Gas resource scheduling method based on intelligent gas call center and internet of things system

Abstract

The embodiment of the present specification provides a kind of gas resource scheduling method and Internet of Things system based on wisdom gas call center, the method is realized by the gas resource scheduling Internet of Things system based on wisdom gas call center, comprising: obtaining the gas use data of different types of gas users, and determining gas use characteristics;Obtain gas demand data;Based on gas use characteristics, gas demand data and the gas maintenance data of wisdom gas call center, predict whether the gas supply of multiple second times meets the gas demand;In response to the gas demand that the gas supply of at least one second time in multiple second times cannot meet, adjust gas allocation scheme.

Description

Technical Field

[0001] This specification relates to the field of gas resource dispatching, and in particular to a gas resource dispatching method and Internet of Things system based on a smart gas call center. Background Technology

[0002] With the widespread use of natural gas, gas transmission and distribution networks have covered all parts of cities. In actual operation, these networks may experience problems such as imbalances between gas supply and demand, and gas emergencies, affecting normal gas supply. Therefore, this paper aims to provide a gas resource scheduling method and an IoT system based on a smart gas call center to offer reasonable gas scheduling solutions, balance gas supply and demand, and ensure normal gas supply. Summary of the Invention

[0003] This invention provides a gas resource scheduling method based on a smart gas call center, comprising: acquiring gas usage data of different types of gas users and determining gas usage characteristics, the gas usage characteristics including at least the gas usage data of different types of gas users at multiple first times; acquiring gas demand data, the gas demand data including demand time and demand quantity; predicting whether gas supply at multiple second times will meet gas demand based on gas usage characteristics, gas demand data, and gas maintenance data from the smart gas call center; and adjusting the gas allocation plan in response to at least one of the multiple second times where gas supply cannot meet gas demand.

[0004] This invention provides a gas resource dispatching system based on a smart gas call center. The IoT system for gas resource dispatching based on the smart gas call center includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform includes at least a smart operation management sub-platform and a smart gas data center. The smart gas data center is used to acquire gas usage data and gas demand data from different types of gas users and send these data to the smart operation management sub-platform for processing. The gas demand data includes demand time and demand quantity. The smart operation management sub-platform is configured to perform the following operations: determine gas usage characteristics based on gas usage data, which at least includes gas usage data from different types of gas users at multiple first times; predict whether gas supply at multiple second times will meet gas demand based on gas usage characteristics, gas demand data, and gas maintenance data from the smart gas call center; adjust the gas allocation plan in response to at least one second time where gas supply cannot meet gas demand; and send the adjusted gas allocation plan to the smart gas data center and then to the smart gas user platform via the smart gas service platform.

[0005] This specification provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the gas resource scheduling method based on a smart gas call center as described above. Attached Figure Description

[0006] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0007] Figure 1 This is a schematic diagram of the platform structure of a gas resource dispatching Internet of Things system based on a smart gas call center, according to some embodiments of this specification.

[0008] Figure 2 This is an exemplary flowchart of a gas resource scheduling method based on a smart gas call center, according to some embodiments of this specification.

[0009] Figure 3 These are exemplary schematic diagrams of prediction models shown according to some embodiments of this specification;

[0010] Figure 4 This is an exemplary flowchart illustrating the adjustment of a gas distribution scheme according to some embodiments of this specification;

[0011] Figure 5 This is an exemplary schematic diagram illustrating the adjustment of a gas distribution scheme according to some embodiments of this specification. Detailed Implementation

[0012] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0013] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0014] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0015] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0016] With the widespread use of natural gas, gas transmission and distribution networks have covered all parts of cities. In actual operation, these networks may experience problems such as imbalances between gas supply and demand, and gas emergencies, which can affect normal gas supply.

[0017] In view of this, some embodiments of this specification provide a gas resource scheduling method and Internet of Things system based on a smart gas call center. By predicting whether the gas supply of gas pipelines will meet the gas demand in the future, and providing reasonable gas scheduling schemes according to the user's needs and importance, the impact of insufficient gas supply on gas users can be reduced, user satisfaction can be improved, the demand for gas supply can be balanced, and normal gas supply can be guaranteed.

[0018] Figure 1 This is a schematic diagram of the platform structure of a gas resource dispatching Internet of Things system based on a smart gas call center, according to some embodiments of this specification.

[0019] In some embodiments, such as Figure 1 As shown, the gas resource scheduling IoT system 100 based on the smart gas call center may include a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially.

[0020] A smart gas user platform can be a platform for interacting with users. Users can be gas users, regulatory users, etc. In some embodiments, the smart gas user platform can be configured as a terminal device, for example, a mobile device, a tablet computer, or any combination thereof. In some embodiments, the smart gas user platform can be used to provide information feedback to users. For example, the smart gas user platform can be used to provide gas dispatch management information to users.

[0021] In some embodiments, the smart gas user platform includes a gas user sub-platform, a government user sub-platform, and a regulatory user sub-platform. The gas user sub-platform is for gas users, providing them with gas usage data and solutions to gas-related problems. A gas user is someone who uses gas. In some embodiments, the gas user sub-platform can interact with and correspond to the smart gas service sub-platform to obtain safe gas usage services. The government user sub-platform is for government users, providing them with gas operation-related data. Government users are those in government departments related to gas operations. In some embodiments, the government user sub-platform can interact with and correspond to the smart operation service sub-platform. For example, the government user sub-platform can issue query instructions regarding gas dispatch management information to the smart operation service sub-platform. Alternatively, the government user sub-platform can obtain gas dispatch management information (such as gas reserves and gas dispatch) uploaded by the smart operation service sub-platform. The regulatory user sub-platform is for regulatory users, overseeing the operation of the entire IoT system. Regulatory users are those in safety departments. In some embodiments, the regulatory user sub-platform can interact with and correspond to the smart regulatory service sub-platform to obtain services related to safety supervision needs. In some embodiments, the smart gas user platform can interact bidirectionally with the smart gas service platform, sending feedback information from gas users to the smart gas service sub-platform and receiving customer service feedback information uploaded by the smart gas service sub-platform; it can also send gas dispatch management information query instructions to the smart operation service sub-platform and receive gas dispatch management information uploaded by the smart operation service sub-platform.

[0022] A smart gas service platform can be a platform for receiving and transmitting data and / or information. In some embodiments, the smart gas service platform can interact downwards with a smart gas management platform, sending gas dispatch management information query instructions to the smart gas data center and receiving gas dispatch management information uploaded by the smart gas data center. In some embodiments, the smart gas service platform can interact upwards with a smart gas user platform. In some embodiments, the smart gas service platform is equipped with a smart gas consumption service sub-platform, a smart operation service sub-platform, and a smart regulatory service sub-platform. The smart gas consumption service sub-platform can interact with the gas user sub-platform to provide gas users with gas consumption information. The smart operation service sub-platform can interact with the government user sub-platform, receiving gas dispatch management information query instructions sent by the government user sub-platform and uploading gas dispatch management information to the government user sub-platform. The smart regulatory service sub-platform can interact with the regulatory user sub-platform to provide regulatory information to regulatory users.

[0023] A smart gas management platform can refer to a platform that coordinates and integrates the connections and collaboration between various functional platforms, gathers all the information from the Internet of Things (IoT), and provides sensing, management, and control functions for the IoT operating system. In some embodiments, the smart gas management platform can interact downwards with a smart gas sensor network platform. For example, it can send instructions to the smart gas sensor network platform to obtain relevant data about gas equipment and receive relevant data about gas equipment (e.g., gas usage data and gas demand data) uploaded by the smart gas sensor network platform; it can also interact upwards with a smart gas service platform, receiving query instructions for gas dispatch management information issued by the smart gas service platform and uploading gas dispatch management information to the smart gas service platform.

[0024] In some embodiments, the smart gas management platform includes a smart customer service management sub-platform, a smart operation management sub-platform, and a smart gas data center. Each management sub-platform can interact bidirectionally with the smart gas data center, which aggregates and stores all operational data from the gas resource scheduling IoT system 100 based on the smart gas call center. In some embodiments, the smart gas management platform can interact with the smart gas sensor network platform and the smart gas service platform through the smart gas data center. For example, the smart gas data center can receive gas dispatch management information query commands issued by the smart operation service sub-platform, and receive customer feedback information issued by the smart gas service sub-platform, sending the customer feedback information to the smart customer service management sub-platform for processing. Similarly, the smart gas data center can receive gas equipment-related data uploaded by the smart gas sensor network platform and send the gas equipment-related data to the smart operation management sub-platform for processing. In some embodiments, the smart customer service management sub-platform can be used for customer analysis and management, including viewing customer feedback information and providing corresponding responses. In some embodiments, the intelligent operation management sub-platform can be used to realize gas reserve management, gas usage scheduling management, and pipeline project management, and can view pipeline project work order information, personnel configuration, and progress to realize pipeline project management, etc.

[0025] A smart gas sensor network platform can be a functional platform for managing sensor communication. In some embodiments, the smart gas sensor network platform can be configured as a communication network and gateway to realize functions such as network management, protocol management, command management, and data parsing. In some embodiments, the smart gas sensor network platform can interact with a smart gas management platform and a smart gas object platform to realize the functions of sensing communication for perception information and sensing communication for control information. For example, the smart gas sensor network platform can send instructions to the smart gas object platform to obtain relevant data of gas equipment, and receive relevant data of gas equipment uploaded by the smart gas object platform. As another example, the smart gas sensor network platform can receive instructions from the smart gas data center to obtain relevant data of gas equipment, and upload relevant data of gas equipment to the smart gas data center.

[0026] In some embodiments, the intelligent gas sensor network platform may include a sub-platform for indoor gas equipment sensing networks and a sub-platform for gas pipeline equipment sensing networks. The indoor gas equipment sensing network sub-platform may correspond to the indoor gas equipment object sub-platform and is used to acquire relevant data from indoor equipment (e.g., metering equipment). The gas pipeline equipment sensing network sub-platform may correspond to the gas pipeline equipment object sub-platform and is used to acquire relevant data from pipeline equipment (e.g., gas gate compressors, pressure regulating equipment, gas flow meters, valve control equipment, thermometers, barometers, etc.) (all of which belong to gas equipment-related data).

[0027] A smart gas object platform can be a functional platform for generating sensing information and executing control information. In some embodiments, the smart gas object platform can be configured to include at least one gas device and at least one other device. The gas device may include indoor equipment and pipeline equipment. Other devices may include monitoring equipment, temperature sensors, pressure sensors, etc. In some embodiments, the smart gas object platform can interact with a smart gas sensor network platform, receiving instructions from the smart gas sensor network platform to acquire data related to the gas device, and uploading relevant data from the gas device to the smart gas sensor network platform.

[0028] In some embodiments, the smart gas target platform may include a sub-platform for indoor gas equipment and a sub-platform for gas pipeline equipment. The indoor gas equipment sub-platform corresponds to the indoor gas equipment sensor network sub-platform and is used to upload relevant data of the indoor equipment to the smart gas data center via the indoor gas equipment sensor network sub-platform. Similarly, the gas pipeline equipment sub-platform corresponds to the gas pipeline equipment sensor network sub-platform and is used to upload relevant data of the pipeline equipment to the smart gas data center via the gas pipeline equipment sensor network sub-platform.

[0029] It should be noted that the above description of the system and its components is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various components or construct subsystems connected to other components without departing from these principles. For example, a smart gas service platform and a smart gas management platform can be integrated into one component. As another example, the various components can share a storage device, or each component can have its own separate storage device. Such variations are all within the scope of this specification.

[0030] Figure 2 This is an exemplary flowchart illustrating a gas resource scheduling method based on a smart gas call center, according to some embodiments of this specification. In some embodiments, process 200 may be executed by a smart gas management platform. Figure 2 As shown, process 200 includes the following steps.

[0031] Step S210: Obtain gas usage data for different types of gas users and determine gas usage characteristics.

[0032] Different types of gas users can include residential users, commercial users (e.g., gas stations, natural gas power plants, etc.), and industrial users (e.g., factories that require gas). In some embodiments, the type of gas user can be determined based on the supply address of the corresponding gas pipeline. For example, a gas pipeline with a supply address in a residential area can be classified as a residential user. It should be noted that different types of gas users can correspond to different types of gas pipelines. For example, residential users correspond to residential pipelines, commercial users to commercial pipelines, and industrial users to industrial pipelines. A gas pipeline can be used to supply gas to one or more gas users of the same type. For example, residential pipeline A can be used to supply gas to 200 residential users.

[0033] Gas usage data refers to data related to gas usage. Each type of gas user corresponds to one set of gas usage data.

[0034] In some embodiments, gas usage data may include gas consumption, gas usage time, etc., for a certain type of gas user. For example, gas usage data for a certain type of gas user may be (x, y), where x represents the gas consumption of that type of gas user and y represents the gas usage time of that type of gas user.

[0035] In some embodiments, gas usage data may further include the gas consumption and usage time of each of the multiple gas pipelines corresponding to a certain type of gas user. For example, the gas usage data of a certain type of gas user may be ([x1, y1], [x2, y2], ...), where x1 represents the gas consumption of gas pipeline 1, y1 represents the gas usage time of gas pipeline 1, x2 represents the gas consumption of gas pipeline 2, y2 represents the gas usage time of gas pipeline 2, etc.

[0036] In some embodiments, gas usage data can be obtained through a smart gas object platform. For example, gas usage data can be obtained through devices such as gas meters.

[0037] Gas usage characteristics refer to features related to gas usage data for each gas pipeline. For example, gas usage characteristics could be features related to the amount of gas used in a particular gas pipeline.

[0038] In some embodiments, gas usage characteristics include at least gas usage data for different types of gas users at multiple times.

[0039] The first time refers to a specific point in time or period during which a gas user historically used gas. The first time can be determined in several ways. For example, a preset time length can be used to divide a day into multiple time periods, resulting in multiple first times. Alternatively, a time point can be selected at regular intervals as the first time. In some embodiments, the first time can be adjusted based on factors such as peak and off-peak gas consumption periods and seasons. For example, the first time during peak gas consumption periods can be longer or more frequent. Similarly, the first time during winter can be longer or more frequent.

[0040] Gas usage characteristics can be represented by vectors. These characteristics include sub-usage characteristics corresponding to each gas pipeline. For example, gas usage characteristics can be represented as ([a1, b1], [a2, b2], ...), where [a1, b1] is the sub-usage characteristic of gas pipeline 1, [a2, b2] is the sub-usage characteristic of gas pipeline 2, and so on; a1, a2, etc., represent multiple first times; b1 represents the gas consumption of gas pipeline 1 at first time a1, b2 represents the gas consumption of gas pipeline 2 at first time a2, and so on.

[0041] In some embodiments, gas usage characteristics can be determined by integrating and analyzing gas usage data from one or more gas users corresponding to a specific gas pipeline. For example, gas usage data from one or more gas users corresponding to a specific gas pipeline can be integrated, and feature extraction can be performed on the integrated gas usage data to determine gas usage characteristics. As an example, the gas usage data of residential user A includes gas usage time from 07:00 to 08:00 and gas consumption of 0.2 m³. 3 Residential user B's gas usage data includes gas usage time from 07:00 to 08:00 and gas consumption of 0.1m³. 3 Residential user C's gas usage data includes gas usage time from 11:00 to 12:00 and gas consumption of 0.2 m³. 3 Among them, residential users A and B correspond to gas pipeline 1, and residential user C corresponds to gas pipeline 2. By integrating and extracting the gas usage data of each residential user in each gas pipeline, the gas usage characteristics can be determined as ([07:00-08:00, 0.3], [11:00-12:00, 0.2]), where [07:00-08:00, 0.3] represents the gas usage characteristics of gas pipeline 1, and [11:00-12:00, 0.2] represents the gas usage characteristics of gas pipeline 2.

[0042] Step S220: Obtain gas demand data.

[0043] Gas demand data refers to data related to gas usage. In some embodiments, gas demand data may include demand time and demand quantity. Different gas pipelines may correspond to different gas demand data.

[0044] Demand time refers to the time when a gas user needs to use gas. Demand time can be a future time after the current time. In some embodiments, the demand time may differ for different types of gas users.

[0045] Demand (also known as gas demand) refers to the amount of gas that a gas user needs to use. In some embodiments, the demand of different types of gas users may differ.

[0046] Gas demand data can be obtained in various ways. For example, it can be obtained from data entered by one or more gas users corresponding to a specific gas pipeline in the gas user sub-platform.

[0047] Step S230: Based on gas usage characteristics, gas demand data, and gas maintenance data from the smart gas call center, predict whether the gas supply at multiple second-time intervals will meet the gas demand.

[0048] A smart gas call center is a service center that provides gas-related services to gas users. For example, gas users can use a smart gas call center to obtain services such as activation, consultation, and reporting gas-related repairs.

[0049] The gas maintenance data in a smart gas call center refers to data related to the maintenance and handling of gas-related facilities (such as gas pipelines and gas equipment). When a gas pipeline is under maintenance, the gas supply to that pipeline can be stopped.

[0050] In some embodiments, the gas maintenance data of a smart gas call center may include the estimated start time of maintenance, the estimated time of restoration of gas supply, the current maintenance work order, and the expected maintenance work order.

[0051] The estimated start time for maintenance refers to the time when maintenance on the gas facilities is expected to begin.

[0052] The estimated start time for maintenance can be determined in several ways. For example, it can be determined based on the estimated time of arrival at the gas-related facility where the malfunction or damage has occurred, and the current time.

[0053] The estimated gas supply restoration time refers to the time when gas facilities will be repaired and gas supply may be restored. The estimated gas supply restoration time can be determined in several ways. For example, it can be determined based on the extent of damage to the gas-related facilities and the estimated start time of repairs. Alternatively, it can be determined by comparing the extent of damage with a first preset table. The estimated gas supply restoration time is determined based on the repair time and the estimated start time of repairs. The first preset table contains various reference damage levels and their corresponding reference repair times. During the comparison, the actual damage level is matched with the reference damage levels, and the reference repair time corresponding to the reference damage level that meets preset conditions (e.g., identical or closest) is taken as the final repair time.

[0054] A current maintenance work order refers to a maintenance work order that is currently being processed. A current maintenance work order may include the maintenance time and location of gas-related facilities (e.g., a specific gas pipeline). In some embodiments, the current maintenance work order may also include the gas storage capacity of a backup gas pipeline or backup gas storage device for the gas-related facilities. When a gas pipeline is under maintenance, gas supply can be provided through the backup gas pipeline or backup gas storage device. Current maintenance work orders can be obtained from the work records of the smart gas call center.

[0055] An anticipated maintenance work order is a maintenance work order that is expected to require processing. For example, an anticipated maintenance work order might include the estimated maintenance time and location for repairing gas-related facilities that may be damaged. Similar to current maintenance work orders, anticipated maintenance work orders may include the gas storage capacity of backup gas pipelines or backup gas storage devices for gas-related facilities. Anticipated maintenance work orders can be determined in various ways. For example, they can be determined based on gas-related facilities with scheduled maintenance times approaching from the periodic maintenance work schedule (which includes the gas-related facilities requiring periodic maintenance and the periodic maintenance time).

[0056] The second time refers to the future point in time or time period when gas will be used. The second time can be determined in several ways. For example, a specific time period within the future can be manually selected as the second time. Alternatively, a point in time can be manually selected at regular intervals as the second time. Similar to the first time, the second time can be adjusted based on factors such as peak and off-peak gas consumption periods and seasons. More details can be found in the preceding descriptions and will not be repeated here.

[0057] Gas supply refers to data related to supplying gas to gas users. For example, gas supply may include gas supply time and gas supply quantity. In some embodiments, gas supply may be determined based on an initially determined gas allocation plan. More information about the gas allocation plan can be found in step S240 and its related description.

[0058] Gas demand refers to the gas demand data of gas users. For further explanation regarding gas demand data, please refer to the relevant sections above.

[0059] In some embodiments, the intelligent operation management sub-platform can predict the expected gas usage characteristics at multiple second times based on gas usage characteristics and gas demand data; and predict whether the gas supply at multiple second times will meet the gas demand based on the expected gas usage characteristics and gas maintenance data.

[0060] Gas expected usage characteristics refer to features related to gas usage data of a gas pipeline at a second time. For example, gas expected usage characteristics could be features related to the amount of gas used by a gas pipeline at a second time.

[0061] Gas expected usage characteristics can be represented by vectors. These characteristics include sub-expected usage characteristics corresponding to each gas pipeline. For example, gas expected usage characteristics can be represented as ([c1, d1], [c2, d2], ...), where [c1, d1] represents the sub-expected usage characteristic of gas pipeline 1, [c2, d2] represents the sub-expected usage characteristic of gas pipeline 2, and so on; c1, c2, etc., represent multiple second times; d1 represents the gas consumption of gas pipeline 1 at second time c1, d2 represents the gas consumption of gas pipeline 2 at second time c2, and so on.

[0062] The expected gas usage characteristics can be determined in several ways. For example, under the same or similar gas demand data for a gas pipeline, the historical gas usage characteristics of a gas pipeline at the first historical time when it is in the same time period as the second time can be determined as the expected gas usage characteristics of the gas pipeline at the second time. As an example, under the same or similar gas demand data for gas pipeline A, the historical gas usage characteristics of gas pipeline A from 07:00 to 08:00 yesterday morning are ([07:00-08:00], 10), then the expected gas usage characteristics of gas pipeline A from 07:00 to 08:00 tomorrow morning can be determined as ([07:00-08:00], 10).

[0063] Whether the gas supply at a second time meets the gas demand can be determined in several ways. In some embodiments, for a given gas pipeline, the gas demand at a second time can be determined based on expected gas usage characteristics, and the gas supply at a second time can be determined based on gas maintenance data. By comparing the gas demand and the gas supply, it can be determined whether the gas supply at a second time meets the gas demand. For example, if the gas demand is greater than the gas supply, it can be determined that the gas supply at a second time does not meet the gas demand.

[0064] In some embodiments, the gas demand of a gas pipeline at a second time can be determined based on the gas demand of one or more corresponding gas users at the second time. For example, the gas demand of a gas pipeline at a second time can be determined by adding up the respective gas demands of the users.

[0065] In some embodiments, the gas supply volume at the second time can be determined based on gas maintenance data. For example, when the gas maintenance data does not include the expected maintenance work orders for the second time, the gas supply volume in the initial gas dispatching plan can be determined as the gas supply volume for the second time. When the gas maintenance data includes the expected maintenance work orders for the second time, the gas storage capacity of the backup gas pipeline or backup gas device can be determined as the gas supply volume for the second time.

[0066] In some embodiments, the intelligent operation management sub-platform can determine the expected gas usage characteristics for multiple second-time periods based on a predictive model, and determine whether the gas supply for these multiple second-time periods meets the gas demand. More information about the predictive model can be found in [link to relevant documentation]. Figure 3 And its related descriptions.

[0067] Step S240: In response to the fact that the gas supply in at least one of the multiple second times cannot meet the gas demand, the gas allocation plan is adjusted.

[0068] A gas distribution scheme can be a scheme for allocating gas supply from multiple gas pipelines. In some embodiments, a gas distribution scheme includes at least a gas storage scheme and a gas transmission scheme.

[0069] A gas storage plan refers to a plan for storing natural gas. In some embodiments, a gas storage plan includes at least one of storage time, storage volume, and storage area. A storage area refers to the area where a backup gas storage device or backup gas pipeline is located.

[0070] A gas delivery plan refers to the relevant schemes for delivering gas to various gas pipelines. In some embodiments, a gas delivery plan can supply gas to gas pipelines where the gas supply cannot meet the gas demand. In some embodiments, a gas delivery plan may include the supply priority of each gas pipeline (gas is supplied first when the supply priority is higher, which can be determined by human preset), the supply source of each gas pipeline, and the supply volume of each gas pipeline (the supply volume is less than or equal to the gas demand of the gas pipeline). The supply source includes gas supply based on the current pipeline, gas supply based on a backup gas storage device, or a backup gas pipeline. For example, the gas delivery plan for gas pipeline A may include a high supply priority, a supply source of a backup gas storage device, and a supply volume less than the corresponding gas demand. In some embodiments, the intelligent operation management sub-platform can determine a gas allocation plan based on the expected gas usage characteristics and whether the gas supply at multiple second times meets the gas demand.

[0071] In some embodiments, when the gas supply of a gas pipeline cannot meet the gas demand at a certain second time, the intelligent operation management sub-platform can determine a gas allocation plan based on the expected gas usage characteristics of the gas pipeline. For example, gas can be stored in advance in a backup gas storage device or backup gas pipeline in the gas storage area where the gas pipeline is located. The gas storage time can be set before the second time. The amount of gas stored can be determined based on the difference between the gas supply and the gas demand. The gas demand can be determined based on the expected gas usage characteristics.

[0072] In some embodiments, when the gas supply from multiple gas pipelines cannot meet the gas demand at a certain second time, a gas storage area can be determined based on the second importance coefficient of each gas pipeline. For example, the area where the gas pipeline with the second importance coefficient exceeds a second threshold is located can be designated as a gas storage area. The second importance coefficient can refer to an indicator that measures the supply priority of different gas pipelines. For example, the higher the second importance coefficient, the higher the supply priority of the corresponding gas pipeline. The second threshold refers to a threshold condition related to the second importance coefficient. The second threshold can be a system default value, an empirical value, a manually preset value, or any combination thereof, and can be set according to actual needs; this specification does not impose any restrictions on this.

[0073] In some embodiments, when the gas supply from multiple gas pipelines is insufficient to meet gas demand at a certain second time, the supply priority of the multiple gas pipelines can be determined based on a second importance coefficient for each gas pipeline. For example, the higher the second importance coefficient, the higher the supply priority. More information about the second importance coefficient can be found in [link to documentation]. Figure 5 And its related descriptions.

[0074] In some embodiments, when the gas supply of a gas pipeline cannot meet the gas demand at a certain second time, the gas delivery scheme in the gas allocation plan can be adjusted. For example, the supply source of the gas pipeline can be changed from "gas supply based on the current pipeline" to "gas supply based on a backup gas storage device or a backup gas pipeline".

[0075] In some embodiments, the intelligent operation management sub-platform can obtain feedback information from different types of gas users through the intelligent gas call center; based on the primary importance coefficient of different types of gas users and the feedback information, it can adjust the gas allocation plan. More details on adjusting the gas allocation plan can be found here. Figure 4 And its related descriptions.

[0076] In some embodiments of this specification, gas resources are scheduled according to the gas needs of different types of gas users, which can better meet the gas needs of users and improve the user experience. At the same time, by combining maintenance data and other relevant information from the smart gas call center, the impact of emergencies such as gas facility maintenance on gas supply can be reduced, gas supply and demand can be guaranteed as much as possible, the impact of insufficient gas supply on gas users can be reduced, user satisfaction can be improved, costs can be saved, and gas supply efficiency can be improved.

[0077] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 200 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0078] Figure 3 These are exemplary schematic diagrams of prediction models shown according to some embodiments of this specification.

[0079] In some embodiments, the intelligent operation management sub-platform can determine the expected gas usage characteristics 322 of multiple second times based on the prediction model 320, and further determine whether the gas supply of multiple second times meets the gas demand 340.

[0080] The predictive model can be a machine learning model. For example, the predictive model can be a neural network model (NN), a deep neural network model (DNN), a recurrent neural network (RNN), or any combination thereof.

[0081] In some embodiments, the input to the prediction model 320 may be gas usage characteristics 311, gas demand data 312, and gas maintenance data 313, and the output may be multiple second-time gas supply statuses (whether they meet gas demand) 340. In some embodiments, the output of the prediction model may be represented by 0 or 1. Here, 1 indicates that the gas supply meets gas demand, and 0 indicates that the gas supply does not meet gas demand. For more information on gas usage characteristics, gas demand data, and gas maintenance data, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0082] In some embodiments, the prediction model 320 may include a feature determination layer 321 and a prediction layer 323.

[0083] In some embodiments, the input to the feature determination layer 321 may include gas usage features 311 and gas demand data 312, and the output may include multiple expected gas usage features 322 at multiple second times. For example, the input to the feature determination layer may be gas usage features and gas demand data for one or more gas pipelines corresponding to 80,000 residential users, and gas usage features and gas demand data for one or more gas pipelines corresponding to 50 industrial users, and the output may be the expected gas usage features for one or more gas pipelines corresponding to residential users and the expected gas usage features for one or more gas pipelines corresponding to industrial users at multiple second times. More information on expected gas usage features can be found in [link to relevant documentation]. Figure 2 And its related descriptions.

[0084] In some embodiments, the inputs to the prediction layer 323 may include multiple second-time gas expected usage characteristics 322 and gas maintenance data 313, and the output may include multiple second-time gas supply status (whether it meets gas demand) 340. For example, the output of the prediction layer may be {([c11 [0], [c2] 1 ,1],…),([c1 2 ,1],[c2 2 ,1],…)}, indicating that the gas supply of gas pipeline 1 at the second time c1 does not meet the gas demand, the gas supply of gas pipeline 1 at the second time c2 meets the gas demand, etc., the gas supply of gas pipeline 2 at the second time c1 meets the gas demand, and the gas supply of gas pipeline 2 at the second time c2 meets the gas demand.

[0085] In some embodiments, the prediction model 320 may be obtained by joint training of the feature determination layer 321 and the prediction layer 323 based on a large number of second training samples with second labels.

[0086] In some embodiments, the second training samples may include sample gas usage characteristics, sample gas demand data, and sample gas maintenance data for multiple sample gas pipelines at a first time point. The second label may include whether the gas supply for each sample gas pipeline meets the gas demand at a second time point. In some embodiments, the second training samples may be obtained based on historical gas supply data. The second label may be determined manually.

[0087] An exemplary joint training process may include: inputting sample gas usage characteristics and sample gas demand data into an initial feature determination layer to obtain the expected gas usage characteristics of multiple samples at a second time, output by the initial feature determination layer; using the output of the initial feature determination layer as training sample data, and inputting it along with sample maintenance data into an initial prediction layer to obtain the results of whether the gas supply of multiple samples at a second time meets the gas demand, output by the initial prediction layer; constructing a loss function based on the second label and the output of the initial prediction layer, and synchronously updating the parameters of the initial feature determination layer and the initial prediction layer. The model training is complete when the loss function meets a preset condition for training termination, resulting in a trained prediction model. The preset condition for training termination may include loss function convergence, the number of iterations reaching an iteration threshold, etc.

[0088] In some embodiments, predicting whether gas supply meets gas demand using machine learning models can yield more accurate results than human judgment, saving costs and resources. Furthermore, jointly training multiple processing layers of the prediction model can effectively improve the accuracy of the prediction results.

[0089] Figure 4 This is an exemplary flowchart illustrating the adjustment of a gas distribution scheme according to some embodiments of this specification. In some embodiments, process 400 may be executed by a smart gas management platform. Figure 4 As shown, process 400 includes the following steps.

[0090] Step S410: Obtain feedback information from different types of gas users based on the smart gas call center.

[0091] Feedback information refers to information related to gas usage that gas users provide to the smart gas call center during the gas usage process. For example, feedback information may include user experience data, complaints and inquiries, and adjustments to gas demand data.

[0092] In some embodiments, feedback information can be obtained through various methods. For example, a smart gas call center can obtain user feedback information through questionnaires or similar means. Another example is that a smart gas call center can obtain user feedback information through user inquiries, complaints, and other related information.

[0093] Step S420: Adjust the gas allocation plan based on the primary importance coefficients of different types of gas users and feedback information.

[0094] The primary importance coefficient is used to assess the importance of a gas user. Different types of gas users may have the same or different primary importance coefficients. The primary importance coefficient can be represented by a value from 1 to 10, with a higher value indicating a higher level of importance for the user.

[0095] In some embodiments, the first importance coefficient can be preset based on the user's gas consumption, user type, etc. For example, the first importance coefficient decreases sequentially for residential users, industrial users, and commercial users. Another example is that for the same type of gas user, the first importance coefficient is positively correlated with the amount of gas consumed. The greater the gas consumption, the higher the first importance coefficient, and so on.

[0096] As mentioned above, after determining the gas allocation plan based on the expected gas usage characteristics and whether the gas supply at multiple secondary times meets the gas demand, the gas allocation plan can be further adjusted.

[0097] In some embodiments, the gas allocation scheme can be adjusted based on the first importance coefficient of different types of gas users and feedback information, including: determining the supply priority of each gas pipeline based on the first importance coefficient; and adjusting the supply volume of each gas pipeline based on feedback information and the supply priority of each gas pipeline.

[0098] In some embodiments, for a given gas pipeline, the supply priority of the gas pipeline can be determined based on the proportion of multiple gas users corresponding to the gas pipeline whose first importance coefficient exceeds a first threshold. For example, the system can preset a range of proportions where the first importance coefficient exceeds the first threshold and their corresponding supply priorities. By determining the range of the actual proportion, the supply priority of the gas pipeline can be determined. This proportion can be determined based on the ratio of the number of users whose first importance coefficient exceeds the first threshold to the total number of users corresponding to the gas pipeline.

[0099] In some embodiments, for a gas pipeline with a priority higher than a priority threshold, the gas supply to that pipeline can be adjusted based on feedback from gas users whose first importance coefficient meets the first threshold. For example, when the feedback indicates insufficient gas supply, the gas supply to the pipeline can be adjusted to exceed the gas demand of the pipeline. The first threshold and priority threshold can be system default values, empirical values, manually preset values, or any combination thereof, and can be set according to actual needs; this specification does not impose any restrictions on them.

[0100] In some embodiments, the intelligent operation management sub-platform can also predict user satisfaction based on feedback information; adjust the first importance coefficient based on user satisfaction; determine the second importance coefficient for different gas pipelines based on the adjusted first importance coefficient; determine the ratio of gas supply to gas demand for different gas pipelines based on the second importance coefficient, and adjust the gas allocation plan accordingly. More information on adjusting the gas allocation plan can be found in [link to relevant documentation]. Figure 5 And its related descriptions.

[0101] In some embodiments of this specification, feedback information from different types of users is obtained through a smart gas call center, and the gas allocation plan is further adjusted to better meet users' gas needs, improve user satisfaction, and effectively reduce user complaints.

[0102] Figure 5 This is an exemplary schematic diagram illustrating the adjustment of a gas distribution scheme according to some embodiments of this specification.

[0103] In some embodiments, adjusting the gas allocation scheme includes: predicting user satisfaction based on feedback information; adjusting a first importance coefficient based on user satisfaction; determining a second importance coefficient for different gas pipelines based on the adjusted first importance coefficient; determining the ratio of gas supply to gas demand for different gas pipelines based on the second importance coefficient, and adjusting the gas allocation scheme accordingly.

[0104] User satisfaction can be an indicator that measures how well users are satisfied with the current gas supply. For example, satisfaction can be represented by a number from 1 to 10. The higher the number, the higher the user's satisfaction.

[0105] In some embodiments, the intelligent operation management sub-platform can process feedback information to determine user satisfaction. For example, the intelligent operation management sub-platform can determine user satisfaction by comparing feedback information with a second preset table. The second preset table contains various reference feedback information and their corresponding reference user satisfaction levels. During the comparison, the actual feedback information is matched with the reference feedback information, and the reference user satisfaction level corresponding to the reference feedback information that meets preset conditions (e.g., identical or closest) is taken as the final user satisfaction level.

[0106] In some embodiments, the intelligent operation management sub-platform can process feedback information 511, gas maintenance data 512, and gas supply pressure 513 of multiple gas pipelines at multiple first moments based on the satisfaction prediction model 520 to determine the user satisfaction 530 corresponding to multiple gas pipelines.

[0107] Satisfaction prediction models can be machine learning models. For example, satisfaction prediction models can include any one or a combination of various feasible models such as Recurrent Neural Network (RNN) models, Deep Neural Network (DNN) models, and Convolutional Neural Network (CNN) models.

[0108] In some embodiments, the input to the satisfaction prediction model 520 may be feedback information 511 from all gas users in multiple gas pipelines, gas maintenance data 512 from multiple gas pipelines, and gas supply pressure 513 from multiple gas pipelines at multiple first moments. The output may be the user satisfaction 530 corresponding to multiple gas pipelines. A particular gas pipeline may be used to supply gas to one or more gas users of the same type. Accordingly, the user satisfaction corresponding to a particular gas pipeline may be the overall satisfaction of all gas users within it. For example, if gas pipeline A corresponds to 3 residential users, then the user satisfaction of gas pipeline A may be the overall satisfaction of the 3 residential users.

[0109] In some embodiments, the multiple user satisfaction levels output by the model can also be labeled. The labels are related to the user type. For example, if the user type corresponding to gas pipeline A is residential user, then the user satisfaction level of gas pipeline A output by the model can be labeled as "residential user".

[0110] The gas supply pressure of multiple gas pipelines at multiple instants can be obtained by a barometer at those instants. In some embodiments, a satisfaction prediction model can determine whether the gas supply pressure of multiple gas pipelines at multiple instants is within a reasonable range. When the gas supply pressure is not within a reasonable range, user satisfaction may be low. The reasonable range can be set manually.

[0111] In some embodiments, a satisfaction prediction model can be trained based on a large number of second training samples with second labels. For example, the second training samples are input into an initial satisfaction prediction model, a loss function is constructed using the second labels and the results of the initial satisfaction prediction model, and the parameters of the initial satisfaction prediction model are iteratively updated based on the loss function. The model training is complete when the loss function of the initial satisfaction prediction model meets a preset condition for training termination, resulting in a trained satisfaction prediction model. The preset condition for training termination could be loss function convergence, the number of iterations reaching a threshold, etc.

[0112] In some embodiments, the second training samples may include sample feedback information from gas users corresponding to multiple sample gas pipelines, sample gas maintenance data for multiple sample gas pipelines, and sample gas supply pressures for multiple sample gas pipelines at multiple first-time points. The second label may be the user satisfaction level corresponding to multiple sample gas pipelines. In some embodiments, the second training samples may be determined based on historical data. For example, historical feedback information, historical gas maintenance data, and historical gas supply pressures from multiple gas pipelines in historical data can be used to determine the training samples. The second label may be manually labeled.

[0113] In some embodiments, user satisfaction can be adjusted based on the number of complaints from gas users. For example, for a particular gas pipeline, if the number of complaints from one or more gas users along that pipeline increases, the user satisfaction level can be lowered accordingly.

[0114] In some embodiments, the intelligent operation management sub-platform can adjust the first importance coefficient 540 of one or more gas users corresponding to a gas pipeline based on the user satisfaction 530 of that gas pipeline. For example, the first importance coefficient of one or more gas users corresponding to a gas pipeline with a user satisfaction higher than the satisfaction threshold can be lowered.

[0115] In some embodiments, the intelligent operation management sub-platform can determine a second importance coefficient 550 for different gas pipelines based on an adjusted first importance coefficient. For example, the second importance coefficient for a gas pipeline can be determined by weighting the first importance coefficients of multiple gas users corresponding to that pipeline. The weights can be determined based on the gas user's satisfaction level. For example, gas users with higher satisfaction levels have lower weights.

[0116] In some embodiments, the intelligent operation management sub-platform can determine the ratio of gas supply to gas demand for different gas pipelines based on a second importance coefficient 550, and adjust the gas allocation plan 570.

[0117] In some embodiments, the intelligent operation management sub-platform can determine the ratio of gas supply to gas demand for different gas pipelines based on a second importance coefficient according to preset rules. The preset rules could prioritize the gas supply to gas pipelines with higher second importance coefficients. For example, assuming the second importance coefficient of gas pipeline A is 1, the ratio of gas supply to gas demand for gas pipeline A can be determined to be 1:1; assuming the second importance coefficient of gas pipeline B is 0.8, the ratio of gas supply to gas demand for gas pipeline B can be determined to be 0.8:1.

[0118] In some embodiments, the operation management sub-platform can adjust the initial gas allocation plan based on the ratio of gas supply to gas demand.

[0119] In some embodiments of this specification, the gas distribution scheme is adjusted based on user feedback and user satisfaction, which can make the gas distribution more in line with user needs.

[0120] One or more embodiments of this specification also provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the gas resource scheduling method based on a smart gas call center as described in any of the above embodiments.

[0121] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0122] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0123] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0124] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0125] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0126] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0127] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A gas resource scheduling method based on a smart gas call center, characterized in that, The method is executed by a smart gas management platform based on a smart gas call center-based gas resource scheduling IoT system, and the method includes: Obtain gas usage data from different types of gas users and determine gas usage characteristics; the gas usage characteristics include at least the gas usage data of the different types of gas users at multiple first-time moments; Obtain gas demand data, which includes demand time and demand quantity; Based on the gas usage characteristics, the gas demand data, and the gas maintenance data from the smart gas call center, predictions are made regarding whether gas supply will meet gas demand at multiple secondary times, including: The expected gas usage characteristics of the multiple second times are determined based on the prediction model, and it is determined whether the gas supply of the multiple second times meets the gas demand. The prediction model is a machine learning model, including a feature determination layer and a prediction layer. The input to the feature determination layer includes the gas usage characteristics and the gas demand data, and the output of the feature determination layer includes the expected gas usage characteristics at multiple second times. The inputs to the prediction layer include the expected gas usage characteristics and gas maintenance data at the multiple second times, and the outputs of the prediction layer include whether the gas supply at the multiple second times meets the gas demand. In response to the gas supply being unable to meet gas demand at at least one of the plurality of second times, the gas allocation plan is adjusted.

2. The gas resource scheduling method based on a smart gas call center according to claim 1, characterized in that, The gas resource dispatch IoT system based on the smart gas call center includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform includes at least a smart operation management sub-platform and a smart gas data center. The intelligent gas data center is used to acquire the gas usage data and the gas demand data, and send the gas usage data and the gas demand data to the intelligent operation management sub-platform for processing; The intelligent operation management sub-platform is used to process the gas usage data and the gas demand data, and send the processed gas allocation management information to the intelligent gas data center, and then to the intelligent gas user platform via the intelligent gas service platform.

3. The gas resource scheduling method based on a smart gas call center according to claim 1, characterized in that, The method further includes: Based on the expected gas usage characteristics and whether the gas supply at the multiple second times meets the gas demand, the gas allocation plan is determined, and the gas allocation plan includes at least a gas storage plan and a gas transmission plan.

4. The gas resource scheduling method based on a smart gas call center according to claim 3, characterized in that, The gas storage scheme includes at least one of the following: storage time, storage volume, and storage area.

5. The gas resource scheduling method based on a smart gas call center according to claim 1, characterized in that, The adjustment of the gas allocation plan in response to the gas supply being unable to meet gas demand at at least one of the plurality of second times includes: Based on the intelligent gas call center, feedback information from the different types of gas users is obtained; The gas allocation scheme is adjusted based on the first importance coefficient of the different types of gas users and the feedback information.

6. A gas resource dispatching Internet of Things system based on a smart gas call center, characterized in that, The gas resource dispatch IoT system based on the smart gas call center includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform includes at least a smart operation management sub-platform and a smart gas data center. The intelligent gas data center is used to acquire gas usage data and gas demand data of different types of gas users, and send the gas usage data and gas demand data to the intelligent operation management sub-platform for processing. The gas demand data includes demand time and demand quantity. The intelligent operation management sub-platform is configured to perform the following operations: Based on the gas usage data, gas usage characteristics are determined, and the gas usage characteristics include at least the gas usage data of the different types of gas users at multiple first-time moments; Based on the gas usage characteristics, the gas demand data, and the gas maintenance data from the smart gas call center, predictions are made regarding whether gas supply will meet gas demand at multiple secondary times, including: The expected gas usage characteristics of the multiple second times are determined based on the prediction model, and it is determined whether the gas supply of the multiple second times meets the gas demand. The prediction model is a machine learning model, including a feature determination layer and a prediction layer. The input to the feature determination layer includes the gas usage characteristics and the gas demand data, and the output of the feature determination layer includes the expected gas usage characteristics at multiple second times. The inputs to the prediction layer include the expected gas usage characteristics and gas maintenance data at the multiple second times, and the outputs of the prediction layer include whether the gas supply at the multiple second times meets the gas demand. In response to the fact that the gas supply at at least one of the plurality of second times cannot meet the gas demand, the gas allocation plan is adjusted; as well as The adjusted gas allocation plan is sent to the smart gas data center and then to the smart gas user platform via the smart gas service platform.

Images