allocation, this application provides an intelligent scheduling management method for gas.

[0005] An intelligent scheduling management method for gas provided by this application includes the following steps:

[0006] Receive scheduling information, where the scheduling information includes data characteristics of scheduling target points;

[0007] According to the data characteristics of the scheduling target points, use a neural network model to predict and analyze the dynamic changes in gas demand, and determine the priority of each scheduling target point;

[0008] According to the priority of the scheduling target points, allocate corresponding scheduling time slices to each scheduling target point to ensure that high-priority target points can obtain more resources and time during the scheduling process;

[0009] Obtain gas pipeline network operation information, where the gas pipeline network operation information includes the real-time operation status of the gas pipeline network, available gas sources, and the distribution of emergency resources;

[0010] According to the gas pipeline network operation information, the priority of the scheduling target points, and the scheduling time slices, formulate gas emergency scheduling rules to achieve dynamic optimization and adjustment of gas scheduling.

[0011] Further, in the step of determining the priority of each scheduling target point, it specifically includes:

[0012] Retrieve user information, which includes user basic data, transaction records, gas usage behavior data, importance, and urgency level;

[0013] Clean the collected data to remove duplicate, invalid, or abnormal data;

[0014] Standardize the data to ensure that data from different sources and formats can be compared and analyzed;

[0015] Analyze the processed user data using a clustering analysis algorithm to divide users into different categories;

[0016] Evaluate the classification effect based on the within-group similarity and between-group differences of the clustering results;

[0017] Make necessary adjustments and optimizations to the classification results according to business requirements;

[0018] Set different weights for each user category, with the highest weight for key users and the lowest weight for replaceable or interruptible users;

[0019] Comprehensively calculate the priority of the scheduling target point based on the weight, importance, and urgency level of the user.

[0020] Furthermore, after the step of receiving scheduling information, it further includes:

[0021] Obtain the actual gas usage data of each user through an intelligent gas meter;

[0022] Correspondingly calculate the unevenness coefficient of each user according to the actual gas usage data of each user. The unevenness coefficient is (maximum value - minimum value) / (maximum value + minimum value); where the maximum value and the minimum value respectively represent the maximum value and the minimum value of the user's gas consumption within a certain time range;

[0023] Analyze the gas usage fluctuation situation of each user according to the result of the unevenness coefficient. The closer the unevenness coefficient is to 0, the more stable the user's gas usage behavior; the closer the unevenness coefficient is to 1, the greater the fluctuation of the user's gas usage behavior;

[0024] Query the unevenness coefficient threshold corresponding to each user from a preset database;

[0025] If the unevenness coefficient exceeds the unevenness coefficient threshold, generate an alarm instruction and execute it.

[0026] Furthermore, after the step of generating an alarm instruction and executing it, it further includes:

[0027] Obtain user safety hazard information, which includes gas leakage and equipment aging;

[0028] Analyze the safety hazard assessment results based on the user safety hazard information;

[0029] Analyze the changes in the user's gas usage behavior according to the user safety hazard information and the non-uniformity coefficient;

[0030] Dynamically adjust the classification and weight of users based on the changes in the user's gas usage behavior and the safety hazard assessment results. For users with large fluctuations in gas usage behavior or potential safety hazards, increase their classification level and priority, and strengthen gas supply guarantee and service.

[0031] Further, after the step of obtaining the actual gas usage data of each user through the intelligent gas meter, it further includes:

[0032] Extract key features according to the actual gas usage data, and the key features include gas consumption, gas usage frequency, and gas usage time period;

[0033] Use time series analysis methods to capture the periodic and trend features in the actual gas usage data;

[0034] Obtain external data, and the external data includes weather data and holiday data;

[0035] Analyze the user's gas usage habits using machine learning algorithms based on the key features, periodic and trend features, and external data;

[0036] Obtain the minimum pre-storage time from a preset database;

[0037] Calculate the pre-stored gas volume according to the user's gas usage habits and the minimum pre-storage time;

[0038] Store gas in advance according to the pre-stored gas volume;

[0039] If the pre-stored gas volume is lower than the pre-stored gas volume, generate and execute a warning instruction, and the warning instruction is used to trigger the supplementary gas process.

[0040] Further, after the step of analyzing the user's gas usage habits, it further includes:

[0041] Determine the special gas transmission time periods in advance according to the user's gas usage habits, and the special gas transmission time periods include peak time periods and off-peak time periods;

[0042] Determine the gas usage demand during the off-peak time period;

[0043] Calculate the actual gas supply volume required during the off-peak time period according to the gas usage demand during the off-peak time period;

[0044] During the off-peak time period, supply gas according to the actual gas supply volume required by the user, and pre-input gas into the gas storage facility for storage;

[0045] During peak hours, monitor in real time whether the gas transmission can meet the usage demand during peak hours;

[0046] If the gas transmission cannot meet the usage demand during peak hours, call the pre-stored gas from the gas storage facility to supplement the gas volume in the pipeline.

[0047] Further, after the step of monitoring in real time whether the gas transmission can meet the usage demand during peak hours, it further includes:

[0048] Retrieve the pipeline identifier for peak gas transmission;

[0049] Obtain the gas pipeline pressure data corresponding to the pipeline identifier for peak gas transmission;

[0050] Obtain the pipeline pressure threshold corresponding to the pipeline identifier for peak gas transmission from the preset database;

[0051] If the gas pipeline pressure data exceeds the pipeline pressure threshold, generate and execute a pipeline pressure regulation instruction, and the pipeline pressure regulation instruction controls the gas flow rate and the pressure in the pipeline through a pressure regulating device until the gas pipeline pressure data is lower than the pipeline pressure threshold;

[0052] Call the pre-stored gas from the gas storage facility to supplement the gas volume in the pipeline nearby.

[0053] In summary, compared with the prior art, the beneficial effects of the above technical solution are:

[0054] An intelligent scheduling management method for gas described in this application can predict and analyze the dynamic changes in gas demand by using a neural network model. This method can dynamically adjust the priorities of each scheduling target point according to actual demands and situations. Compared with traditional fixed scheduling strategies or simple queue management, it can more flexibly respond to emergency scheduling demands, ensuring that target points in urgent need of gas can obtain services first. The dynamic priority adjustment and the allocation of scheduling time slices enable high-priority target points to obtain more resources and time during the scheduling process, thereby improving the emergency response speed, which is crucial for handling emergencies or urgent demands, and helps to reduce losses and impacts caused by insufficient gas supply.

[0055] By comprehensively considering the real-time operating status of the gas pipeline network, available gas sources, and the distribution of emergency resources, this method can formulate more reasonable gas emergency dispatching rules, which helps optimize resource allocation, ensure the efficient utilization of gas resources, and reduce waste and unnecessary losses. Through dynamically optimizing and adjusting the gas dispatching strategy, the intelligent dispatching management method can significantly improve the overall dispatching efficiency, which not only improves the stability and reliability of gas supply but also reduces the dispatching cost and enhances the operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a schematic flowchart of an intelligent dispatching management method for gas in an embodiment of the present application.

[0057] Figure 2 is a schematic flowchart of comprehensively calculating the priority of dispatching target points in an embodiment of the present application.

[0058] Figure 3 is a schematic flowchart of obtaining the actual gas consumption data of each user through an intelligent gas meter in an embodiment of the present application.

[0059] Figure 4 is a schematic flowchart of obtaining user safety hazard information in an embodiment of the present application.

[0060] Figure 5 is a schematic flowchart of analyzing users' gas consumption habits using machine learning algorithms in an embodiment of the present application.

[0061] Figure 6 is a schematic flowchart of determining special gas transmission periods in advance in an embodiment of the present application.

[0062] Figure 7 is a schematic flowchart of obtaining the gas pipeline pressure data corresponding to the pipeline identifier for peak gas transmission in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following further elaborates on the present application in conjunction with all the drawings.

[0064] An embodiment of the present application discloses an intelligent dispatching management method for gas. Referring to Figure 1 , an intelligent dispatching management method for gas includes:

[0065] S101. Receive dispatching information.

[0066] Specifically, the dispatching management system receives dispatching information, and the dispatching information includes the data characteristics of the dispatching target points; these data characteristics can be the gas demand of the target points, historical gas usage records, user types (such as residential users, industrial users, etc.), and geographical location information. By establishing a dedicated data receiving system or interface, these dispatching information are received and stored in real time, providing a basis for subsequent analysis and prediction, ensuring the accuracy and timeliness of the dispatching information, and providing reliable data support for the subsequent steps.

[0067] S102. Use a neural network model to predict and analyze the dynamic changes in gas demand, and determine the priority of each dispatching target point.

[0068] Specifically, the dispatching management system uses a neural network model to predict and analyze the dynamic changes in gas demand according to the data characteristics of the dispatching target points, and determines the priority of each dispatching target point; after receiving the dispatching information, the dispatching management system uses the trained neural network model to predict and analyze the dynamic changes in gas demand. The neural network model can learn the patterns in historical data to predict the gas demand and its change trend of each dispatching target point in a future period of time. According to the prediction results, the priority of each dispatching target point can be determined, and the priority level can be comprehensively determined according to factors such as the predicted gas demand and demand urgency. This improves the accuracy and reliability of gas demand prediction, provides a scientific basis for dispatching decisions, and by dynamically adjusting the priority, it can ensure that in the case of tight gas supply, the needs of the target points in urgent need of gas are preferentially met.

[0069] S103. Allocate corresponding dispatching time slices to each dispatching target point.

[0070] Specifically, the dispatching management system allocates corresponding dispatching time slices to each target point according to the determined priority of the dispatching target points. The allocation of the dispatching time slices can be set differently based on the priority level. The target points with high priority can obtain more dispatching time and resources. The specific setting of the dispatching time slices can be flexibly adjusted according to the actual situation to ensure the fairness and efficiency of dispatching. By differentially allocating the dispatching time slices, it can ensure that the target points with high priority obtain more resources and time during the dispatching process, thereby improving the emergency response speed and overall dispatching efficiency.

[0071] S104. Obtain the operation information of the gas pipeline network.

[0072] Specifically, the dispatching management system obtains the operation information of the gas pipeline network, which includes the real-time operation status of the gas pipeline network, available gas sources, and the distribution of emergency resources. By establishing a dedicated monitoring system and data acquisition equipment, the operation status information of the gas pipeline network is obtained in real time, including key parameters such as the pressure, flow rate, and temperature of the pipeline network, as well as the reserves and distribution of available gas sources. At the same time, it is also necessary to collect the distribution of emergency resources, including emergency repair teams, emergency equipment, and materials, etc., ensuring the timeliness and accuracy of the operation information of the gas pipeline network and providing a reliable basis for formulating emergency dispatching rules.

[0073] S105. Develop gas emergency dispatching rules.

[0074] Specifically, the dispatching management system formulates gas emergency dispatching rules based on the operation information of the gas pipeline network, the priority of the dispatching target point, and the dispatching time slice, realizing the dynamic optimization and adjustment of gas dispatching. According to the obtained operation information of the gas pipeline network, the priority of the dispatching target point, and the dispatching time slice, gas emergency dispatching rules are formulated, including dispatching strategies to be adopted in different emergency situations, allocation plans for emergency resources, etc., to ensure the scientificity and rationality of dispatching decisions. By formulating gas emergency dispatching rules, the dynamic optimization and adjustment of gas dispatching are realized. In case of an emergency, the emergency dispatching mechanism can be quickly activated to ensure the stability and safety of gas supply.

[0075] In another embodiment, referring to Figure 2 , S102 specifically includes the following sub-steps:

[0076] S102.1. Retrieve user information.

[0077] Specifically, the dispatching management system retrieves the basic data of users (such as user ID, name, address, etc.), transaction records (such as historical gas consumption, gas usage fees, etc.), gas usage behavior data (such as gas usage stability, gas usage time pattern, etc.), and the importance and urgency information of users (such as whether they are key users, whether they have special gas usage requirements, etc.) from the database, obtaining comprehensive user information and providing a basis for subsequent data processing and analysis.

[0078] S102.2. Clean the collected data to remove duplicate, invalid, or abnormal data.

[0079] Specifically, the scheduling management system cleans the collected data to remove duplicate, invalid or abnormal data; identifies and removes duplicate data, such as duplicate user IDs or transaction records. Checks and corrects invalid data, such as obviously incorrect gas consumption or expense records. Identifies and processes abnormal data, such as suddenly surging or dropping gas consumption, which may require interpolation or smoothing processing. Ensures the accuracy and consistency of the data and avoids the impact of invalid or abnormal data on subsequent analysis.

[0080] S102.3. Standardize the data.

[0081] Specifically, the scheduling management system standardizes the data to ensure that data from different sources and in different formats can be compared and analyzed; converts and unifies data from different sources and in different formats, such as unifying the time format to a unified time zone and unifying the gas consumption unit to the same standard. Scales or normalizes the data so that data in different dimensions can be compared and analyzed on the same scale. Improves the comparability and analysis efficiency of the data and provides convenience for subsequent data analysis.

[0082] S102.4. Analyze the processed user data using a clustering analysis algorithm to divide users into different categories.

[0083] Specifically, the scheduling management system analyzes the processed user data using a clustering analysis algorithm to divide users into different categories; in this embodiment, the K-means algorithm is selected to analyze the processed user data. Set the number of clusters and other parameters according to business requirements and data characteristics, execute the clustering algorithm, and divide users into different categories, realizing the segmentation and classification of users, which helps to identify the characteristics and needs of different user groups.

[0084] S102.5. Evaluate the classification effect based on the within-group similarity and between-group difference of the clustering results.

[0085] Specifically, the scheduling management system evaluates the classification effect based on the within-group similarity and between-group difference of the clustering results; calculates the within-group similarity and between-group difference indicators (such as the silhouette coefficient) of the clustering results. Compare the classification effects under different numbers of clusters and select the optimal clustering scheme to ensure the rationality and effectiveness of the classification results, providing a basis for subsequent user weight setting.

[0086] S102.6. Make necessary adjustments and optimizations to the classification results.

[0087] Specifically, the scheduling management system makes necessary adjustments and optimizations to the classification results according to business requirements; reviews and adjusts the classification results according to business requirements. Merge or split user categories that do not meet expectations or business requirements. Ensure that the classification results meet business requirements, providing an accurate classification basis for subsequent user weight setting and priority calculation.

[0088] S102.7. Set different weights for each user category.

[0089] Specifically, the scheduling management system sets different weight values according to user categories, with the highest weight for key users and the lowest weight for replaceable or interruptible users. The weight setting can be comprehensively considered based on factors such as business requirements, user historical performance, and user value, realizing the differential setting of user weights and providing an important basis for subsequent priority calculation.

[0090] S102.8. Comprehensively calculate the priority of the scheduling target points.

[0091] Specifically, the scheduling management system comprehensively calculates the priority of the scheduling target points according to the weight, importance, and urgency of the user. According to the weight, importance, and urgency information of the user, comprehensively calculate the priority of each scheduling target point. The priority calculation can be based on algorithms such as weighted average, linear regression, and decision tree to obtain the priority ranking of each scheduling target point, providing clear guidance for subsequent scheduling decisions.

[0092] Refer to Figure 3 , further, after S101, as another implementation manner, the embodiments of the present application may further include the following steps:

[0093] S201. Obtain the actual gas consumption data of each user through an intelligent gas meter.

[0094] Specifically, the scheduling management system obtains the actual gas consumption data of each user through an intelligent gas meter; the intelligent gas meter needs to be pre-installed at each user end and configured to send the actual gas consumption data of the user to the data center of the gas company regularly or in real time. The data center obtains the detailed gas consumption records of each user within a specific time period by receiving and processing these data. Realize the real-time or quasi-real-time monitoring of the user's gas consumption behavior, providing a data basis for subsequent analysis and warning.

[0095] S202. Correspondingly calculate the uneven coefficient of each user.

[0096] Specifically, the dispatching management system calculates the uneven coefficient for each user according to the actual gas consumption data of each user. The uneven coefficient is (maximum value - minimum value) / (maximum value + minimum value); where the maximum value and the minimum value respectively represent the maximum value and the minimum value of the user's gas consumption within a certain time range. According to the actual gas consumption data of each user, calculate the maximum gas consumption and the minimum gas consumption within a certain time range (such as one day, one week or one month). Using the formula (maximum value - minimum value) / (maximum value + minimum value) to calculate the uneven coefficient of each user provides an index to quantify the volatility of the user's gas consumption, which is convenient for subsequent analysis and comparison.

[0097] S203. Analyze the fluctuation of gas consumption of each user.

[0098] Specifically, the dispatching management system analyzes the fluctuation of the user's gas consumption behavior according to the calculated uneven coefficient. The closer the uneven coefficient is to 0, the more stable the user's gas consumption behavior; the closer the uneven coefficient is to 1, the greater the fluctuation of the user's gas consumption behavior. Thus, it provides an intuitive evaluation of the stability of the user's gas consumption behavior, which helps to identify potential problem users or abnormal gas consumption behaviors.

[0099] S204. Query the corresponding uneven coefficient threshold for each user from the preset database.

[0100] Specifically, the dispatching management system queries the corresponding uneven coefficient threshold for each user from the preset database; a database containing the uneven coefficient thresholds of users is preset, where each user or user category has a corresponding threshold. According to the user ID or user category, query the corresponding uneven coefficient threshold of the user from the database. It realizes the personalized evaluation of the volatility of the user's gas consumption behavior and improves the accuracy and pertinence of the alarm.

[0101] S205. If the uneven coefficient exceeds the uneven coefficient threshold, generate an alarm instruction and execute it.

[0102] Specifically, if the uneven coefficient exceeds the uneven coefficient threshold, the dispatching management system generates an alarm instruction and executes it. When the uneven coefficient of a user exceeds its corresponding uneven coefficient threshold, an alarm instruction is automatically generated. The alarm instruction includes information such as user ID, alarm type, alarm time, uneven coefficient, etc., and is sent to the relevant management personnel or systems. According to the business requirements, execute the corresponding alarm handling measures, such as dispatching staff for on-site inspection, communicating with the user, etc. It timely discovers the abnormal situation of the user's gas consumption behavior and takes corresponding handling measures, avoiding potential safety hazards or economic losses, and improving the operation efficiency and user satisfaction of the gas company.

[0103] Refer to Figure 4, Further, after S205, as another implementation manner, the embodiments of the present application may further include the following steps:

[0104] S301. Obtain user safety hazard information.

[0105] Specifically, the dispatching management system obtains user safety hazard information, and the user safety hazard information includes gas leakage and equipment aging; through tools and technical means such as intelligent gas meters, gas leakage detectors, and equipment aging detection systems, the user's safety hazard information is collected in real time or regularly. The safety hazard information includes gas leakage, equipment aging, and illegal use of gas. Timely obtaining the user's safety hazard information provides data support for subsequent analysis and evaluation.

[0106] S302. Analyze the safety hazard assessment results.

[0107] Specifically, the dispatching management system analyzes the safety hazard assessment results according to the user safety hazard information; classifies, sorts, and evaluates the collected safety hazard information to determine the severity of the hazard and the possible consequences. According to the assessment results, corresponding hazard handling measures and emergency plans are formulated, thereby providing a quantitative assessment of safety hazards, helping to identify and handle high-risk users or areas, formulating targeted hazard handling measures, and improving the efficiency and effect of hazard handling.

[0108] S303. Analyze the changes in the user's gas consumption behavior.

[0109] Specifically, the dispatching management system combines the user's uneven coefficient and safety hazard information to analyze the changes in the user's gas consumption behavior. Focus on users with large fluctuations in gas consumption behavior or safety hazards, and analyze the changing trends and reasons of their gas consumption behavior. Thereby providing an in-depth understanding of the changes in the user's gas consumption behavior, helping to identify potential problem users or abnormal gas consumption behaviors, and providing a basis for subsequent user classification and weight adjustment.

[0110] S304. Dynamically adjust the user's classification and weight.

[0111] Specifically, the dispatching management system dynamically adjusts the user's classification and weight according to the changes in the user's gas consumption behavior and the safety hazard assessment results. For users with large fluctuations in gas consumption behavior or safety hazards, increase their classification level and priority, and strengthen gas supply guarantee and service. For users with stable gas consumption behavior and fewer safety hazards, their classification level and priority can be appropriately reduced to optimize resource allocation. The dynamic adjustment of the user's classification and weight is realized, the pertinence and efficiency of gas supply guarantee and service are improved, it helps to timely discover and handle potential problem users or areas, and reduces safety hazards and operation risks.

[0112] Refer toFigure 5 , Further, after S201, as another implementation manner, the embodiments of the present application may further include the following steps:

[0113] S401. Extract key features.

[0114] Specifically, the scheduling management system extracts key features according to the actual gas consumption data. The key features include gas consumption, gas usage frequency, and gas usage time period. From the actual gas consumption data obtained from the intelligent gas meter, key features such as gas consumption, gas usage frequency, and gas usage time period are extracted. The gas consumption can be calculated by reading the change in the gas meter reading; the gas usage frequency is determined according to the number of times the user uses gas within a certain period of time; the gas usage time period records the time range when the user usually uses gas. These key features can intuitively reflect the user's gas usage behavior and provide basic data for subsequent analysis.

[0115] S402. Use time series analysis methods to capture the periodic and trend features in the actual gas consumption data.

[0116] Specifically, the scheduling management system uses time series analysis methods, such as autocorrelation function (ACF), Fourier transform, etc., to process the actual gas consumption data and capture the periodic and trend features therein. The periodic features can be manifested as seasonal changes in the user's gas usage behavior, such as an increase in gas consumption in winter; the trend features can be manifested as long-term growth or decline in the user's gas consumption. By capturing the periodic and trend features, the gas usage behavior pattern of the user can be understood more deeply, providing a basis for predicting future gas consumption.

[0117] S403. Obtain external data.

[0118] Specifically, the scheduling management system obtains external data, and the external data includes weather data and holiday data; information such as weather data and holiday data is obtained from reliable external data sources. The weather data may include temperature, humidity, etc., and these factors may affect the user's gas consumption demand; the holiday data can reflect the user's gas usage behavior changes due to holidays. The external data provides an additional dimension for analyzing the user's gas usage habits and helps to understand the user's gas usage behavior more comprehensively.

[0119] S404. Use machine learning algorithms to analyze the user's gas usage habits.

[0120] Specifically, the scheduling management system analyzes the user's gas usage habits based on the key features, periodic and trend features, and external data, using machine learning algorithms. The extracted key features, captured periodic and trend features, and obtained external data are used as inputs for training and analysis using machine learning algorithms (such as logistic regression, decision tree, etc.). Through machine learning algorithms, the correlation between the user's gas usage behavior and various factors can be mined, thus forming a user's gas usage habit model. The user's gas usage habit model can accurately reflect the characteristics of the user's gas usage behavior, providing a reliable basis for subsequent calculation of the pre-stored gas volume and early gas storage.

[0121] S405. Obtain the minimum pre-storage time from a preset database.

[0122] Specifically, the scheduling management system obtains the minimum pre-storage time from a preset database; the minimum pre-storage time is obtained from a preset database, which is usually determined according to the operating strategy of the gas company and the needs of users. The minimum pre-storage time provides a time limit for users to store gas in advance, helping to ensure that users always have sufficient gas supply.

[0123] S406. Calculate the pre-stored gas volume.

[0124] Specifically, the scheduling management system calculates the pre-stored gas volume based on the user's gas usage habits and the minimum pre-storage time; according to the user's gas usage habit model and the minimum pre-storage time, the pre-stored gas volume required by the user in a future period is calculated through a certain algorithm (such as a time series prediction model). The calculation of the pre-stored gas volume can provide reasonable gas reserve suggestions for users, ensuring that users will not be interrupted due to insufficient gas during use.

[0125] S407. Store gas in advance.

[0126] Specifically, the scheduling management system stores gas in advance according to the pre-stored gas volume; according to the calculated pre-stored gas volume, the gas company or users can store gas in advance, involving measures such as adjusting the gas supply system and increasing gas storage facilities. Storing gas in advance can ensure that users have sufficient gas supply during peak gas usage periods or special situations, improving the user's gas usage experience.

[0127] S408. If the gas volume stored in advance is lower than the pre-stored gas volume, generate a warning instruction and execute it.

[0128] Specifically, if the amount of gas stored in advance by the dispatching management system is lower than the pre-stored gas volume, a warning instruction is generated and executed. The warning instruction is used to trigger the supplementary gas process. When the amount of gas stored in advance is lower than the pre-stored gas volume, the system automatically generates a warning instruction. The warning instruction triggers the supplementary gas process, including measures such as notifying the user or the gas company and adjusting the gas supply plan. By generating a warning instruction and executing the supplementary gas process, the problem of insufficient gas can be detected and handled in a timely manner, avoiding the impact on users due to gas shortage.

[0129] Referring to Figure 6 , further, after S404, as another implementation manner, the embodiments of the present application may further include the following steps:

[0130] S501. Determine the special gas transmission period in advance.

[0131] Specifically, the dispatching management system determines the special gas transmission period in advance according to the gas usage habits of the user. The special gas transmission period includes peak hours and off-peak hours; based on the analysis of the user's gas usage habits, tools such as time series analysis and machine learning models are used to identify the peak hours and off-peak hours of the user's gas usage. These periods may vary due to factors such as season, weather, and holidays. Peak hours are usually the periods with the highest gas demand, such as the morning and evening meal times and the heating period in winter; off-peak hours are the periods with relatively low gas demand, such as late at night or the working period during the day. Determining the special period helps the gas company arrange the gas transmission and storage plans more reasonably, improving the efficiency and stability of gas supply.

[0132] S502. Determine the gas demand during off-peak hours.

[0133] Specifically, the dispatching management system determines the gas demand during off-peak hours; after identifying the off-peak hours, the gas demand during off-peak hours is predicted according to historical data and the user gas usage habit model. This requires considering various factors, such as weather changes and holiday impacts. Through data analysis, the average gas consumption per hour or per minute during off-peak hours is obtained as the actual gas demand during this period. Accurately predicting the gas demand during off-peak hours helps the gas company formulate the supply plan more precisely, avoiding over-supply or under-supply situations.

[0134] S503. Calculate the actual gas supply volume required during off-peak hours.

[0135] Specifically, the dispatching management system calculates the actual gas supply quantity required during the low-demand period according to the gas consumption demand during the low-demand period; calculates the actual gas supply quantity required during the low-demand period according to the gas consumption demand during the low-demand period, combined with factors such as the capacity and transportation efficiency of the gas transmission pipeline. This requires considering factors such as losses during gas transmission and pipeline pressure changes to ensure that the supplied gas quantity can meet the actual demand. By accurately calculating the gas supply quantity during the low-demand period, it can ensure that the gas company provides sufficient gas supply at the lowest cost during the low-demand period.

[0136] S504. During the low-demand period, supply gas according to the actual gas supply quantity required by users, and pre-input gas into the gas storage facility for storage.

[0137] Specifically, during the low-demand period, the dispatching management system supplies gas according to the actual gas supply quantity required by users, and pre-inputs gas into the gas storage facility for storage; during the low-demand period, according to the calculated actual gas demand, adjusts the flow rate of the gas transmission pipeline to ensure that users obtain sufficient gas supply. At the same time, utilize gas storage facilities (such as gas storage tanks, underground gas storage caverns, etc.) to pre-input gas for storage during the low-demand period for use during the peak-demand period. This helps to balance gas supply and demand, reduce the gas transmission pressure during the peak-demand period, and improve the utilization rate of gas storage facilities.

[0138] S505. During the peak-demand period, monitor in real time whether the gas transmission can meet the usage demand during the peak-demand period.

[0139] Specifically, during the peak-demand period, the dispatching management system monitors in real time whether the gas transmission can meet the usage demand during the peak-demand period; during the peak-demand period, monitors parameters such as the pressure and flow rate of the gas transmission pipeline through a real-time monitoring system to ensure that the gas transmission can meet the usage demand during the peak-demand period. If it is found that the gas transmission cannot meet the demand during the peak-demand period, immediately activate the emergency plan, such as calling the pre-stored gas from the gas storage facility, etc. The real-time monitoring system helps to promptly discover and respond to the situation of insufficient gas supply, ensuring that users obtain stable gas supply during the peak-demand period.

[0140] S506. If the gas transmission cannot meet the usage demand during the peak-demand period, call the pre-stored gas from the gas storage facility to supplement the gas quantity in the pipeline.

[0141] Specifically, if the gas transmission of the dispatching management system cannot meet the usage demand during peak hours, the pre-stored gas is called from the gas storage facility to supplement the gas volume in the pipeline. When it is found that the gas transmission cannot meet the demand during peak hours, the release mechanism of the gas storage facility is immediately activated to release the pre-stored gas into the transmission pipeline to supplement the gas volume in the pipeline. At the same time, by adjusting parameters such as the flow rate and pressure of the transmission pipeline, it is ensured that the gas can be stably and continuously transported to the user end. By calling the pre-stored gas from the gas storage facility, the gas demand during peak hours can be quickly responded to, ensuring that users obtain sufficient gas supply. At the same time, this step also helps to improve the emergency response ability and service level of the gas company.

[0142] Referring to Figure 7 , further, after S505, as another implementation manner, the embodiments of the present application may further include the following steps:

[0143] S601. Retrieve the pipeline identifier for peak gas transmission.

[0144] Specifically, the dispatching management system retrieves the pipeline identifier for peak gas transmission; during the monitoring process in peak hours, when it is found that the gas transmission may not meet the usage demand during peak hours, first, according to the layout of the gas transmission system and the gas usage demand during peak hours, determine the pipelines that need to conduct peak gas transmission. Retrieve the identifier information of these pipelines from the database of the gas transmission system, including key parameters such as pipeline number, location, length, diameter, etc. Accurately retrieving the identifier information of the peak gas transmission pipelines provides basic data for subsequent pressure monitoring and adjustment, ensuring stable gas supply during peak hours.

[0145] S602. Obtain the gas pipeline pressure data corresponding to the pipeline identifier for peak gas transmission.

[0146] Specifically, the dispatching management system obtains the gas pipeline pressure data corresponding to the pipeline identifier for peak gas transmission; uses the pressure sensors installed on the peak gas transmission pipelines to monitor and record the gas pressure data in the pipelines in real time. Associate these pressure data with the pipeline identifier information to ensure that the pressure conditions of each pipeline can be accurately tracked. Obtaining the pressure data of the peak gas transmission pipelines in real time helps to promptly discover and respond to abnormal pressure situations, ensuring the safe operation of the gas transmission system.

[0147] S603. Obtain the pipeline pressure threshold corresponding to the pipeline identifier for peak gas transmission from the preset database.

[0148] Specifically, the dispatching management system obtains the pipeline pressure thresholds corresponding to the pipeline identifiers for peak gas transmission from a preset database; according to parameters such as the material, diameter, and wall thickness of the peak gas transmission pipelines, as well as the design requirements of the gas transmission system, it obtains the pressure thresholds of each pipeline from the preset database. These pressure thresholds generally include the maximum allowable working pressure and the minimum working pressure, which are used to ensure that the pipeline can remain within a safe range during normal operation and peak periods. Obtaining accurate pipeline pressure thresholds provides a scientific basis for subsequent pipeline pressure regulation and prevents safety accidents caused by excessive or too low pressure.

[0149] S604. If the gas pipeline pressure data exceeds the pipeline pressure threshold, generate a pipeline pressure regulation instruction and execute it.

[0150] Specifically, if the gas pipeline pressure data exceeds the pipeline pressure threshold, the dispatching management system generates a pipeline pressure regulation instruction and executes it. The pipeline pressure regulation instruction controls the gas flow rate and the pressure in the pipeline through a pressure regulating device until the gas pipeline pressure data is lower than the pipeline pressure threshold; when the gas pipeline pressure data monitored in real time exceeds the preset pressure threshold, the system automatically generates a pipeline pressure regulation instruction. These instructions control the gas flow rate and the pressure in the pipeline through pressure regulating devices (such as pressure reducing valves, throttle valves, etc.), gradually reducing the gas pressure in the pipeline until it is lower than the pressure threshold, timely adjusting the pipeline pressure, preventing safety accidents such as pipeline rupture and leakage caused by excessive pressure, and ensuring the stable operation of the gas transmission system.

[0151] S605. Call the pre-stored gas from the gas storage facility and supplement the gas volume in the pipeline nearby.

[0152] Specifically, the dispatching management system calls the pre-stored gas from the gas storage facility and supplements the gas volume in the pipeline nearby. While adjusting the pipeline pressure, according to the gas consumption demand during peak periods and the pipeline pressure conditions, it calls the pre-stored gas from the gas storage facility. By optimizing the gas transmission path and dispatching strategy, it ensures that the gas volume in the pipeline is supplemented nearby, so that the gas pipeline does not always conduct gas transmission operations under excessive pressure, meeting the gas consumption demand during peak periods. Timely supplementing the gas volume in the pipeline ensures that users can obtain stable gas supply during peak periods. At the same time, by optimizing the dispatching strategy and supplementing the gas volume nearby, the transmission pressure of the gas pipeline is reduced, and the gas utilization efficiency is improved.

[0153] The embodiment of the present application also discloses an intelligent terminal, which includes a memory and a processor. Among them, a computer program capable of being loaded and executed by the processor, such as the intelligent dispatching management method of a kind of gas as described above, is stored on the memory.

[0154] The embodiments of the present application also disclose a computer-readable storage medium. The computer-readable storage medium stores a computer program that can be loaded and executed by a processor to perform an intelligent scheduling management method for a gas as described above. The computer-readable storage medium includes, for example, various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting the protection scope of the invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on these embodiments, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art can still, without conflict and without creative efforts, combine, add, delete, or make other adjustments to the features in the embodiments of the present invention according to the situation, so as to obtain different technical solutions that are essentially not divorced from the concept of the present invention. These technical solutions also belong to the scope of protection of the present invention.

Claims

1. A method for intelligent dispatching and management of gas, characterized in that: The following steps are involved: receiving scheduling information, wherein the scheduling information includes data characteristics of a scheduling target point; According to the data characteristics of the scheduling target point, a neural network model is used to predict and analyze the dynamic changes in gas demand and determine the priority of each scheduling target point; According to the priority of the scheduling target point, a corresponding scheduling time slice is allocated to each scheduling target point to ensure that the target point with high priority obtains more resources and time during the scheduling process; Obtaining gas network operation information, wherein the gas network operation information includes the real-time operation status of the gas network, available gas sources, and emergency resource distribution; According to the gas network operation information, the priority of the dispatch target point and the dispatch time slice, gas emergency dispatch rules are formulated to achieve dynamic optimization and adjustment of gas dispatch.

2. The intelligent dispatching management method for gas according to claim 1 is characterized in that: The step of determining the priority of each scheduling target point specifically includes: Retrieving user information, including basic user data, transaction records, gas usage behavior data, importance and urgency; Clean the collected data to remove duplicate, invalid or abnormal data; Standardize data to ensure that data from different sources and formats can be compared and analyzed; Use cluster analysis algorithms to analyze the processed user data and divide users into different categories; Evaluate the classification effect based on the intra-group similarities and inter-group differences of the clustering results; Make necessary adjustments and optimizations to the classification results based on business needs; Set different weights for each user category, with key users having the highest weight and replaceable or interruptible users having the lowest weight; The priority of the scheduling target point is comprehensively calculated according to the weight, importance and urgency of the user.

3. The intelligent dispatching management method for gas according to claim 2 is characterized in that: After the step of receiving the scheduling information, the method further includes: Obtain actual gas usage data of each user through smart gas meters; According to the actual gas consumption data of each user, the uneven coefficient of each user is calculated, and the uneven coefficient is (maximum value-minimum value) / (maximum value+minimum value); wherein the maximum value and the minimum value represent the maximum value and the minimum value of the gas consumption of the user within a certain time range, respectively; According to the result of the uneven coefficient, the fluctuation of each user's gas usage is analyzed. The closer the uneven coefficient is to 0, the more stable the user's gas usage behavior is; the closer the uneven coefficient is to 1, the greater the fluctuation of the user's gas usage behavior is. Query the uniformity coefficient threshold corresponding to each user from the preset database; If the non-uniformity coefficient exceeds the uniformity coefficient threshold, an alarm instruction is generated and executed.

4. The intelligent dispatching management method for gas according to claim 3 is characterized in that: After the step of generating and executing the alarm instruction, the method further includes: Obtaining user safety hazard information, wherein the user safety hazard information includes gas leakage and equipment aging; Analyze the safety hazard assessment results according to the user safety hazard information; Analyze the user's gas usage behavior changes based on the user's safety hazard information and uneven coefficient; Based on changes in users’ gas usage behavior and safety hazard assessment results, we dynamically adjust users’ classification and weight. For users whose gas usage behavior fluctuates greatly or who have safety hazards, we improve their classification level and priority, and strengthen gas supply guarantees and services.

5. The intelligent dispatching management method for gas according to claim 3 is characterized in that: After the step of obtaining actual gas usage data of each user through the smart gas meter, the method further includes: Extracting key features based on the actual gas usage data, wherein the key features include gas usage volume, gas usage frequency, and gas usage time period; Using time series analysis methods to capture the periodicity and trend characteristics in the actual gas consumption data; Acquire external data, including weather data and holiday data; Analyze the gas usage habits of users using machine learning algorithms based on the key features, periodic and trend features, and external data; Get the minimum storage time from the preset database; Calculate the pre-stored gas volume according to the user's gas usage habits and the minimum pre-stored time; storing gas in advance according to the pre-stored gas amount; If the amount of gas stored in advance is lower than the pre-stored amount of gas, an early warning instruction is generated and executed, and the early warning instruction is used to trigger the gas replenishment process.

6. The intelligent dispatching management method for gas according to claim 5 is characterized in that: After the step of analyzing the user's gas usage habits, the method further includes: According to the gas usage habits of the user, a special time period for gas delivery is determined in advance, and the special time period for gas delivery includes a peak time period and a valley time period; Determine gas demand during off-peak hours; Calculate the actual gas supply required during the off-peak period according to the gas demand during the off-peak period; During off-peak hours, gas is supplied according to the actual gas supply required by users, and gas is pre-input into gas storage facilities for storage; During peak hours, real-time monitoring of gas delivery to see if it can meet the demand during peak hours; If the gas delivery cannot meet the demand during peak hours, the pre-stored gas will be called from the gas storage facilities to replenish the gas in the pipeline.

7. The intelligent dispatching management method for gas according to claim 6 is characterized in that: During peak hours, after the step of real-time monitoring of whether gas delivery can meet the demand during peak hours, it also includes: Retrieve the pipeline identification for peak gas transmission; Obtain the gas pipeline pressure data corresponding to the pipeline identification for peak gas transmission; Obtaining a pipeline pressure threshold value corresponding to a pipeline identifier for peak gas transmission from a preset database; If the gas pipeline pressure data exceeds the pipeline pressure threshold, a pipeline pressure adjustment instruction is generated and executed, and the pipeline pressure adjustment instruction controls the gas flow and the pressure in the pipeline through the pressure regulating device until the gas pipeline pressure data is lower than the pipeline pressure threshold; Call up pre-stored gas from gas storage facilities to replenish the gas in the nearby pipeline.

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