Intelligent scheduling management method of gas

Abstract

The application discloses a kind of intelligent scheduling management methods of gas, belong to the technical field of gas management system, including receiving scheduling information, scheduling information includes the data characteristics of scheduling target point;According to the data characteristics of scheduling target point, using neural network model, the dynamic change of gas demand is predicted and analyzed, and the priority of each scheduling target point is determined;According to the priority of scheduling target point, each scheduling target point is allocated corresponding scheduling time slice;Obtain gas pipe network operation information;According to gas pipe network operation information, the priority of scheduling target point and scheduling time slice, formulate gas emergency scheduling rule, realize the dynamic optimization and adjustment of gas scheduling.The application can dynamically adjust scheduling priority according to real-time situation, ensure that each scheduling target point can obtain timely, reasonable gas distribution.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas management systems, in particular to an intelligent scheduling management method for gas. BACKGROUND

[0002] In recent years, the market size of urban gas in China has continued to expand. With the acceleration of urbanization and the improvement of residents' living standards, natural gas as a clean and efficient energy source has increased in supply and consumption in cities and towns. The industry chain of gas management mainly includes upstream gas source exploration and exploitation, midstream storage and transportation system, and downstream distribution system. With the development of smart gas, products such as intelligent gas meters and remote meter reading systems have emerged in large numbers.

[0003] However, at the present stage, gas scheduling mainly relies on fixed scheduling strategies or simple queue management, such as multi-level feedback queue scheduling algorithm. When dealing with emergency scheduling demand, this kind of method may cause some target points in urgent need of gas to be unable to obtain service for a long time due to fixed rules, affecting the emergency response speed and overall scheduling efficiency. SUMMARY

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

[0005] The intelligent scheduling management method for gas provided by the present application comprises the following steps:

[0006] Receiving scheduling information, the scheduling information comprising data characteristics of scheduling target points;

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

[0008] According to the priority of the scheduling target points, allocating corresponding scheduling time slices to each scheduling target point, so as to ensure that high-priority target points obtain more resources and time in the scheduling process;

[0009] Obtaining gas pipe network operation information, the gas pipe network operation information comprising real-time operation state of the gas pipe network, available gas sources and emergency resource distribution situation;

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

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

[0012] Retrieving user information, including user basic data, transaction records, gas usage behavior data, importance, and urgency;

[0013] Cleaning the collected data to remove duplicates, invalid, or abnormal data;

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

[0015] Using clustering analysis algorithms to analyze the processed user data and dividing users into different categories;

[0016] Evaluating the classification effect according to the similarity within the cluster and the difference between the clusters;

[0017] Adjusting and optimizing the classification results according to business needs;

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

[0019] According to the weight, importance, and urgency of the user, the priority of the dispatch target point is calculated.

[0020] Further, the step of receiving dispatch information also includes:

[0021] Obtaining actual gas usage data for each user through intelligent gas meters;

[0022] According to the actual gas usage data of each user, the non-uniformity coefficient of each user is calculated, which is (max-min) / (max+min); where max and min represent the maximum and minimum values of the user's gas usage within a certain time range;

[0023] According to the result of the non-uniformity coefficient, analyze the fluctuation of each user's gas usage, the closer the non-uniformity coefficient is to 0, the more stable the user's gas usage behavior is; the closer the non-uniformity coefficient is to 1, the greater the fluctuation of the user's gas usage behavior is;

[0024] Querying the uniformity coefficient threshold corresponding to each user from the preset database;

[0025] If the non-uniformity coefficient exceeds the uniformity coefficient threshold, an alarm instruction is generated and executed.

[0026] Further, the step of generating an alarm instruction and executing also includes:

[0027] Obtaining user safety hazard information, including gas leakage, equipment aging;

[0028] According to the user safety hazard information, analyze the safety hazard evaluation result;

[0029] According to the user safety hazard information and the non-uniformity coefficient, analyze the user's gas use behavior change situation;

[0030] According to the user's gas use behavior change and the safety hazard evaluation result, dynamically adjust the user's classification and weight, and for the user with large gas use behavior fluctuation or safety hazard, improve the classification level and priority, and strengthen the gas supply guarantee and service.

[0031] Further, the step of obtaining actual gas use data of each user through the intelligent gas meter further comprises:

[0032] According to the actual gas use data, extract key features, including gas use amount, gas use frequency, and gas use time period;

[0033] Using time series analysis method, capture the periodicity and trend characteristics in the actual gas use data;

[0034] Obtain external data, including weather data and holiday data;

[0035] According to the key features, periodicity and trend characteristics, and external data, analyze the user's gas use habit by using machine learning algorithm;

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

[0037] According to the user's gas use habit and the minimum pre-storage time, calculate the pre-storage gas amount;

[0038] According to the pre-storage gas amount, store gas in advance;

[0039] If the amount of gas stored in advance is less than the pre-storage gas amount, generate a warning instruction and execute it, the warning instruction is used to trigger the gas replenishment process.

[0040] Further, the step of analyzing the user's gas use habit further comprises:

[0041] According to the user's gas use habit, determine the gas delivery special period in advance, including peak period and valley period;

[0042] Determine the gas use demand in the valley period;

[0043] According to the gas use demand in the valley period, calculate the actual gas supply amount needed in the valley period;

[0044] In the valley period, supply gas according to the actual gas supply amount needed by the user, and input gas in advance into the gas storage facility for storage;

[0045] In the peak period, real-time monitoring whether the gas delivery can meet the use demand in the peak period;

[0046] If the gas delivery cannot meet the use demand in the peak period, the pre-stored gas is called from the gas storage facility to supplement the gas amount in the pipeline.

[0047] Further, after the step of real-time monitoring whether the gas delivery can meet the use demand in the peak period in the peak period, the method further comprises:

[0048] Calling the pipeline identifier for peak gas delivery;

[0049] Obtaining the gas pipeline pressure data corresponding to the pipeline identifier for peak gas delivery;

[0050] Obtaining the pipeline pressure threshold corresponding to the pipeline identifier for peak gas delivery from a preset database;

[0051] If the gas pipeline pressure data exceeds the pipeline pressure threshold, a pipeline pressure adjustment instruction is generated and executed, the pipeline pressure adjustment instruction controls the flow of gas 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] Calling the pre-stored gas from the gas storage facility to supplement the gas amount in the pipeline nearby.

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

[0054] The intelligent scheduling management method of gas disclosed in the present application can predict and analyze the dynamic changes of gas demand by using a neural network model. The method can dynamically adjust the priority of each scheduling target point according to actual demand and conditions. Compared with traditional fixed scheduling strategies or simple queue management, the method can more flexibly respond to emergency scheduling demand and ensure that target points in urgent need of gas can obtain services in priority. Dynamic priority adjustment and scheduling time slice allocation enable high-priority target points to obtain more resources and time in the scheduling process, thereby improving the emergency response speed. This is crucial for handling unexpected events or emergency demand and helps to reduce losses and impacts caused by insufficient gas supply.

[0055] By comprehensively considering the real-time operation state of the gas pipe network, available gas sources, and the distribution of emergency resources, the method can formulate more reasonable gas emergency dispatching rules, which helps to optimize resource allocation, ensure efficient use of gas resources, and reduce waste and unnecessary losses. The intelligent dispatching management method can significantly improve the overall dispatching efficiency by dynamically optimizing and adjusting the gas dispatching strategy, which not only improves the stability and reliability of gas supply, but also reduces the dispatching cost and improves the operation benefit. BRIEF DESCRIPTION OF DRAWINGS

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

[0057] Figure 2 is a flowchart of a method for comprehensively calculating the priority of the dispatching target point according to an embodiment of the present application.

[0058] Figure 3 is a flowchart of a method for obtaining actual gas data of each user through an intelligent gas meter according to an embodiment of the present application.

[0059] Figure 4 is a flowchart of a method for obtaining user safety hazard information according to an embodiment of the present application.

[0060] Figure 5 is a flowchart of a method for analyzing user gas usage habits using a machine learning algorithm according to an embodiment of the present application.

[0061] Figure 6 is a flowchart of a method for determining a special gas delivery period in advance according to an embodiment of the present application.

[0062] Figure 7 is a flowchart of a method for obtaining gas pipe pressure data corresponding to the pipeline identification of peak gas delivery according to an embodiment of the present application. DETAILED DESCRIPTION

[0063] The present application will be further described in detail below in conjunction with all the drawings.

[0064] The present application discloses a gas intelligent dispatching management method, referring to Figure 1 A gas intelligent dispatching management method includes:

[0065] S101, receiving dispatching information.

[0066] Specifically, the scheduling management system receives scheduling information, which includes data characteristics of the scheduling 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 geographic location information. Through the establishment of a special data receiving system or interface, these scheduling information is received and stored in real time, providing a basis for subsequent analysis and prediction, ensuring the accuracy and real-time nature of the scheduling information, and providing reliable data support for subsequent steps.

[0067] S102, using a neural network model, predicting and analyzing the dynamic changes of gas demand to determine the priority of each scheduling target point.

[0068] Specifically, the scheduling management system uses a neural network model to predict and analyze the dynamic changes of gas demand based on the data characteristics of the scheduling target points to determine the priority of each scheduling target point. After receiving the scheduling information, the scheduling management system uses the trained neural network model to predict and analyze the dynamic changes of gas demand. The neural network model can learn the rules in historical data to predict the gas demand and its trend of each scheduling target point in the future. According to the prediction results, the priority of each scheduling target point can be determined, and the priority can be determined based on the predicted gas demand, demand urgency, etc. This improves the accuracy and reliability of gas demand prediction and provides a scientific basis for scheduling decisions. By dynamically adjusting the priority, it can ensure that the demand for gas in the case of gas supply shortage is met in priority.

[0069] S103, allocating a corresponding scheduling time slice to each scheduling target point.

[0070] Specifically, the scheduling management system allocates a corresponding scheduling time slice to each target point based on the determined priority of the scheduling target points. The allocation of the scheduling time slice can be differentiated based on the priority, and the target points with high priority can get more scheduling time and resources. The specific setting of the scheduling time slice can be flexibly adjusted according to the actual situation to ensure the fairness and efficiency of the scheduling. By differentiating the allocation of the scheduling time slice, the target points with high priority can get more resources and time in the scheduling process, thereby improving the emergency response speed and overall scheduling efficiency.

[0071] S104, obtaining gas pipeline network operation information.

[0072] Specifically, the scheduling management system obtains gas pipeline network operation information, which includes real-time operation state of the gas pipeline network, available gas sources, and emergency resource distribution; through establishing a special monitoring system and data acquisition equipment, real-time operation state information of the gas pipeline network is obtained, including key parameters such as pressure, flow, and temperature of the pipeline network, and reserves and distribution of available gas sources. Meanwhile, distribution of emergency resources, including emergency repair teams, emergency equipment, and materials, is also collected, ensuring real-time and accuracy of the gas pipeline network operation information, and providing a reliable basis for formulating emergency scheduling rules.

[0073] S105, formulating gas emergency scheduling rules.

[0074] Specifically, the scheduling management system formulates gas emergency scheduling rules according to the gas pipeline network operation information, priority of the scheduling target point, and scheduling time slice, to realize dynamic optimization and adjustment of gas scheduling. According to the obtained gas pipeline network operation information, priority of the scheduling target point, and scheduling time slice, the gas emergency scheduling rules are formulated, including scheduling strategies to be taken under different emergency situations, and deployment schemes of emergency resources, to ensure scientificity and rationality of scheduling decisions. Through formulating the gas emergency scheduling rules, dynamic optimization and adjustment of gas scheduling are realized, and when an emergency situation occurs, the emergency scheduling mechanism can be quickly started to ensure stability and safety of gas supply.

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

[0076] S102.1, calling user information.

[0077] Specifically, the scheduling management system calls basic data of users (such as user ID, name, address, etc.), transaction records (such as historical gas consumption, gas consumption cost, etc.), gas consumption behavior data (such as gas consumption stability, gas consumption time mode, etc.), and importance and emergency degree information of users (such as whether it is a key user, whether there is a special gas consumption demand, etc.) from the database, to obtain comprehensive user information, which provides a basis for subsequent data processing and analysis.

[0078] S102.2, cleaning the collected data to remove repeated, invalid, or abnormal data.

[0079] Specifically, the dispatch management system cleans the collected data, removing duplicates, invalid or abnormal data; identifies and removes duplicate data, such as duplicate user IDs or transaction records. Check and correct invalid data, such as obviously incorrect gas consumption or cost records. Identify and handle abnormal data, such as sudden spikes or drops in gas consumption, which may require interpolation or smoothing. Ensures the accuracy and consistency of the data, avoiding the impact of invalid or abnormal data on subsequent analysis.

[0080] S102.3, standardize the data.

[0081] Specifically, the dispatch management system standardizes the data to ensure that data from different sources and formats can be compared and analyzed; converts and unifies data from different sources and formats, such as converting time formats to a uniform time zone and converting gas consumption units to a uniform standard. Scale or normalize the data to enable comparison and analysis of data from different dimensions on the same scale. Improves the comparability and analysis efficiency of the data, providing convenience for subsequent data analysis.

[0082] S102.4, analyze the processed user data using clustering analysis algorithms to divide users into different categories.

[0083] Specifically, the dispatch management system uses clustering analysis algorithms to analyze the processed user data and divide users into different categories; in this embodiment, the K-means algorithm is used to analyze the processed user data. According to business requirements and data characteristics, set the number of clusters and other parameters, execute the clustering algorithm, and divide users into different categories, achieving user segmentation and classification, which helps to identify the characteristics and needs of different user groups.

[0084] S102.5, evaluate the classification effect according to the similarity within the group and the difference between the groups of the clustering results.

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

[0086] S102.6, adjust and optimize the classification results as necessary.

[0087] Specifically, the dispatch management system adjusts and optimizes the classification results according to business needs; reviews and adjusts the classification results according to business needs. Merging or splitting user categories that do not meet expectations or business needs. Ensures that the classification results meet business needs, 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 dispatch management system sets different weight values for user categories, with the highest weight for key users and the lowest weight for replaceable or interruptible users. Weight setting can be based on business needs, user historical performance, user value and other factors, realizing differentiated setting of user weights and providing an important basis for subsequent priority calculation.

[0090] S102.8, calculate the priority of the dispatch target point.

[0091] Specifically, the dispatch management system calculates the priority of the dispatch target point according to the weight, importance and urgency of the user. According to the weight, importance and urgency information of the user, the priority of each dispatch target point is calculated. Priority calculation can be based on weighted average, linear regression, decision tree and other algorithms, and the priority of each dispatch target point is obtained, providing clear guidance for subsequent dispatch decisions.

[0092] Reference Figure 3 Further, after S101, as another embodiment, the embodiments of the application can further include the following steps:

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

[0094] Specifically, the dispatch management system obtains the actual gas consumption data of each user through the 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 periodically or in real time. The data center obtains the detailed gas consumption record of each user in a specific time period by receiving and processing these data. Real-time or quasi-real-time monitoring of user gas consumption behavior is realized, providing a data basis for subsequent analysis and alarm.

[0095] S202, calculate the non-uniform coefficient of each user.

[0096] Specifically, the scheduling management system calculates the uneven coefficient of each user according to the actual gas consumption data of each user, wherein the uneven coefficient is (max-min) / (max+min); wherein max and min represent the maximum value and the minimum value of the gas consumption of the user within a certain time range; the maximum gas consumption and the minimum gas consumption within a certain time range (such as one day, one week or one month) are calculated according to the actual gas consumption data of each user. The uneven coefficient of each user is calculated using the formula (max-min) / (max+min), which provides an index for quantifying the volatility of user gas consumption, facilitating subsequent analysis and comparison.

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

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

[0099] S204, query the uniform coefficient threshold corresponding to each user from a preset database.

[0100] Specifically, the scheduling management system queries the uniform coefficient threshold corresponding to each user from a preset database; a database containing user uniform coefficient thresholds is preset, wherein each user or user category has a corresponding threshold. According to the user ID or user category, the uniform coefficient threshold corresponding to the user is queried from the database. This realizes 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 uniform coefficient threshold, an alarm instruction is generated and executed.

[0102] Specifically, if the uneven coefficient exceeds the uniform coefficient threshold, the scheduling management system generates an alarm instruction and executes it. When the uneven coefficient of the user exceeds the corresponding uniform coefficient threshold, an alarm instruction is automatically generated. The alarm instruction includes user ID, alarm type, alarm time, uneven coefficient and other information, and is sent to relevant management personnel or system. According to business needs, corresponding alarm processing measures are executed, such as dispatching staff to conduct on-site inspection, communicating with the user, etc. The abnormal situation of the user's gas consumption behavior is discovered in time, and corresponding processing measures are taken to avoid potential safety hazards or economic losses, improving the operation efficiency and user satisfaction of the gas company.

[0103] Reference Figure 4Further, after S205, as another embodiment, the embodiment of the present application can further include the following steps:

[0104] S301, acquire user security risk information.

[0105] Specifically, the dispatch management system acquires user security risk information, which includes gas leakage and equipment aging. Through intelligent gas meters, gas leakage detectors, equipment aging detection systems and other tools and technical means, the user's security risk information is collected in real time or periodically. The security risk information includes gas leakage, equipment aging, and illegal use of gas. The user's security risk information is obtained in a timely manner, providing data support for subsequent analysis and evaluation.

[0106] S302, analyze the security risk evaluation result.

[0107] Specifically, the dispatch management system analyzes the security risk evaluation result according to the user security risk information; the collected security risk information is classified, sorted and evaluated to determine the severity of the risk and the possible consequences. According to the evaluation result, corresponding risk treatment measures and emergency plans are formulated, thereby providing quantitative evaluation of security risks, helping to identify and handle high-risk users or areas, formulating targeted risk treatment measures, and improving the efficiency and effectiveness of risk treatment.

[0108] S303, analyze the user's gas behavior change situation.

[0109] Specifically, the dispatch management system analyzes the user's gas behavior change situation in combination with the user's non-uniform coefficient and security risk information. It focuses on users with large fluctuations in gas behavior or security risks, analyzes the change trend and reason of their gas behavior, thereby providing in-depth understanding of the user's gas behavior change, helping to identify potential problem users or abnormal gas behavior, and providing a basis for subsequent user classification and weight adjustment.

[0110] S304, dynamically adjust the classification and weight of the user.

[0111] Specifically, the dispatch management system dynamically adjusts the classification and weight of the user according to the user's gas behavior change and security risk evaluation result. For users with large fluctuations in gas behavior or security risks, increase their classification level and priority to strengthen gas supply guarantee and service. For users with stable gas behavior and less security risks, their classification level and priority can be appropriately reduced to optimize resource allocation. The dynamic adjustment of user classification and weight is realized, the pertinence and efficiency of gas supply guarantee and service are improved, and it is helpful to timely discover and handle potential problem users or areas, and reduce security risks and operational risks.

[0112] Refer toFigure 5 Further, after S201, as another embodiment, the embodiment of the present application can further include the following steps:

[0113] S401, extracting key features.

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

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

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

[0117] S403, obtaining external data.

[0118] Specifically, the dispatch management system obtains external data, including weather data and holiday data; weather data and holiday data and other information are obtained from reliable external data sources. Weather data can include temperature, humidity, and other factors that may affect user gas demand; holiday data can reflect changes in user gas consumption behavior due to holidays. External data provides additional dimensions for analyzing user gas habits, helping to better understand user gas behavior.

[0119] S404, using machine learning algorithm to analyze user's gas 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 input to train and analyze using machine learning algorithms such as logistic regression, decision tree, etc. Through machine learning algorithms, the association between user gas behavior and various factors can be mined, forming a user's gas usage habit model that accurately reflects the user's gas behavior characteristics, providing a reliable basis for subsequent calculation of pre-stored gas and advance storage of gas.

[0121] S405, obtaining the minimum pre-storage time from the preset database.

[0122] Specifically, the scheduling management system obtains the minimum pre-storage time from the preset database; the minimum pre-storage time is usually determined according to the operation strategy of the gas company and the demand of the user. The minimum pre-storage time provides a time limit for the user to store gas in advance, which helps to ensure that the user always has enough gas supply.

[0123] S406, calculating the pre-stored gas amount.

[0124] Specifically, the scheduling management system calculates the pre-stored gas amount according to 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 amount required by the user in the future is calculated through a certain algorithm (such as time series prediction model). The calculation of pre-stored gas amount can provide reasonable gas storage suggestions for users to ensure that users will not be interrupted due to insufficient gas during use.

[0125] S407, storing gas in advance.

[0126] Specifically, the scheduling management system stores gas in advance according to the pre-stored gas amount; according to the calculated pre-stored gas amount, the gas company or the user can store gas in advance, involving adjusting the gas supply system, increasing gas storage facilities, etc. Pre-stored gas can ensure that users have enough gas supply during peak gas usage period or special circumstances, and improve the user's gas experience.

[0127] S408, if the amount of gas stored in advance is less than the pre-stored gas amount, a warning instruction is generated and executed.

[0128] Specifically, if the advance storage gas amount is less than the pre-stored gas amount, the dispatch management system generates a warning instruction and executes it. The warning instruction is used to trigger the gas replenishment process. When the advance storage gas amount is less than the pre-stored gas amount, the system automatically generates a warning instruction. The warning instruction triggers the gas replenishment process, including notifying the user or gas company, adjusting the gas supply plan, and other measures. By generating a warning instruction and executing the gas replenishment process, the problem of insufficient gas can be discovered and handled in a timely manner, avoiding the impact on users due to gas shortage.

[0129] With reference to Figure 6 Further, after S404, as another embodiment, the embodiment of the application can further include the following steps:

[0130] S501, determine the special gas delivery period in advance.

[0131] Specifically, the dispatch management system determines the special gas delivery period in advance according to the user's gas usage habits, which includes peak period and valley period; based on the analysis of user's gas usage habits, time series analysis, machine learning model and other tools are used to identify the peak period and valley period of user's gas usage. These periods may vary due to factors such as season, weather, holidays, etc. Peak period is usually the time period with the highest gas demand, such as meal time and heating period in winter; valley period is the time period with relatively low gas demand, such as late night or daytime work period. Determining the special period helps the gas company to arrange the gas delivery and storage plan more reasonably, and improves the efficiency and stability of gas supply.

[0132] S502, determine the gas demand in the valley period.

[0133] Specifically, the dispatch management system determines the gas demand in the valley period; after identifying the valley period, the gas demand in the valley period is predicted according to historical data and user gas usage habit model. This needs to consider various factors such as weather changes, holiday effects, etc. Through data analysis, the average gas consumption per hour or per minute in the valley period is obtained as the actual gas demand of the period. Accurate prediction of gas demand in the valley period helps the gas company to develop more accurate supply plan, avoiding over-supply or insufficient supply.

[0134] S503, calculate the actual gas supply amount needed in the valley period.

[0135] Specifically, the scheduling management system calculates the actual gas supply amount required during the valley period based on the gas demand during the valley period. It also calculates the actual gas supply amount required during the valley period by considering factors such as the capacity and efficiency of the gas transmission pipeline. This takes into account the loss during gas transmission and changes in pipeline pressure to ensure that the supplied gas amount meets the actual demand. By accurately calculating the gas supply amount during the valley period, the gas company can provide sufficient gas supply at the lowest cost during the valley period.

[0136] S504, during the valley period, gas supply is carried out according to the actual gas supply amount required by the user, and gas is pre-input into the gas storage facility for storage.

[0137] Specifically, the scheduling management system carries out gas supply according to the actual gas supply amount required by the user during the valley period, and pre-inputs gas into the gas storage facility for storage. During the valley period, the flow of the gas transmission pipeline is adjusted according to the calculated actual gas demand to ensure that the user obtains sufficient gas supply. At the same time, the gas storage facility (such as gas tank, underground gas storage, etc.) is used to pre-input gas for storage during the valley period for use during the peak period. This helps to balance gas supply and demand, reduce the pressure of gas transmission during the peak period, and improve the utilization rate of the gas storage facility.

[0138] S505, during the peak period, real-time monitoring is carried out to determine whether the gas transmission can meet the demand during the peak period.

[0139] Specifically, the scheduling management system carries out real-time monitoring to determine whether the gas transmission can meet the demand during the peak period during the peak period. During the peak period, the real-time monitoring system monitors the pressure and flow of the gas transmission pipeline to ensure that the gas transmission can meet the demand during the peak period. If it is found that the gas transmission cannot meet the demand during the peak period, an emergency plan is immediately started, such as calling pre-stored gas from the gas storage facility. The real-time monitoring system helps to timely discover and respond to the situation of insufficient gas supply, ensuring that the user obtains stable gas supply during the peak period.

[0140] S506, if the gas transmission cannot meet the demand during the peak period, pre-stored gas from the gas storage facility is called to supplement the amount of gas in the pipeline.

[0141] Specifically, if the gas delivery cannot meet the demand during peak hours, the dispatching management system calls for pre-stored gas from the gas storage facility to supplement the amount of gas in the pipeline. When it is found that the gas delivery cannot meet the demand during peak hours, the release mechanism of the gas storage facility is immediately started to release the pre-stored gas into the delivery pipeline to supplement the amount of gas in the pipeline. At the same time, by adjusting the flow rate and pressure of the delivery pipeline and other parameters, it is ensured that the gas can be stably and continuously delivered to the user end. By calling for pre-stored gas from the gas storage facility, the gas demand during peak hours can be quickly responded to, and it is ensured that the user obtains sufficient gas supply. At the same time, this step also helps to improve the emergency response capability and service level of the gas company.

[0142] With reference to Figure 7 Further, after S505, as another embodiment, the embodiment of the application can further include the following steps:

[0143] S601, call the pipeline identification for peak gas delivery.

[0144] Specifically, the dispatching management system calls the pipeline identification for peak gas delivery; during the monitoring process in the peak hours, when it is found that the gas delivery cannot meet the demand during peak hours, first, according to the layout of the gas delivery system and the gas demand during peak hours, the pipelines that need to deliver gas during peak hours are determined. The identification information of these pipelines, including pipeline number, position, length, diameter and other key parameters, is called from the database of the gas delivery system. Accurate calling of the identification information of the peak gas delivery pipeline provides basic data for subsequent pressure monitoring and adjustment, and ensures the stable supply of gas during peak hours.

[0145] S602, obtain the gas pipeline pressure data corresponding to the pipeline identification for peak gas delivery.

[0146] Specifically, the dispatching management system obtains the gas pipeline pressure data corresponding to the pipeline identification for peak gas delivery; the pressure sensor installed on the peak gas delivery pipeline is used to monitor and record the gas pressure data in the pipeline in real time. These pressure data are associated with the pipeline identification information to ensure that the pressure condition of each pipeline can be accurately tracked. Real-time acquisition of the pressure data of the peak gas delivery pipeline helps to timely discover and respond to abnormal pressure conditions, and ensures the safe operation of the gas delivery system.

[0147] S603, obtain the pipeline pressure threshold corresponding to the pipeline identification for peak gas delivery from the preset database.

[0148] Specifically, the dispatch management system obtains the pipeline pressure threshold corresponding to the pipeline identifier for peak gas transmission from the preset database; according to the material, diameter, wall thickness and other parameters of the peak gas transmission pipeline, and the design requirements of the gas transmission system, the pressure threshold of each pipeline is obtained from the preset database. These pressure thresholds usually 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 excessively high or low pressure.

[0149] S604, if the gas pipeline pressure data exceeds the pipeline pressure threshold, a pipeline pressure regulation instruction is generated and executed.

[0150] Specifically, if the gas pipeline pressure data exceeds the pipeline pressure threshold, the dispatch management system generates a pipeline pressure regulation instruction and executes it, and the pipeline pressure regulation instruction controls the flow of gas and the pressure in the pipeline through a pressure regulating device until the gas pipeline pressure data is below the pipeline pressure threshold; when the real-time monitored gas pipeline pressure data exceeds the preset pressure threshold, the system automatically generates a pipeline pressure regulation instruction. These instructions control the flow of gas and the pressure in the pipeline through pressure regulating devices (such as pressure reducing valves, throttling valves, etc.), gradually reduce the gas pressure in the pipeline until it is below the pressure threshold, timely adjust the pipeline pressure, prevent safety accidents such as pipeline rupture and leakage caused by excessively high pressure, and ensure stable operation of the gas transmission system.

[0151] S605, calling pre-stored gas from the gas storage facility to supplement the amount of gas in the pipeline.

[0152] Specifically, the dispatch management system calls pre-stored gas from the gas storage facility to supplement the amount of gas in the pipeline. While regulating the pipeline pressure, pre-stored gas is called from the gas storage facility according to the gas demand during peak periods and the pipeline pressure condition. By optimizing the gas transmission path and dispatch strategy, the amount of gas in the pipeline is supplemented nearby to ensure that the gas pipeline does not always operate at a high pressure, meet the gas demand during peak periods. Timely supplementing the amount of gas in the pipeline ensures stable gas supply for users during peak periods. At the same time, by optimizing the dispatch strategy, the amount of gas is supplemented nearby, the transmission pressure of the gas pipeline is reduced, and the gas utilization efficiency is improved.

[0153] The embodiment of the application also discloses an intelligent terminal comprising a memory and a processor, wherein the memory stores a computer program capable of being loaded and executed by the processor to execute the intelligent dispatch management method of the gas as described above.

[0154] The embodiment of the present application further discloses a computer readable storage medium. The computer readable storage medium stores a computer program capable of being loaded by a processor and executing the intelligent scheduling management method of the gas as described above, and the computer readable storage medium includes, for example, a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

[0155] The above embodiments are only used to illustrate the technical solutions of the present application, and are not used to limit the protection scope of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative work belong to the scope to be protected by the present application. Although the present application is described in detail with reference to the above embodiments, those skilled in the art can still combine, add or delete the features in the embodiments of the present application according to the circumstances without creative work, so as to obtain different other technical solutions which do not deviate from the concept of the present application in essence, and these technical solutions also belong to the scope to be protected by the present application.

Claims

1. A method for intelligent scheduling management of gas, characterized in that, The method comprises the following steps: receiving scheduling information, the scheduling information comprising data characteristics of scheduling target points; using a neural network model to predict and analyze the dynamic changes of gas demand according to the data characteristics of the scheduling target points, and determining the priority of each scheduling target point; specifically comprising: calling user information, the user information comprising user basic data, transaction records, gas usage behavior data, importance and urgency; cleaning the collected data to remove duplicate, invalid or abnormal data; standardizing the data to ensure that data of different sources and formats can be compared and analyzed; using a clustering analysis algorithm to analyze the processed user data and dividing the users into different categories; evaluating the classification effect according to the similarity within the clusters and the difference between the clusters; according to the business requirements, adjusting and optimizing the classification results as necessary; setting different weights for each user category, with the weight of the key user being the highest and the weight of the replaceable or interruptible user being the lowest; comprehensively calculating the priority of the scheduling target point according to the weight, importance and urgency of the user; allocating a corresponding scheduling time slice to each scheduling target point according to the priority of the scheduling target point, to ensure that high-priority target points obtain more resources and time in the scheduling process; obtaining gas pipeline network operation information, the gas pipeline network operation information comprising the real-time operation state of the gas pipeline network, available gas sources and emergency resource distribution; formulating gas emergency scheduling rules according to the gas pipeline network operation information, the priority of the scheduling target point and the scheduling time slice, to realize dynamic optimization and adjustment of gas scheduling.

2. The intelligent scheduling management method of gas according to claim 1, characterized in that, After the step of receiving scheduling information, the method further comprises: obtaining actual gas usage data of each user through an intelligent gas meter; correspondingly calculating a non-uniformity coefficient of each user according to the actual gas usage data of each user, the non-uniformity coefficient being (max-min) / (max+min), wherein max and min represent the maximum and minimum values of the user's gas usage within a certain time range; analyzing the fluctuation of the gas usage of each user according to the result of the non-uniformity coefficient, wherein the closer the non-uniformity coefficient is to 0, the more stable the user's gas usage behavior is; the closer the non-uniformity coefficient is to 1, the greater the fluctuation of the user's gas usage behavior is; querying a uniformity coefficient threshold corresponding to each user from a preset database; if the non-uniformity coefficient exceeds the uniformity coefficient threshold, generating an alarm instruction and executing it.

3. The intelligent scheduling management method of gas according to claim 2, characterized in that, After the step of generating an alarm instruction and executing it, the method further comprises: obtaining user safety hazard information, the user safety hazard information comprising gas leakage and equipment aging; analyzing the safety hazard evaluation result according to the user safety hazard information; analyzing the change situation of the user's gas usage behavior according to the user safety hazard information and the non-uniformity coefficient; dynamically adjusting the classification and weight of the user according to the change of the user's gas usage behavior and the safety hazard evaluation result, increasing the classification level and priority of the user whose gas usage behavior fluctuates greatly or has safety hazards, and strengthening the gas supply guarantee and service.

4. The intelligent scheduling management method of gas according to claim 2, characterized in that, After the step of obtaining actual gas usage data of each user through an intelligent gas meter, the method further comprises: According to the actual gas data, key features are extracted, including gas consumption, gas consumption frequency, and gas consumption time period; Using time series analysis method, periodic and trend features in the actual gas data are captured; External data, including weather data and holiday data, are obtained; According to the key features, periodic and trend features, and external data, user's gas usage habits are analyzed by using machine learning algorithm; The minimum pre-storage time is obtained from the preset database; According to the user's gas usage habits and the minimum pre-storage time, the pre-storage gas amount is calculated; According to the pre-storage gas amount, gas is stored in advance; If the amount of gas stored in advance is less than the pre-storage gas amount, a warning instruction is generated and executed, which is used to trigger the gas replenishment process.

5. The intelligent scheduling management method of gas according to claim 4, characterized in that, After the step of analyzing the user's gas usage habits, the method further comprises: According to the user's gas usage habits, gas delivery special time periods, including peak time period and valley time period, are determined in advance; The gas demand in the valley time period is determined; According to the gas demand in the valley time period, the actual gas supply amount required in the valley time period is calculated; In the valley time period, gas is supplied according to the actual gas supply amount required by the user, and gas is pre-input into the gas storage facility for storage; In the peak time period, it is monitored in real time whether the gas delivery can meet the usage demand in the peak time period; If the gas delivery cannot meet the usage demand in the peak time period, the pre-stored gas in the gas storage facility is called to supplement the gas amount in the pipeline.

6. The intelligent scheduling management method of gas according to claim 5, characterized in that, After the step of monitoring in real time whether the gas delivery can meet the usage demand in the peak time period, the method further comprises: The pipeline identifier for peak gas delivery is retrieved; The gas pipeline pressure data corresponding to the pipeline identifier for peak gas delivery is obtained; The pipeline pressure threshold value corresponding to the pipeline identifier for peak gas delivery is obtained from the preset database; If the gas pipeline pressure data exceeds the pipeline pressure threshold value, a pipeline pressure adjustment instruction is generated and executed, which controls the flow of gas and the pressure in the pipeline through a pressure regulating device until the gas pipeline pressure data is lower than the pipeline pressure threshold value; The pre-stored gas in the gas storage facility is called to supplement the gas amount in the pipeline.

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