A smart gas pipeline distribution control method, Internet of Things system and medium

Abstract

The present invention provides a smart gas pipeline distribution control method, an Internet of Things system, and a medium. The method includes: obtaining historical usage data of end users of a target pipeline; determining a distribution demand sequence based on the historical usage data; generating a gas distribution instruction based on the distribution demand sequence to adjust distribution control parameters of a distribution control device in the target pipeline; obtaining historical monitoring data and initial gas supply parameters of a gas supply source; determining a gas peak usage period based on the historical monitoring data; in response to a gas transmission time point being within a gas peak usage period: determining peak shaving parameters for the target pipeline based on the distribution demand sequence and the initial gas supply parameters; and generating a peak shaving distribution instruction based on the peak shaving parameters to control the distribution control device in the target pipeline to perform distribution and peak shaving operations according to the peak shaving parameters. This method achieves intelligent management and control of gas pipeline distribution control, improving the stability and efficiency of gas supply.

Description

Technical Field

[0001] This specification relates to the field of pipeline distribution, and in particular to a smart gas pipeline distribution control method, Internet of Things system and medium. Background Art

[0002] Gas distribution is the complex process of separating natural gas or other fuel gases from the main pipeline and delivering them to dispersed areas or users. This process involves multiple stages of pressure reduction and distribution from the high-pressure main pipeline to the low-pressure user end.

[0003] Affected by factors such as seasons, the start and stop usage of gas users in different time periods, and equipment failures, gas supply is prone to interruptions and fluctuations, resulting in unstable flow, low transmission efficiency, and other problems, which cannot fully meet user requirements.

[0004] Therefore, a smart gas pipeline distribution control method, an Internet of Things system and a medium are provided to realize intelligent management and control of gas pipeline distribution control and improve the stability and efficiency of gas supply. Summary of the Invention

[0005] One or more embodiments of the present specification provide a smart gas pipeline distribution control method, which is executed by a smart gas company management platform, including: obtaining historical usage data of end users of a target pipeline; determining a distribution demand sequence based on the historical usage data; generating a gas distribution instruction based on the distribution demand sequence to adjust the distribution control parameters of a distribution control device in the target pipeline; and obtaining historical monitoring data and initial gas supply parameters of a gas supply source; determining a gas peak period based on the historical monitoring data; in response to a gas transmission time point being in the gas peak period: determining the peak shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply parameters; generating a peak shaving distribution instruction based on the peak shaving parameters to control the distribution control device in the target pipeline to perform distribution peak shaving operations according to the peak shaving parameters.

[0006] One or more embodiments of this specification provide an Internet of Things system for smart gas pipeline distribution control, wherein the Internet of Things system includes a smart gas company management platform; the smart gas company management platform is configured to execute the smart gas pipeline distribution control method as described above.

[0007] In some embodiments, one or more embodiments of the present specification provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the smart gas pipeline distribution control method.

[0008] Beneficial effects: This application determines the distribution demand sequence based on the historical usage data of the end users of the target pipeline, and can analyze the user's gas distribution demand more accurately based on the end users' actual historical gas usage; and generates gas distribution instructions based on the distribution demand sequence, and adjusts the distribution control parameters of the distribution control device in the target pipeline; by determining the peak gas consumption period, it can be achieved that when the gas transmission time point is at the peak gas consumption period, based on the distribution demand sequence and the initial gas supply parameters, the peak-shaving parameters of the target pipeline are determined, so as to fully meet the gas demand of each end user. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0010] Figure 1 This is a schematic diagram of the platform structure of a smart gas pipeline distribution control Internet of Things system according to some embodiments of this specification;

[0011] Figure 2 This is an exemplary flow chart of a smart gas pipeline distribution control method according to some embodiments of this specification;

[0012] Figure 3 is an exemplary schematic diagram of determining a distribution demand sequence according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary flow chart for controlling a backup fuel gas source to supply gas according to target call parameters according to some embodiments of this specification.

[0014] 100-Smart gas pipeline distribution control IoT system, 110-Smart gas government safety supervision management platform, 111-Government supervision comprehensive database, 120-Smart gas government safety supervision sensor network platform, 130-Smart gas government safety supervision object platform, 131-Smart gas gas company management platform, 140-Smart gas gas company sensor network platform, 150-Smart gas equipment object platform, 310-Historical usage data, 320-Historical monitoring data, 330-Pipeline equipment data, 340-End-user characteristics, 350-Meteorological data, 360-Gas demand map, 370-Demand determination model, 380-Distribution demand sequence. DETAILED DESCRIPTION

[0015] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0017] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0018] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0019] Figure 1 This is a schematic diagram of the platform structure of a smart gas pipeline distribution control Internet of Things system according to some embodiments of this specification.

[0020] In some embodiments, as Figure 1 As shown, the smart gas pipeline distribution control Internet of Things system 100 may include a smart gas government safety supervision management platform 110, a government supervision comprehensive database 111, a smart gas government safety supervision sensor network platform 120, a smart gas government safety supervision object platform 130, a smart gas gas company management platform 131, a smart gas gas company sensor network platform 140 and a smart gas equipment object platform 150.

[0021] The smart gas government safety supervision and management platform 110 is a platform for supervising and safely managing gas pipelines. In some embodiments, the smart gas government safety supervision and management platform 110 can interact with the smart gas government safety supervision sensor network platform 120.

[0022] In some embodiments, the smart gas government safety supervision and management platform 110 may include a government supervision integrated database 111. In some embodiments, the smart gas government safety supervision and management platform 110 may be configured in a processor and / or a server.

[0023] The government supervision integrated database 111 is a database used to store supervision data. For example, the government supervision integrated database 111 can be used to store end-user characteristics of end-users of the target pipeline, and to integrate and store relevant data generated during the government supervision process.

[0024] The Smart Gas Government Safety Supervision Sensor Network Platform 120 is a functional platform for managing government sensor communications. In some embodiments, the Smart Gas Government Safety Supervision Sensor Network Platform 120 can be configured as a communication device and / or server to implement sensor communication of perception information and sensor communication of control information. For example, the Smart Gas Government Safety Supervision Sensor Network Platform 120 can be configured as a communication network and gateway to implement functions such as network management, protocol management, command management, and data parsing.

[0025] In some embodiments, the smart gas government safety supervision sensor network platform 120 can interact with the smart gas government safety supervision management platform 110 and the smart gas company management platform 131 of the smart gas government safety supervision target platform 130. For example, the smart gas government safety supervision sensor network platform 120 can obtain end-user characteristics of end users of the target pipeline collected by the smart gas government safety supervision management platform 110 and send the end-user characteristics to the smart gas company management platform 131.

[0026] The smart gas government safety supervision platform 130 is an information processing platform used by the government to conduct safety supervision of various gas safety-related supervision objects. For example, the smart gas government safety supervision platform 130 can generate and execute perception information and control information. In some embodiments, the smart gas government safety supervision platform 130 may include a smart gas company management platform 131.

[0027] Smart Gas Company Management Platform 131 is a comprehensive platform that coordinates and integrates the connections and collaborations between the various functional platforms of a gas company, aggregates all IoT information, analyzes and processes data and information generated during gas company operations, generates and executes instructions. In some embodiments, Smart Gas Company Management Platform 131 can be configured as a processor and / or server.

[0028] In some embodiments, the smart gas company management platform 131 can interact with the smart gas government safety supervision sensor network platform 120 and the smart gas company sensor network platform 140.

[0029] In some embodiments, the smart gas company management platform 131 can be configured to: obtain historical usage data of end users of the target pipeline from the smart gas equipment object platform through the smart gas company sensor network platform; determine the distribution demand sequence based on the historical usage data; generate gas distribution instructions based on the distribution demand sequence, and send the gas distribution instructions to the smart gas equipment object platform to adjust the distribution control parameters of the distribution control device in the target pipeline; and obtain historical monitoring data and initial gas supply parameters of the gas supply source through the smart gas equipment object platform; determine the peak gas consumption period based on the historical monitoring data; in response to the gas transmission time point being in the peak gas consumption period: determine the peak shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply parameters; generate peak shaving distribution instructions based on the peak shaving parameters, and send the peak shaving instructions to the smart gas equipment object platform to control the distribution control device in the target pipeline to perform distribution peak shaving operations according to the peak shaving parameters.

[0030] In some embodiments, the smart gas company management platform further includes a storage device that can store data and / or information obtained from other platforms.

[0031] The Smart Gas Company Sensor Network Platform 140 is a comprehensive management platform for gas company sensor information. In some embodiments, the Smart Gas Company Sensor Network Platform 140 can be configured as a communication device and / or gateway to implement sensory communication of sensory information and control information. For example, the Smart Gas Company Sensor Network Platform 140 can be configured as a communication network and gateway to implement network management, protocol management, command management, and data analysis.

[0032] In some embodiments, the smart gas company sensor network platform 140 can interact with the smart gas company management platform 131 and the smart gas equipment object platform 150 of the smart gas government safety supervision object platform 130. For example, the smart gas company sensor network platform 140 can obtain a gas distribution instruction generated by the smart gas company management platform 131 and send the gas distribution instruction to the smart gas equipment object platform 150. In another example, the smart gas company sensor network platform 140 can obtain historical monitoring data and initial gas supply parameters collected by the smart gas equipment object platform 150 and send the historical monitoring data and initial gas supply parameters to the smart gas company management platform 131.

[0033] The smart gas equipment object platform 150 is a functional platform for real-time monitoring and intelligent regulation of the gas pipeline network. In some embodiments, the smart gas equipment object platform 150 includes at least monitoring equipment and gas control devices deployed in the gas pipeline network.

[0034] Monitoring equipment refers to equipment used to monitor and record the operating status of the gas pipeline network. In some embodiments, monitoring equipment may include gas flow sensors, temperature sensors, pipeline pressure sensors, and indoor terminal equipment (e.g., gas meters).

[0035] Gas control devices refer to related equipment used to control and adjust the state of gas in the gas network. In some embodiments, the gas control devices may include valves, pump stations, etc.

[0036] For more information about each of the above platforms, please refer to Figure 2-Figure 4 and related descriptions.

[0037] Some embodiments of this specification, based on the smart gas pipeline distribution control Internet of Things system 100, can form an information operation closed loop between various functional platforms, and coordinate and operate regularly under the unified management of the smart gas company management platform, thereby realizing the informatization and intelligence of gas pipeline distribution control management.

[0038] It should be noted that the above description of the smart gas pipeline distribution control method, IoT system, and platform is for illustrative purposes only and does not limit this specification to the exemplary embodiments presented. It is understood that those skilled in the art, once they understand the principles of this system, may arbitrarily combine the various platforms or construct subsystems that connect to other platforms without departing from these principles.

[0039] Some embodiments of the present specification also disclose a smart gas pipeline distribution control method, which is executed by a smart gas company management platform, including: obtaining historical usage data of end users of the target pipeline; determining a distribution demand sequence based on the historical usage data; generating a gas distribution instruction based on the distribution demand sequence to adjust the distribution control parameters of the distribution control device in the target pipeline; and obtaining historical monitoring data and initial gas supply parameters of the gas supply source; determining a gas peak period based on the historical monitoring data; in response to the gas transmission time point being in the gas peak period: determining the peak shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply parameters; generating a peak shaving distribution instruction based on the peak shaving parameters to control the distribution control device in the target pipeline to perform distribution peak shaving operations according to the peak shaving parameters.

[0040] Figure 2 This is an exemplary flow chart of a smart gas pipeline distribution control method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps.

[0041] Step 210 , obtain historical usage data of end users of the target pipeline from the smart gas equipment object platform through the smart gas company sensor network platform.

[0042] The target pipeline refers to a pipeline in the gas pipeline that needs to monitor and control the gas flow. In some embodiments, the target pipeline may include a trunk pipeline and a distribution pipeline.

[0043] A trunk pipeline refers to a pipeline directly connected to a gas supply source, such as a gas supply station. A distribution pipeline refers to a pipeline connected to a trunk pipeline, used to distribute gas from the trunk pipeline to end users.

[0044] End users refer to the users to whom the gas in the target pipeline ultimately reaches.

[0045] Historical usage data refers to the data related to gas usage by end users over the past period of time, such as gas flow rate, gas usage time, and gas usage frequency.

[0046] In some embodiments, the smart gas company management platform can obtain historical usage data of end users of the target pipeline from the smart gas equipment object platform through the smart gas company sensor network platform.

[0047] Step 220: Determine the distribution demand sequence based on the historical usage data.

[0048] The distribution demand sequence is a sequence of gas demand parameters for the target pipeline. In some embodiments, the distribution demand sequence may include the gas demand parameters for each pipeline in the target pipeline. For example, one element of the distribution demand sequence corresponds to a gas demand parameter for one target pipeline.

[0049] The gas demand parameter refers to data related to gas demand in the target pipeline. In some embodiments, the gas demand parameter may include the gas distribution flow rate requirement for the target pipeline in a future time period. The gas distribution flow rate requirement may be a specific value or a data range.

[0050] In some embodiments, the smart gas company management platform can determine the distribution demand sequence based on historical usage data using various methods. For example, the smart gas company management platform can use the sum of the gas demand of the end user directly connected to the distribution pipeline and the gas demand of the downstream distribution pipeline directly connected to the distribution pipeline as the gas demand parameter corresponding to the distribution pipeline, and the sum of the gas demand parameters of all distribution pipelines as the gas demand parameter of the corresponding trunk pipeline.

[0051] For example, if the distribution pipeline corresponds to only one user or one downstream distribution pipeline, the gas demand parameter of the user or the gas demand parameter of the downstream distribution pipeline can be directly obtained as the gas demand parameter of the distribution pipeline.

[0052] For another example, if the distribution pipeline corresponds to multiple users and / or multiple downstream distribution pipelines, the gas demand parameters of the distribution pipeline can be determined by combining the gas demands of multiple users and / or the gas demand parameters of multiple downstream distribution pipelines.

[0053] The combination can be performed by direct summation or weighted summation. When performing weighted summation, users and / or downstream distribution pipelines with lower gas usage volatility are given higher weights. Alternatively, the weights can be positively correlated with the user's tier. For example, downstream distribution pipelines closer to the user end tier correspond to lower user tiers. Gas usage volatility can refer to the average gas usage volatility of the distribution pipeline's corresponding end users and / or downstream distribution pipelines.

[0054] In some embodiments, to further improve the accuracy of the determined gas demand parameters, redundancy adjustments can be performed based on the gas demand parameters determined in the above manner. For example, an adjustment parameter can be added or subtracted from the gas demand parameters determined in the above manner, and the adjusted value or range can be used as the final gas demand parameter for the target pipeline. The adjustment parameter can be determined based on historical data or preset.

[0055] For more information on determining the order of distribution requirements, see Figure 3 and related instructions.

[0056] Step 230: Generate a gas distribution instruction based on the distribution demand sequence, and send the gas distribution instruction to the smart gas equipment object platform to adjust the distribution control parameters of the distribution control device in the target pipeline.

[0057] Gas distribution instructions refer to operations that control the delivery of gas in target pipelines. Examples include instructions for adjusting gas flow rate and gas flow velocity. In some embodiments, the smart gas company management platform can determine the gas demand parameters for each target pipeline based on the distribution demand sequence and generate corresponding gas distribution instructions.

[0058] The distribution control device is a control device used to adjust the gas flow in the target pipeline. In some embodiments, the distribution control device may include regulating devices such as flow control valves and pressure regulating valves, and control devices such as the station control system PLC (Programmable Logic Controller).

[0059] Distribution control parameters refer to data referenced by the distribution control device during operation. In some embodiments, distribution control parameters may include operating parameters of the distribution control device and a distribution demand sequence. Examples include gas flow limits, gas pressure limits, gas valve opening, and valve pressure.

[0060] In some embodiments, the Smart Gas Company Management Platform can adjust the operating parameters of the distribution control device based on the gas distribution instruction through various methods to adjust the distribution control parameters. For example, the Smart Gas Company Management Platform can adjust the gas flow by adjusting the opening of the flow control valve and adjust the gas pressure by adjusting the operating parameters of the pressure regulating valve by issuing a gas distribution instruction. In another example, the Smart Gas Company Management Platform can send the gas distribution instruction to the station control system PLC in the distribution control device, and the PLC can automatically adjust the operating parameters of other regulating devices based on the distribution control parameters corresponding to the gas distribution instruction.

[0061] Step 240: Obtain historical monitoring data and initial gas supply parameters of the gas supply source through the smart gas equipment object platform.

[0062] The historical monitoring data refers to the data related to the gas in the target pipeline monitored over a period of time in the past. In some embodiments, the historical monitoring data may include gas flow rate, gas flow rate, pipeline pressure, etc.

[0063] The initial gas supply parameters refer to gas-related data output by the gas supply source in the target pipeline. In some embodiments, the initial gas supply parameters may include gas supply quantity, gas flow rate, gas flow rate, gas temperature, gas output pressure, etc.

[0064] In some embodiments, the smart gas company management platform can obtain historical monitoring data and initial gas supply parameters of the gas supply source through the smart gas equipment object platform.

[0065] Step 250: Determine the peak gas usage period based on historical monitoring data.

[0066] Gas peak hours refer to periods when gas demand in the target pipeline increases significantly, such as morning, noon, and evening.

[0067] In some embodiments, the smart gas company management platform can determine peak gas usage periods based on historical monitoring data through various methods. For example, the smart gas company management platform can calculate the average historical gas usage of all end users in various time periods and identify periods where the average historical gas usage exceeds a preset usage threshold as peak gas usage periods. The preset usage threshold can be set manually or determined based on historical experience.

[0068] In some embodiments, gas supply shortage may easily occur during gas consumption peak periods, and therefore it is necessary to determine the gas consumption peak periods in advance so as to regulate and control gas distribution at the corresponding time points.

[0069] Step 260 , in response to the gas transmission time point being in a gas consumption peak period; determining the peak shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply parameters.

[0070] The gas transmission time point refers to the time point at which gas pipeline distribution control is required, for example, a preset time point or the current time point.

[0071] Peak-shaving parameters refer to parameters related to regulating gas delivery in a target pipeline. In some embodiments, these parameters may include the order in which each target pipeline's supply targets are supplied, as well as the gas distribution volume supplied to each target. The target pipeline's supply targets include its directly connected downstream distribution pipelines and / or end users.

[0072] The supply order of each supply target refers to the priority of gas supply when meeting its corresponding gas demand. The gas distribution volume refers to the specific gas supply volume for each supply target.

[0073] In some embodiments, the smart gas company management platform can determine the peak-shaving parameters of the target pipeline in a variety of ways based on the distribution demand sequence and initial gas supply parameters.

[0074] For example, the smart gas company management platform can determine whether the gas supply of the gas supply source can meet the gas demand of all supply objects at the current time based on the gas demand parameters of the main pipeline in the distribution demand sequence. If it can be met, the priority of each supply object can be regarded as the same level, and the gas supply of each supply object can be the demand corresponding to the distribution demand sequence.

[0075] If the demand cannot be met, the priority of each supply target will be further determined, with the gas demand of the higher-priority supply targets being met first. If there is any remaining gas supply, gas will be supplied to the lower-priority supply targets. The priority of each supply target can be determined based on a preset, the volatility of its historical gas usage, or the level of the supply target. For example, the user with less volatility has a higher priority; the higher the level of the supply target, the higher its priority.

[0076] When the gas supply from a single source cannot meet the gas needs of all end users, the Smart Gas Company Management Platform can also determine the allocation ratio for each supply target based on priority. The allocation ratio refers to the proportion of the gas supply allocated to each end user as a percentage of the total gas supply at that point in time. The higher the priority, the larger the allocation ratio.

[0077] In some embodiments, when the gas supply of the gas supply source cannot meet the gas demand of all end users, the gas supply can be supplemented by calling the gas from the backup gas source. For details, see Figure 4 The corresponding content.

[0078] Step 270: Generate a peak-shaving distribution instruction based on the peak-shaving parameters, and send the peak-shaving distribution instruction to the smart gas equipment object platform to control the distribution control device in the target pipeline to perform distribution peak-shaving operations according to the peak-shaving parameters.

[0079] Peak-shaving distribution instructions refer to control adjustment instructions for distribution control devices and can be generated based on peak-shaving parameters. For example, the peak-shaving distribution instructions can be used to determine the operating parameters of the distribution control devices on each target pipeline.

[0080] Distribution peak-shaving refers to the operation of distributing gas to a target pipeline based on a distribution control device. In some embodiments, after the smart gas company management platform issues a peak-shaving distribution instruction to the smart gas equipment object platform, the smart gas equipment object platform can control the corresponding distribution control devices to adjust their operating parameters according to the operating parameters of each distribution control device in the peak-shaving distribution instruction. The distribution control devices then operate according to the adjusted operating parameters to achieve gas distribution peak-shaving.

[0081] In some embodiments of the present specification, the smart gas company management platform determines the distribution demand sequence based on the historical usage data of the end users of the target pipeline, and can analyze the more accurate user demand for gas distribution based on the historical actual usage of the end users; and generates gas distribution instructions based on the distribution demand sequence, and adjusts the distribution control parameters of the distribution control device in the target pipeline; by determining the peak gas consumption period, it can be achieved that when the gas transmission time point is at the peak gas consumption period, based on the distribution demand sequence and the initial gas supply parameters, the peak-shaving parameters of the target pipeline are determined to fully meet the gas demand of each end user.

[0082] It should be noted that the above description of process 200 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 200 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0083] Figure 3 is an exemplary schematic diagram of determining a distribution requirement sequence according to some embodiments of this specification.

[0084] In some embodiments, the smart gas company management platform obtains the end user characteristics 340 of the end users of the target pipeline from the smart gas government safety supervision management platform through the smart gas government safety supervision sensor network platform; constructs a gas demand map 360 based on historical usage data 310, historical monitoring data 320, pipeline equipment data 330, end user characteristics 340 and meteorological data 350; and determines the distribution demand sequence 380 based on the gas demand map 360 through the demand determination model 370.

[0085] End-user characteristics 340 refer to characteristic information related to the end user. In some embodiments, end-user characteristics may include user type, historical complaint information, gas usage scale, etc. User type may include residential, industrial, or commercial users. If the user type is industrial, the end-user characteristics may also include factory type.

[0086] In some embodiments, the smart gas company management platform can construct a gas demand map 360 based on historical usage data 310, historical monitoring data 320, pipeline equipment data 330, end user characteristics 340 and meteorological data 350.

[0087] Pipeline equipment data 330 refers to information related to the gas pipeline. In some embodiments, the pipeline equipment data may include pipeline equipment type, pipeline inner diameter, gas flow limit, gas pressure limit, etc. The pipeline equipment data may be obtained based on historical pipeline laying records.

[0088] Meteorological data 350 refers to meteorological data for the current and future time points in the area where the terminal user is located, such as temperature, humidity, wind speed, weather conditions, etc. In some embodiments, meteorological data can be obtained from a third-party platform, such as a weather forecast website.

[0089] In some embodiments, the nodes of the gas demand map 360 may include at least one of a pipeline node and a user node. For example, the nodes of the gas demand map 360 may include a pipeline node and a user node.

[0090] In some embodiments, the node characteristics of the pipeline node may include historical monitoring data and pipeline equipment data. The node characteristics of the user node may include historical usage data, end-user characteristics, and meteorological data.

[0091] In some embodiments, the node characteristics of the user node may further include a gas stability requirement, which is determined based on the terminal user characteristics and the device parameters used.

[0092] Device parameters refer to parameters related to gas-using devices. In some embodiments, these parameters may include the type of gas-using device and the age of the gas-using device. Examples of gas-using device types include wall-mounted boilers, gas stoves, and water heaters. In some embodiments, these parameters may be uploaded and retrieved by the user.

[0093] Gas stability requirements are used to characterize the end-user's need for stable gas delivery. For example, industrial users require gas for product manufacturing, and the stability of gas flow has a significant impact on product quality, so their gas stability requirements are relatively high.

[0094] In some embodiments, the smart gas company management platform can determine the gas stability requirements in a variety of ways based on end-user characteristics and equipment parameters.

[0095] For example, a smart gas company management platform can construct a vector database based on a large number of historical end-user characteristics and historical device parameters, and determine the corresponding gas stability requirements based on matching vector retrieval. The vector database can include multiple reference feature vectors and the reference gas stability requirements corresponding to those reference feature vectors. Each reference feature vector can be constructed based on historical end-user characteristics and device parameters collected during daily gas usage. For example, a reference feature vector can be constructed based on a historical end-user characteristic and its corresponding historical device parameters.

[0096] The smart gas company management platform can determine the reference gas stability requirement corresponding to each reference eigenvector based on historical feedback data. For example, during the gas supply process for the end user corresponding to the corresponding reference eigenvector, when gas delivery fluctuations occur, if feedback such as user complaints about gas fluctuations or feedback such as product quality fluctuations or unstable equipment status during the corresponding period is obtained, it can be considered that the reference gas stability requirement corresponding to the reference eigenvector is higher. Gas delivery fluctuation refers to the instability of gas during gas delivery in the target pipeline. For example, gas flow fluctuations, pressure fluctuations, etc. In some embodiments, gas delivery fluctuations can be calculated based on gas data read by a meter corresponding to the end user's household pipeline. For example, the meter can be a flow meter or a pressure meter.

[0097] The smart gas company management platform can construct a usage feature vector based on the terminal user characteristics and the usage device parameters corresponding to the terminal user characteristics. The reference feature vector is constructed in a similar way to the usage feature vector. In some embodiments, the smart gas company management platform can determine the gas stability requirement corresponding to the usage feature vector based on the similarity between the usage feature vector and multiple reference feature vectors in the vector database. For example, a reference feature vector whose similarity with the usage feature vector meets the similarity preset condition is used as the target vector, and the reference gas stability requirement corresponding to the target vector is used as the final gas stability requirement. The similarity preset condition can be set according to the situation. For example, the similarity is maximum, or the similarity is greater than a threshold, etc.

[0098] Edges are used to connect nodes that have connectivity.

[0099] Edge features can include gas flow direction.

[0100] In some embodiments, the demand determination model 370 is a machine learning model. For example, the demand determination model 370 is a graph neural network model.

[0101] In some embodiments, the input of the demand determination model 370 is the gas demand map 360 , and the output is the distribution demand sequence 380 .

[0102] In some embodiments, demand determination model 370 is trained using a training sample dataset. The training process for demand determination model 370 includes an initial training phase and an intensive training phase. The initial training phase refers to a pre-training phase using a large amount of general data as training data before the target pipeline is connected. The intensive training phase refers to a phase in which personalized training is performed using data specific to the target pipeline as training data.

[0103] In some embodiments, the training data in the training sample dataset includes training samples and their corresponding training labels. In some embodiments, the training samples may include sample gas demand maps, and the training labels may include distribution demand sequences actually collected by the training samples at future times.

[0104] During training, the Smart Gas Company Management Platform can input multiple training samples with training labels into the initial demand determination model. Using the training labels and the results of the initial demand determination model, it constructs a loss function. Based on the loss function, it iteratively updates the parameters of the initial demand determination model using gradient descent or other methods. When preset conditions are met, the demand determination model training is complete, resulting in a trained demand determination model. These preset conditions can include convergence of the loss function or a threshold number of iterations.

[0105] In the initial training phase, the initial demand determination model is trained using the training sample data set of the initial phase. In the intensive training phase, the initial demand determination model trained in the initial training phase is trained using the training sample data set of the intensive phase.

[0106] In some embodiments, during the initial training phase, the training sample data set is obtained based on some general data on the cloud platform. In some embodiments, the general data on the cloud platform may include corresponding data of pipelines in multiple areas of the same city and corresponding data of pipelines in other cities.

[0107] In some embodiments, during the intensive training phase, the training sample dataset is generated from historical data actually collected from the target pipeline, and the proportion of training samples corresponding to a time period is no less than a preset threshold. The preset threshold is positively correlated with the total gas delivery volume during that time period. The training samples corresponding to a time period are those consisting of historical data collected during that time period.

[0108] In some embodiments of this specification, the training process of the demand determination model includes an initial training stage and an intensive training stage. Phased training can not only accelerate the training process of the model and improve the performance of the model, but also improve the prediction accuracy of the model. The model after intensive training can obtain a more accurate distribution demand sequence based on the actual situation in the target pipeline. In addition, the distribution of gas demand in different time periods may be very uneven. For example, the gas consumption during peak hours in the morning and evening may be much higher than that in other time periods. If there are too few samples in a certain time period during model training, the model may not be able to accurately capture the gas consumption pattern of this time period. By ensuring that the proportion of samples in each time period is not less than the preset threshold, the data distribution can be balanced and the prediction accuracy can be improved.

[0109] In some embodiments of this specification, the Smart Gas Company Management Platform uses a demand determination model to determine the distribution demand sequence, leveraging the demand determination model's ability to accurately predict future gas demand by learning from historical patterns. This allows the forecast results to be more accurate and align with reality. Furthermore, the model can integrate multiple data sources, including pipeline equipment data, user characteristics, and meteorological data, to improve the comprehensiveness and accuracy of forecasts. For example, meteorological data can help predict the impact of extreme weather on gas demand.

[0110] Figure 4 This is an exemplary flow chart of controlling the backup gas source to supply gas according to target call parameters according to some embodiments of this specification. Figure 4 As shown, the process 400 includes the following steps.

[0111] In some embodiments, to further meet user gas demand and reduce peak gas supply pressure, a gas storage facility can be installed in the pipeline network, as well as gas storage devices on some or all of the most downstream distribution pipelines. The most downstream distribution pipeline refers to the distribution pipeline directly connected to the end user, and the most downstream distribution pipeline equipped with a gas storage device can be referred to as the terminal gas storage pipeline. The gas storage facility and terminal gas storage pipeline can be collectively referred to as a backup gas source.

[0112] In some embodiments, the peak-shaving parameters may further include target call parameters, and the peak-shaving distribution instructions may further include gas call instructions.

[0113] Target call parameters refer to parameters related to the backup gas source to be called and the use of the gas called. In some embodiments, the target call parameters may include a target call target, a target call time, a target call gas volume, and a gas delivery target. The target call target refers to the backup gas source to be called; the target call time refers to the specific time of the gas call; the target call gas volume refers to the gas output volume to be called; and the gas delivery target refers to the end user to whom the called backup gas source is to be delivered.

[0114] In some embodiments, the target call parameters are confirmed by the smart gas government safety supervision and management platform. The smart gas government safety supervision and management platform can adjust the target call parameters based on actual conditions, and then return the adjusted target call parameters to the smart gas company management platform.

[0115] A gas call instruction is a gas call control instruction determined based on target call parameters, for example, calling gas flow, calling gas flow rate, etc.

[0116] Step 410: Determine initial call parameters based on the distribution demand sequence and initial gas supply parameters.

[0117] The initial call parameters refer to the call-related data of the backup gas source initially set. In some embodiments, the initial call parameters may include the call object, call time, call gas volume, and gas delivery object.

[0118] In some embodiments, the smart gas company management platform can determine the initial call parameters in a variety of ways.

[0119] For example, based on the distribution demand sequence and the initial gas supply parameters, the net gas demand of the terminal user is determined, and based on the net gas demand of the terminal user, the initial call parameters corresponding to the backup gas source are determined.

[0120] Among them, net gas transmission demand = gas demand corresponding to end users - actual supply of target pipeline corresponding to end users.

[0121] In some embodiments, different backup gas sources have different calling priorities. For example, the calling priority of the terminal gas storage pipeline is higher than that of the gas storage reservoir. The smart gas company management platform can give priority to controlling the terminal gas storage pipeline to supplement the supply to its corresponding terminal users with insufficient supply. If the gas storage of the terminal gas storage pipeline still cannot meet the gas demand of its corresponding terminal users, the smart gas company management platform will call the gas storage reservoir to supplement the gas supply to each terminal user.

[0122] In some embodiments, the terminal gas storage pipeline may correspond to (ie, directly connect to) multiple users, among which users with insufficient gas supply (hereinafter referred to as users to be supplemented) are the ones who need to be supplemented with gas by the terminal gas storage pipeline.

[0123] In the initial call parameters corresponding to the terminal gas storage pipeline, the gas transmission object is the user to be replenished corresponding to the terminal gas storage pipeline, the call object is the terminal gas storage pipeline, the call time is the time when the net gas transmission demand is greater than 0, and the called gas volume can be determined based on multiple methods.

[0124] For example, when the gas storage capacity of the terminal gas storage pipeline is greater than or equal to the net gas transmission demand of the user to be supplemented, the called gas capacity in the initial calling parameters of the terminal gas storage pipeline is the gas capacity corresponding to the net gas transmission demand.

[0125] For another example, when the gas storage capacity of the terminal gas storage pipeline is less than the net gas transmission demand of the user to be supplemented, the called gas capacity in the initial calling parameters of the terminal gas storage pipeline can be the corresponding gas capacity when controlling the terminal gas storage pipeline to supplement the supply to the corresponding user to be supplemented by dividing its gas storage equally or distributing its gas storage according to the distribution weight.

[0126] The allocation weight is negatively correlated with the gas consumption volatility of the user to be replenished, and positively correlated with the net gas transmission demand and gas stability requirement of the user to be replenished. In some embodiments, if the gas replenishment provided by the terminal gas storage pipeline corresponding to the user to be replenished still cannot meet the gas demand of the user to be replenished, that is, the gas replenishment provided by the terminal gas storage pipeline is less than the net gas transmission demand of the user to be replenished, the initial call parameters also include the initial call parameters corresponding to the gas storage reservoir.

[0127] In some embodiments, the gas storage is connected to the distribution pipeline and is located upstream of the main pipeline, so that gas can be supplemented and supplied to more end users based on the gas storage.

[0128] In some embodiments, the smart gas company management platform can obtain user information for users who have a demand for additional gas from gas storage (hereinafter referred to as "additional gas users"). This user information may include information such as the additional gas quantity and the time of demand. The additional gas quantity can be the difference between the net gas demand and the gas quantity available from the terminal gas storage pipeline, and the time of demand can be the time when the net gas demand becomes greater than 0.

[0129] In some embodiments, there may be one or more gas storage facilities. If there is only one gas storage facility, the additional replenishment volume for all additional users is provided by this gas storage facility. In the initial call parameters corresponding to this gas storage facility, the gas delivery target is the additional user, the call target is the gas storage facility, and the call time is the aforementioned demand time point. The gas volume to be called for each gas delivery target can be determined based on various methods. For example, if the storage capacity of the gas storage facility is greater than or equal to the sum of the additional replenishment volumes of all additional users, the gas volume to be called for each gas delivery target is the corresponding additional replenishment volume.

[0130] If there are multiple gas storage facilities, the smart gas company management platform can summarize the storage capacity of all gas storage facilities and the additional replenishment capacity of all additional replenishment users. If the total storage capacity is greater than or equal to the total additional replenishment capacity, the smart gas company management platform can determine the additional replenishment users that each gas storage facility can supply and the corresponding gas call volume according to the principle of shortest transmission path or lowest transmission cost, and then obtain the initial call parameters of each gas storage facility.

[0131] If the storage capacity of multiple gas storage facilities (including a single gas storage facility) is less than the total additional replenishment capacity, the Smart Gas Company Management Platform will prioritize high-priority additional replenishment users by providing additional gas first. If there is still gas remaining, additional replenishment can be provided to lower-priority additional replenishment users until all storage facilities have been fully allocated. Each time an additional replenishment user is replenished, if the remaining available gas volume is greater than or equal to the additional replenishment capacity of the additional replenishment user, the additional replenishment user's requested gas volume will be the additional replenishment capacity. If the remaining available gas volume is less than the additional replenishment capacity of the additional replenishment user, the additional replenishment user's requested gas volume will be the remaining available gas volume. Remaining available gas volume = stored gas volume - determined gas volume to be requested, thus deriving the initial call parameters for each gas storage facility.

[0132] In step 420, the initial call parameters are uploaded to the smart gas government safety supervision and management platform, and the target call parameters fed back by the smart gas government safety supervision and management platform are obtained.

[0133] In some embodiments, the smart gas government safety supervision and management platform can adjust the initial call parameters according to actual conditions, and then determine the target call parameters. The adjustment of the initial call parameters may include adjusting the call gas volume or priority of some users to be supplemented or additional users, adjusting the supply volume of some terminal gas pipelines or gas storage reservoirs, etc. If the target call parameters fed back by the smart gas government safety supervision and management platform only adjust the supply volume of the terminal gas pipeline or gas storage reservoir, then the target call object, target call time, target call gas volume and gas transmission object in the target call parameters can be updated, and the gas storage volume of the gas storage reservoir and the terminal pipeline can be replaced with the supply volume of the terminal gas pipeline or gas storage reservoir adjusted by the smart gas government safety supervision and management platform.

[0134] Step 430: Determine the peak-shaving distribution parameters of the distribution pipeline based on the target call parameters and the initial gas supply parameters.

[0135] The peak-shaving distribution parameters of the distribution pipeline refer to the control parameters of the distribution pipeline, including the supply sequence of each corresponding end user and the gas distribution volume supplied to each end user.

[0136] In some embodiments, the smart gas company management platform can determine the peak-shaving distribution parameters of the distribution pipeline through various methods based on the target call parameters and the initial gas supply parameters.

[0137] For example, the smart gas company management platform can determine the supply order of end users based on the priority of the end users. The higher the priority, the higher the supply order. The gas distribution volume corresponding to each end user can be the sum of the gas volume obtained by the end user from the gas supply source and the gas supplementary supply volume obtained from the backup gas source. Among them, the gas supplementary supply volume obtained from the backup gas source can be determined based on the target call parameters, and the gas volume obtained from the gas supply source can be determined from the peak-shaving parameters of the target pipeline determined by the initial gas supply parameters and the distribution demand sequence. For details, see Figure 2 The corresponding content.

[0138] In some embodiments, the smart gas company management platform can determine the distribution priority based on gas stability requirements and user importance; and determine the peak-shaving distribution parameters of the distribution pipeline based on the distribution priority, target call parameters and initial gas supply parameters.

[0139] Distribution priority refers to the priority of supply objects determined based on gas stability requirements and user importance.

[0140] For more information on gas stability requirements, see Figure 3 and related instructions.

[0141] In some embodiments, the importance of a user can be determined based on preset rules. For example, the preset rules may be that industrial users are more important than residential users; the greater the user's average daily gas usage, the greater the user's monthly payment, the longer the user has been connected to the gas system, and the more times the user pays on time, the higher the user's importance.

[0142] In some embodiments, the smart gas company management platform can determine the distribution priority based on gas stability requirements and user importance in various ways. For example, users with strict gas stability requirements can be given a higher distribution priority; or users with high user importance can be given a higher distribution priority.

[0143] In some embodiments, the method of determining the peak-shaving distribution parameters of the distribution pipeline based on the distribution priority, target call parameters and initial gas supply parameters is the same as the above-mentioned method of determining the peak-shaving distribution parameters of the distribution pipeline based on the target call parameters and initial gas supply parameters. The difference is that when the priority is needed to determine the information, the priority used is replaced by the distribution priority.

[0144] In some embodiments of this specification, the smart gas company management platform determines the peak-shaving distribution parameters of the distribution pipeline based on the distribution priority, etc., so that the determined peak-shaving distribution parameters can better meet actual needs and achieve reasonable distribution of gas.

[0145] In some embodiments, when performing distribution and peak-shaving operations, the smart gas company management platform determines, based on actual monitoring data, whether the actual distribution parameters and the peak-shaving distribution parameters meet preset difference conditions, and / or whether the actual call parameters and the target call parameters meet preset difference conditions; in response to meeting the preset difference conditions, generates correction instructions based on the actual monitoring data, the actual distribution parameters and the actual call parameters; and sends the correction instructions to the smart gas equipment object platform to correct the operating parameters of the distribution control device.

[0146] The actual monitoring data refers to the relevant data of the gas monitored in real time. In some embodiments, the actual monitoring data may include actual distribution parameters and actual call parameters.

[0147] The preset difference condition refers to the allowable difference between the preset actual distribution parameters and the peak-shaving distribution parameters and / or the actual call parameters and the target call parameters. In some embodiments, the preset difference condition can be set manually or based on historical experience. For example, the preset difference condition can include the existence of a difference or the difference exceeding a preset range.

[0148] Actual distribution parameters refer to real-time monitored gas distribution data, such as the actual supply sequence of each end user and the actual gas distribution volume supplied to each end user.

[0149] The smart gas company management platform can obtain the actual supply order of each terminal user and the actual gas distribution volume corresponding to each terminal user in real time, and compare the actual distribution parameters obtained with the peak-shaving distribution parameters determined above. If there is a difference or the difference exceeds the preset range, a correction instruction will be generated.

[0150] A gas distribution volume discrepancy or a discrepancy exceeding a preset range may mean that the value of (gas distribution volume in the peak-shaving distribution parameter - actual gas distribution volume supplied) / gas distribution volume in the peak-shaving distribution parameter is greater than a preset discrepancy threshold. The preset discrepancy threshold can be obtained by querying a preset table based on the end user's information. For example, an end user with high gas stability requirements may set a lower preset discrepancy threshold.

[0151] Actual call parameters refer to the actual call status of backup gas sources. For example, they include the actual call time, actual gas volume, and actual gas transmission destination of the gas storage facility and terminal gas pipeline. The Smart Gas Company Management Platform can obtain the actual call status of each backup gas source in real time as actual call parameters, compare the obtained actual call parameters with the previously determined target call parameters, and generate correction instructions if any discrepancies exist.

[0152] Correction instructions refer to instructions for modifying and adjusting the operating parameters of the gas distribution control device.

[0153] In some embodiments, modifying the operating parameters of the distribution control device may include modifying the valve opening, pressure regulation parameters, gas flow rate, gas flow rate, etc. of the distribution control device.

[0154] In some embodiments, the smart gas company management platform can generate correction instructions in various ways based on actual monitoring data, actual distribution parameters, and actual call parameters. For example, if the actual gas distribution volume of the actual call parameters is less than the gas distribution volume in the peak-shaving distribution parameters, the valve opening of the downstream distribution pipeline of the corresponding end user can be increased accordingly.

[0155] In some embodiments, if there are objective limitations such as equipment aging, pipeline blockage, etc., which prevent the actual distribution parameters from meeting the peak-shaving distribution parameters, or the actual call parameters cannot meet the target call parameters, the actual call parameters can be used as new target call parameters, and based on the new target call parameters, combined with the distribution priority, initial gas supply parameters, etc., new peak-shaving distribution parameters can be determined, and the actual monitoring data can be obtained again to make the above-mentioned correction judgment.

[0156] In some embodiments of this specification, the smart gas company management platform generates correction instructions based on whether the actual monitoring data, actual distribution parameters and actual call parameters meet the preset difference conditions with the peak-shaving distribution parameters and target call parameters, so as to correct the operating parameters of the distribution control device, thereby ensuring that the operating parameters of the equipment can be discovered and adjusted in time during the actual gas distribution process.

[0157] In some embodiments, the smart gas company management platform can determine candidate distribution parameters based on target call parameters, distribution demand sequence and initial gas supply parameters; determine the evaluation scores of candidate distribution parameters based on candidate distribution parameters, end-user characteristics, pipeline characteristic maps through a peak shaving evaluation model; and determine the peak shaving distribution parameters based on the evaluation scores.

[0158] Candidate distribution parameters refer to data to be determined as peak-shaving distribution parameters.

[0159] In some embodiments, the smart gas company management platform uses the peak-shaving distribution parameters for the distribution pipelines determined based on the target call parameters and the initial gas supply parameters as initial parameters. Based on the initial parameters, the platform performs random adjustments within preset limits based on a preset step size to obtain multiple sets of candidate distribution parameters. The preset limits can be determined based on a distribution demand sequence. For example, the gas distribution volume in the candidate distribution parameters cannot be greater than the sum of the gas demands of the end users corresponding to the distribution pipeline.

[0160] In some embodiments, the smart gas company management platform can determine the evaluation scores of the candidate distribution parameters based on the candidate distribution parameters, end-user characteristics, and pipeline characteristic maps through a peak-shaving evaluation model.

[0161] In some embodiments, the peak shaving assessment model is a machine learning model. In some embodiments, the peak shaving assessment model is a convolutional neural network (CNN) model.

[0162] In some embodiments, the inputs of the peak shaving assessment model may include candidate distribution parameters, end-user characteristics, pipeline characteristic maps, meteorological data, and distribution demand sequences.

[0163] In some embodiments, the output of the peak shaving evaluation model is an evaluation score of the candidate transmission parameters.

[0164] In some embodiments, the peak shaving assessment model is trained using a training sample dataset. The training process of the peak shaving assessment model includes an initial training phase and an intensive training phase. The initial training phase refers to a pre-training phase before the corresponding data of the target pipeline is used as training data. The intensive training phase refers to a phase of personalized training based on the corresponding data of the target pipeline as training data.

[0165] In some embodiments, the training data in the training sample dataset includes training samples and their corresponding training labels. In some embodiments, the training samples may include sample peak-shaving and distribution parameters, sample end-user characteristics, sample pipeline characteristic maps, sample meteorological data, and sample distribution demand sequences; the training labels are evaluation scores corresponding to the samples.

[0166] During training, the Smart Gas Company Management Platform can input multiple labeled training samples into the initial peak-shaving assessment model. Using the training labels and the results of the initial peak-shaving assessment model, it constructs a loss function. Based on the loss function, it iteratively updates the parameters of the initial peak-shaving assessment model using gradient descent or other methods. When preset conditions are met, the peak-shaving assessment model training is complete, resulting in a trained peak-shaving assessment model. These preset conditions can include convergence of the loss function or a threshold number of iterations.

[0167] In the initial training stage, the training sample data set of the initial stage is used to perform the above training on the initial peak shaving evaluation model. In the intensive training stage, the training sample data set of the intensive stage is used to perform the above training on the initial peak shaving evaluation model trained in the initial training stage.

[0168] In some embodiments, tags can be obtained by collecting actual feedback from end users. For example, gas usage experience, user complaint rates, production efficiency, etc. can be used as the basis for scoring to determine the evaluation score. Another example is using appropriate monitoring equipment to monitor changes in product quality, gas supply stability, production efficiency, etc. to assess the evaluation score of peak-shaving and distribution parameters.

[0169] In some embodiments of this specification, the smart gas company management platform determines the evaluation scores of the candidate distribution parameters based on the candidate distribution parameters, terminal user characteristics, and pipeline characteristic maps through the peak-shaving evaluation model, and then determines the peak-shaving distribution parameters. Combined with the machine model, more accurate peak-shaving distribution parameters are obtained, so that the operation of the distribution control device is more in line with actual needs.

[0170] In some embodiments, the smart gas company management platform determines the peak-shaving distribution parameter based on the evaluation score in a variety of ways. For example, the candidate distribution parameter with the highest evaluation score is determined as the peak-shaving distribution parameter.

[0171] In step 440 , a gas call instruction is generated based on the target call parameters, and the gas call instruction is sent to the smart gas equipment object platform to control the backup gas source to supply gas according to the target call parameters.

[0172] In some embodiments of this specification, the initial call parameters are uploaded to the smart gas government safety supervision and management platform, the target call parameters fed back by the smart gas government safety supervision and management platform are obtained, the peak-shaving distribution parameters of the distribution pipeline are determined, and a gas call instruction is generated to realize real-time government monitoring and processing and reasonably adjust the gas distribution operation.

[0173] One or more embodiments of this specification provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a smart gas pipeline distribution control method.

[0174] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0175] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0176] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0177] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0178] 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 the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may vary according to the required features of the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0179] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This excludes any application history documents that are inconsistent with or conflicting with the content of this specification, as well as any documents (currently or subsequently appended to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0180] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A smart gas pipeline distribution control method, characterized in that: Executed by the Smart Gas Company Management Platform, including: Acquire historical usage data of end users of a target pipeline, wherein the target pipeline includes a trunk pipeline and a distribution pipeline; Obtaining terminal user characteristics of the terminal user; Construct a gas demand map based on historical usage data, historical monitoring data, pipeline equipment data, end-user characteristics, and meteorological data. The historical monitoring data includes gas flow, gas flow velocity, and pipeline pressure. The end-user characteristics include user type, historical complaint information, and gas usage scale. Based on the gas demand map, a distribution demand sequence is determined by a demand determination model, wherein the demand determination model is a graph neural network model; The node characteristics of the user node of the gas demand map include a gas stability requirement, wherein the gas stability requirement represents the end user's requirement for gas delivery stability, and the gas stability requirement is determined based on the end user's characteristics and the parameters of the device used; The training process of the demand determination model includes an initial training phase and an intensive training phase. The training sample dataset in the initial training phase is obtained based on general data on the cloud platform. The training sample dataset in the intensive training phase is generated from historical data actually collected by the target pipeline, and the proportion of training samples corresponding to a time period is not less than a preset threshold. The general data on the cloud platform includes corresponding data of pipelines in multiple regions of the same city and corresponding data of pipelines in other cities. generating a gas distribution instruction based on the distribution demand sequence to adjust a distribution control parameter of a distribution control device in the target pipeline; and Obtain historical monitoring data and initial gas supply parameters of the gas supply source; Determining a peak gas consumption period based on the historical monitoring data; In response to the gas transmission time point being in the gas consumption peak period: Determining peak-shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply parameters, wherein the peak-shaving parameters include target call parameters; A peak-shaving distribution instruction is generated based on the peak-shaving parameters to control the distribution control device in the target pipeline to perform distribution and peak-shaving operations according to the peak-shaving parameters, wherein the peak-shaving distribution instruction includes a gas call instruction, wherein: Determining the distribution priority based on the gas stability requirement and the user importance; determining the peak-shaving distribution parameters of the distribution pipeline based on the distribution priority, the target call parameter, and the initial gas supply parameter, specifically including: Determining candidate distribution parameters based on the target call parameter, the distribution demand sequence, and the initial gas supply parameter, wherein the candidate distribution parameters refer to data to be determined as the peak-shaving distribution parameters; Based on the candidate distribution parameters, end-user characteristics, and pipeline characteristic maps, an evaluation score of the candidate distribution parameters is determined by a peak-shaving evaluation model; the peak-shaving evaluation model is a convolutional neural network model; the training process of the peak-shaving evaluation model includes an initial training phase and an intensive training phase, wherein the initial training phase refers to a pre-training phase before the corresponding data of the target pipeline is connected as training data, and the intensive training phase refers to a phase of personalized customized training based on the corresponding data of the target pipeline as training data; the label of the peak-shaving evaluation model is the evaluation score, which is determined by gas usage experience, user complaint rate, and production efficiency; Determining peak-shaving distribution parameters of the distribution pipeline based on the evaluation score; Based on the target call parameters, the gas call instruction is generated to control the backup gas source to supply gas according to the target call parameters.

2. A smart gas pipeline distribution control Internet of Things system, characterized by: The Internet of Things system includes a smart gas company management platform; the smart gas company management platform is configured to execute the smart gas pipeline distribution control method as described in claim 1.

3. The smart gas pipeline distribution control Internet of Things system according to claim 2 is characterized in that: The IoT system also includes a smart gas government safety supervision management platform, a smart gas government safety supervision sensor network platform, a smart gas government safety supervision object platform, a smart gas gas company sensor network platform, and a smart gas equipment object platform. The smart gas government safety supervision management platform includes a government supervision comprehensive database; the smart gas government safety supervision object platform includes the smart gas gas company management platform; The smart gas government safety supervision object platform and the smart gas government safety supervision management platform exchange data through the smart gas government safety supervision sensor network platform; the smart gas government safety supervision object platform and the smart gas equipment object platform exchange data through the smart gas gas company sensor network platform.

4. A computer-readable storage medium, characterized in that The storage medium stores computer instructions. When the computer reads the computer instructions in the storage medium, the computer executes the smart gas pipeline distribution control method as described in claim 1.

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