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

Abstract

The invention provides an intelligent gas pipeline distribution control method, an Internet of Things system and a medium. The method comprises the steps of obtaining historical use data of a terminal user of a target pipeline; based on the historical use data, determining a distribution demand sequence; generating a gas distribution instruction based on the distribution demand sequence so as to adjust distribution control parameters of a distribution control device in the target pipeline; historical monitoring data and initial gas supply parameters of a gas supply source are obtained; based on historical monitoring data, determining a gas consumption peak period; in response to the fact that the gas transmission time point is in the gas consumption peak period, determining peak regulation parameters of the target pipeline based on the branch transmission demand sequence and the initial gas supply parameters; and generating a peak regulation and distribution instruction based on the peak regulation parameter so as to control a distribution control device in the target pipeline to carry out distribution and peak regulation operation according to the peak regulation parameter. Intelligent control over gas pipeline branch conveying control is achieved, and the stability and efficiency of gas supply are improved.

Description

Technical Field

[0001] This specification relates to the field of pipeline sub - transmission, and particularly to an intelligent gas pipeline sub - transmission control method, an Internet of Things system, and a medium. Background Art

[0002] Gas sub - transmission refers to the complex process of separating natural gas or other gases from the main pipeline and transporting them to various dispersed areas or users. This process involves multi - stage pressure reduction and distribution from high - pressure main pipelines to low - pressure user ends.

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

[0004] Therefore, providing an intelligent gas pipeline sub - transmission control method, an Internet of Things system, and a medium can achieve intelligent management and control of gas pipeline sub - transmission, and improve the stability and efficiency of gas supply. Summary of the Invention

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

[0006] One or more embodiments of this specification provide an intelligent gas pipeline sub - transmission control Internet of Things system, where the Internet of Things system includes an intelligent gas company management platform; the intelligent gas company management platform is configured to execute the intelligent gas pipeline sub - transmission control method as described above.

[0007] In some embodiments, one or more embodiments of this specification provide a computer - readable storage medium, where the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the intelligent gas pipeline sub - transmission control method.

[0008] Beneficial effects: Based on the historical usage data of the end-users of the target pipeline, this application determines the subtransmission demand sequence, and can analyze the relatively accurate subtransmission demand of users for gas based on the historical actual usage of gas by the end-users; and generates a gas subtransmission instruction based on the subtransmission demand sequence to adjust the subtransmission control parameters of the subtransmission control device in the target pipeline; by determining the peak gas usage period, it can be realized that when the gas transmission time point is in the peak gas usage period, based on the subtransmission 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. Description of the Drawings

[0009] This specification will be further described by way of exemplary embodiments, which will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where: Figure 1 is a schematic diagram of the platform structure of an intelligent gas pipeline subtransmission control Internet of Things system shown in some embodiments of this specification; Figure 2 is an exemplary flowchart of an intelligent gas pipeline subtransmission control method shown in some embodiments of this specification; Figure 3 is an exemplary diagram for determining the subtransmission demand sequence shown in some embodiments of this specification; Figure 4 is an exemplary flowchart for controlling the standby gas source to supply gas according to the target calling parameters shown in some embodiments of this specification.

[0010] 100 - Intelligent gas pipeline subtransmission control Internet of Things system, 110 - Intelligent gas government safety supervision and management platform, 111 - Government supervision comprehensive database, 120 - Intelligent gas government safety supervision sensor network platform, 130 - Intelligent gas government safety supervision object platform, 131 - Intelligent gas gas company management platform, 140 - Intelligent gas gas company sensor network platform, 150 - Intelligent 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 - Subtransmission demand sequence. Detailed Embodiments

[0011] To more clearly illustrate the technical solutions of the embodiments of this specification, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the drawings represent the same structure or operation.

[0012] It should be understood that the "system", "device", "unit" and / or "module" used herein is a way to distinguish different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0013] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0014] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the previous or subsequent operations are not necessarily executed precisely in sequence. On the contrary, the steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0015] Figure 1 It is a schematic diagram of the platform structure of an intelligent gas pipeline subtransmission control Internet of Things system shown according to some embodiments of this specification.

[0016] In some embodiments, as Figure 1 shown, the intelligent gas pipeline subtransmission control Internet of Things system 100 may include an intelligent gas government safety supervision and management platform 110, a government supervision comprehensive database 111, an intelligent gas government safety supervision sensor network platform 120, an intelligent gas government safety supervision object platform 130, an intelligent gas gas company management platform 131, an intelligent gas gas company sensor network platform 140, and an intelligent gas equipment object platform 150.

[0017] The intelligent gas government safety supervision and management platform 110 refers to a platform for supervising and safely managing gas pipelines. In some embodiments, the intelligent gas government safety supervision and management platform 110 may interact with the intelligent gas government safety supervision sensor network platform 120.

[0018] In some embodiments, the intelligent gas government safety supervision management platform 110 may include a government supervision comprehensive database 111. In some embodiments, the intelligent gas government safety supervision management platform 110 may be configured in a processor and / or a server.

[0019] The government supervision comprehensive database 111 refers to a database for storing supervision data. For example, the government supervision comprehensive database 111 may be used to store the end-user characteristics of the end-users of the target pipeline, and to integrate and store relevant data generated during the government supervision process, etc.

[0020] The intelligent gas government safety supervision sensing network platform 120 refers to a functional platform for managing the sensing communication of the government. In some embodiments, the intelligent gas government safety supervision sensing network platform 120 may be configured as a communication device and / or a server, etc., for realizing the functions of sensing communication of sensing information and sensing communication of control information. For example, the intelligent gas government safety supervision sensing network platform 120 may be configured as a communication network and a gateway to realize functions such as network management, protocol management, instruction management, and data parsing.

[0021] In some embodiments, the intelligent gas government safety supervision sensing network platform 120 may interact with the intelligent gas government safety supervision management platform 110 and the intelligent gas company management platform 131 of the intelligent gas government safety supervision object platform 130. For example, the intelligent gas government safety supervision sensing network platform 120 may obtain the end-user characteristics of the end-users of the target pipeline collected by the intelligent gas government safety supervision management platform 110, and send the end-user characteristics to the intelligent gas company management platform 131.

[0022] The intelligent gas government safety supervision object platform 130 refers to an information processing platform for the government to conduct safety supervision on various supervision objects related to gas safety. For example, the intelligent gas government safety supervision object platform 130 may realize the generation and execution of sensing information and control information, etc. In some embodiments, the intelligent gas government safety supervision object platform 130 may include an intelligent gas company management platform 131.

[0023] The intelligent gas company management platform 131 refers to a comprehensive platform that coordinates and collaborates the connections between the various functional platforms of the gas company, gathers all the information of the Internet of Things, generates instructions and executes them by analyzing and processing the data / and information generated during the operation of the gas company. In some embodiments, the intelligent gas company management platform 131 may be configured as a processor and / or a server, etc.

[0024] In some embodiments, the intelligent gas company management platform 131 may interact with the intelligent gas government safety supervision sensor network platform 120 and the intelligent gas company sensor network platform 140.

[0025] In some embodiments, the intelligent gas company management platform 131 may be configured to: obtain, through the intelligent gas company sensor network platform, historical usage data of end users of a target pipeline from the intelligent gas device object platform; determine a subtransmission demand sequence based on the historical usage data; generate a gas subtransmission instruction based on the subtransmission demand sequence, and send the gas subtransmission instruction to the intelligent gas device object platform to adjust the subtransmission control parameters of the subtransmission control device in the target pipeline; and obtain, through the intelligent gas device object platform, historical monitoring data and initial gas supply parameters of the gas supply source; determine the peak gas usage period based on the historical monitoring data; in response to the gas transmission time point being in the peak gas usage period: determine the peak shaving parameters of the target pipeline based on the subtransmission demand sequence and the initial gas supply parameters; generate a peak shaving subtransmission instruction based on the peak shaving parameters, and send the peak shaving instruction to the intelligent gas device object platform to control the subtransmission control device in the target pipeline to perform peak shaving subtransmission operations according to the peak shaving parameters.

[0026] In some embodiments, the intelligent gas company management platform further includes a storage device. The storage device may store data and / or information obtained from other platforms.

[0027] The intelligent gas company sensor network platform 140 refers to an integrated management platform for the sensor information of the gas company. In some embodiments, the intelligent gas company sensor network platform 140 may be configured as communication devices and / or gateways, etc., for implementing the functions of sensing communication of perception information and sensing communication of control information. For example, the intelligent gas company sensor network platform 140 may be configured as a communication network and a gateway to implement functions such as network management, protocol management, instruction management, and data parsing.

[0028] In some embodiments, the intelligent gas company sensor network platform 140 may interact with the intelligent gas company management platform 131 and the intelligent gas device object platform 150 of the intelligent gas government safety supervision object platform 130. For example, the intelligent gas company sensor network platform 140 obtains the gas subtransmission instruction generated by the intelligent gas company management platform 131 and sends the gas subtransmission instruction to the intelligent gas device object platform 150. For another example, the intelligent gas company sensor network platform 140 obtains the historical monitoring data and the initial gas supply parameters of the gas supply source collected by the intelligent gas device object platform 150, and sends the historical monitoring data and the initial gas supply parameters of the gas supply source to the intelligent gas company management platform 131.

[0029] The intelligent gas equipment object platform 150 refers to a functional platform for real-time monitoring and intelligent regulation of gas pipe networks. In some embodiments, the intelligent gas equipment object platform 150 at least includes supervision equipment and gas regulation devices arranged in the gas pipe network.

[0030] The supervision equipment refers to relevant equipment for monitoring and recording the operating status of the gas pipe network. In some embodiments, the supervision equipment may include gas flow sensors, temperature sensors, pipeline pressure sensors, and indoor terminal equipment (such as gas meters), etc.

[0031] The gas regulation device refers to relevant equipment for controlling and regulating the gas state in the gas pipe network. In some embodiments, the gas regulation device may include valves, pumping stations, etc.

[0032] For more content about the above-mentioned various platforms, reference can be made to Figures 2 - 4 and related descriptions.

[0033] In some embodiments of this specification, based on the intelligent gas pipeline distribution control Internet of Things system 100, an information operation closed-loop can be formed among the functional platforms, and coordinated and regular operation can be achieved under the unified management of the intelligent gas company management platform, realizing the informatization and intelligence of gas pipeline distribution control management.

[0034] It should be noted that the above descriptions of the intelligent gas pipeline distribution control method, the Internet of Things system, and its platform are only for the convenience of description and do not limit this specification to the scope of the exemplified embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, various platforms may be arbitrarily combined, or a subsystem may be formed and connected to other platforms without departing from this principle.

[0035] Some embodiments of this specification also disclose an intelligent gas pipeline distribution control method, which is executed by the intelligent gas company management platform and includes: obtaining the historical usage data of the end-users of the target pipeline; determining the 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 the initial gas supply parameters of the gas supply source; determining the peak gas usage period based on the historical monitoring data; in response to the gas transmission time point being in the peak gas usage 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 peak shaving operation according to the peak shaving parameters.

[0036] Figure 2 is an exemplary flowchart of an intelligent gas pipeline distribution control method shown in some embodiments of this specification. AsFigure 2 As shown in Figure 2 , process 200 includes the following steps.

[0037] Step 210: Obtain the historical usage data of the end users of the target pipeline from the intelligent gas equipment object platform through the intelligent gas company's sensing network platform.

[0038] The target pipeline refers to the pipeline in the gas pipeline that needs to monitor and control the gas flow. In some embodiments, the target pipeline may include the main pipeline and the distribution pipeline.

[0039] The main pipeline may refer to the pipeline directly connected to the gas supply source such as the gas supply station. The distribution pipeline refers to the pipeline connected to the main pipeline and is used to distribute the gas in the main pipeline to the end users.

[0040] The end user refers to the user who finally receives the gas in the target pipeline.

[0041] The historical usage data refers to the relevant data of the gas usage of the end user in the past period of time. For example, the gas flow rate used in the past period of time, the gas usage time, the number of gas usage times, etc.

[0042] In some embodiments, the intelligent gas company's management platform may obtain the historical usage data of the end users of the target pipeline from the intelligent gas equipment object platform through the intelligent gas company's sensing network platform.

[0043] Step 220: Determine the distribution demand sequence based on the historical usage data.

[0044] The distribution demand sequence refers to the sequence composed of the gas demand parameters of the target pipeline. In some embodiments, the distribution demand sequence may include the gas demand parameters of each pipeline in the target pipeline. By way of example only, one element of the distribution demand sequence corresponds to a gas demand parameter of a target pipeline.

[0045] The gas demand parameter refers to the relevant data regarding the gas demand in the target pipeline. In some embodiments, the gas demand parameter may include the gas distribution flow rate demand in the future time period of the target pipeline. Among them, the gas distribution flow rate demand may be a specific value or a data range.

[0046] In some embodiments, the intelligent gas company's management platform may determine the distribution demand sequence based on the historical usage data through various methods. For example, the intelligent gas company's management platform may use the sum of the gas demands of the end users directly connected to the distribution pipeline and the gas demands of the downstream distribution pipelines directly connected to the distribution pipeline as the gas demand parameter corresponding to the distribution pipeline, and use the sum of the gas demand parameters of all distribution pipelines as the gas demand parameter corresponding to the main pipeline.

[0047] For example, if the distribution pipeline corresponds to only one user or one downstream distribution pipeline, the gas demand of the user or the gas demand parameters of the downstream distribution pipeline can be directly obtained as the gas demand parameters of the distribution pipeline.

[0048] 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.

[0049] The combination method can be direct addition or weighted summation. When performing weighted summation, higher weights are assigned to users and / or downstream distribution pipelines with less volatile gas consumption. Or, the weight can also be positively correlated with the user level. For example, the user level corresponding to the downstream distribution pipeline closer to the user end layer is smaller. Among them, the gas consumption volatility can refer to the average value of the gas consumption volatility of the end users and / or downstream distribution pipelines corresponding to the distribution pipeline.

[0050] In some embodiments, in order to further improve the accuracy of the determined gas demand parameters, redundancy adjustment can be performed on the basis of the gas demand parameters determined by the above method. For example, an adjustment parameter can be added or subtracted on the basis of the gas demand parameters determined by the above method, and the adjusted value or range is used as the final gas demand parameters of the target pipeline. Among them, the adjustment parameter can be determined or preset based on historical data.

[0051] For more content on determining the fractional demand sequence, reference can be made to Figure 3 and its related descriptions.

[0052] Step 230, generate a gas distribution instruction based on the distribution demand sequence, and send the gas distribution instruction to the intelligent gas equipment object platform to adjust the distribution control parameters of the distribution control device in the target pipeline.

[0053] The gas distribution instruction refers to the instruction operation for gas transportation in the target pipeline. For example, an instruction to adjust the gas flow rate, an instruction to adjust the gas velocity, etc. In some embodiments, the intelligent gas company management platform can determine the gas demand parameters of each target pipeline based on the distribution demand sequence, and then generate corresponding gas distribution instructions.

[0054] The distribution control device refers to the control equipment used to adjust the gas in the target pipeline. In some embodiments, the distribution control device can include regulating equipment such as flow regulating valves and pressure regulating valves, and control equipment such as the station control system PLC (Programmable Logic Controller).

[0055] The sub - transmission control parameters refer to the data referred to during the operation of the sub - transmission control device. In some embodiments, the sub - transmission control parameters may include the operation parameters of the sub - transmission control device and the sub - transmission demand sequence. For example, gas flow rate limit, gas pressure limit, gas valve opening, and valve pressure, etc.

[0056] In some embodiments, the intelligent gas company management platform can adjust the operation parameters of the sub - transmission control device through various methods based on the gas sub - transmission instruction, so as to adjust the sub - transmission control parameters. For example, the intelligent gas company management platform can adjust the opening of the flow regulating valve to adjust the gas flow rate and adjust the operation parameters of the pressure regulating valve to adjust the gas pressure by issuing the gas sub - transmission instruction. Also, for example, the intelligent gas company management platform sends the gas sub - transmission instruction to the station control system PLC in the sub - transmission control device, and the PLC can automatically adjust the operation parameters of other regulating devices according to the sub - transmission control parameters corresponding to the gas sub - transmission instruction.

[0057] Step 240, obtain the historical monitoring data and the initial gas supply parameters of the gas supply source through the intelligent gas device object platform.

[0058] The historical monitoring data refers to the relevant data of the gas in the target pipeline monitored over a past period of time. In some embodiments, the historical monitoring data may include gas flow rate, gas flow velocity, and pipeline pressure, etc.

[0059] The initial gas supply parameters refer to the 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 volume, gas flow rate, gas flow velocity, gas temperature, and gas output pressure, etc.

[0060] In some embodiments, the intelligent gas company management platform can obtain the historical monitoring data and the initial gas supply parameters of the gas supply source through the intelligent gas device object platform.

[0061] Step 250, determine the peak gas - using period based on the historical monitoring data.

[0062] The peak gas - using period refers to the period when the gas demand in the target pipeline increases significantly. For example, morning, noon, and evening.

[0063] In some embodiments, the intelligent gas company management platform can determine the peak gas - using period based on the historical monitoring data in various ways. For example, the intelligent gas company management platform can calculate the average historical gas consumption of all end - users in each time period, and determine the time period with the average historical gas consumption greater than the preset consumption threshold as the peak gas - using period. Among them, the preset consumption threshold can be set manually or determined based on historical experience.

[0064] In some embodiments, during the peak gas usage period, there is a tendency for insufficient gas supply. Therefore, it is necessary to determine the peak gas usage period in advance so as to conduct gas distribution regulation at the corresponding time points.

[0065] Step 260, in response to the gas transmission time point being in the peak gas usage period; based on the gas distribution demand sequence and the initial gas supply parameters, determine the peak shaving parameters of the target pipeline.

[0066] The gas transmission time point refers to the time point at which gas pipeline distribution control needs to be carried out. For example, a preset time point or the current time point, etc.

[0067] The peak shaving parameters refer to the parameters related to gas transmission regulation in the target pipeline. In some embodiments, the peak shaving parameters may include the supply order of the supply objects of each target pipeline and the gas distribution volume supplied corresponding to each supply object, etc. Among them, the supply objects of the target pipeline include the downstream distribution pipelines directly connected to the target pipeline and / or end users.

[0068] The supply order of the supply objects refers to the priority of gas supply when meeting their corresponding gas demands. The gas distribution volume is the specific gas supply volume of each supply object.

[0069] In some embodiments, the intelligent gas company management platform can determine the peak shaving parameters of the target pipeline based on the gas distribution demand sequence and the initial gas supply parameters through various methods.

[0070] For example, the intelligent gas company management platform can determine whether the gas supply volume of the gas supply source at the current time point can meet the gas demands of all supply objects based on the gas demand parameters of the main pipeline in the gas 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 volume of each supply object can be the demand volume corresponding to the gas distribution demand sequence.

[0071] If it cannot be met, it is necessary to further determine the priority of each supply object, give priority to meeting the gas demands of the supply objects with higher priority, and if there is a remaining gas supply volume, then supply gas to the supply objects with the next highest priority. Among them, the priority of each supply object can be determined based on a preset, based on its historical gas usage volatility or the level of the supply object. For example, the smaller the volatility of the user, the higher its priority; another example is that the higher the level of the supply object, the higher its priority.

[0072] When the gas supply volume of the gas supply source cannot meet the gas demands of all end users, the intelligent gas company management platform can also determine the distribution ratio corresponding to each supply object through the priority. The distribution ratio refers to the proportion of the gas supply volume allocated to the end user in the total gas supply volume at that time point. For example, the higher the priority, the larger the distribution ratio.

[0073] In some embodiments, when the gas supply volume of the gas supply source cannot meet the gas demands of all end-users, the gas from a standby gas source can also be called to supplement the gas supply. For specific descriptions, refer to Figure 4 the corresponding content of

[0074] Step 270: Generate a peak shaving distribution instruction based on the peak shaving parameters, and send the peak shaving distribution instruction to the intelligent gas equipment object platform to control the distribution control device in the target pipeline to perform peak shaving distribution operations according to the peak shaving parameters.

[0075] The peak shaving distribution instruction refers to a control adjustment instruction for the distribution control device and can be generated based on the peak shaving parameters. For example, based on the peak shaving distribution instruction, the operating parameters of the distribution control devices on each target pipeline can be determined, etc.

[0076] The peak shaving distribution operation refers to an operation for realizing the gas distribution of the target pipeline based on the distribution control device. In some embodiments, after the intelligent gas company management platform sends the peak shaving distribution instruction to the intelligent gas equipment object platform, the intelligent gas equipment object platform can control the corresponding distribution control device to adjust the operating parameters according to the operating parameters of each distribution control device in the peak shaving distribution instruction, and the distribution control device operates according to the adjusted operating parameters to realize the peak shaving distribution operation of the gas.

[0077] In some embodiments of this specification, the intelligent gas company management platform determines a distribution demand sequence based on the historical usage data of the end-users of the target pipeline, and can analyze the relatively accurate distribution demands of the users for gas based on the historical actual usage of gas by the end-users; and generates 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; by determining the peak gas usage period, when the gas transmission time point is in the peak gas usage period, the peak shaving parameters of the target pipeline can be determined based on the distribution demand sequence and the initial gas supply parameters, fully adapting to the gas demands of each end-user.

[0078] It should be noted that the above description of process 200 is only for illustration and example, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to process 200 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0079] Figure 3 is an exemplary schematic diagram of determining the distribution demand sequence shown in some embodiments of this specification.

[0080] In some embodiments, the intelligent gas company management platform obtains the end - user characteristics 340 of the end - users of the target pipeline from the intelligent gas government safety supervision management platform through the intelligent gas government safety supervision sensing network platform; constructs a gas demand map 360 based on the historical usage data 310, historical monitoring data 320, pipeline equipment data 330, end - user characteristics 340, and meteorological data 350; and determines the sub - transmission demand sequence 380 through the demand determination model 370 based on the gas demand map 360.

[0081] The end - user characteristics 340 refer to the characteristic information related to end - users. In some embodiments, the end - user characteristics may include user type, historical complaint information, gas consumption scale, etc. Among them, the user type may include residential users, industrial users, or commercial users, etc. If the user type is an industrial user, the end - user characteristics may further include the factory type.

[0082] In some embodiments, the intelligent gas company management platform may construct a gas demand map 360 based on the historical usage data 310, historical monitoring data 320, pipeline equipment data 330, end - user characteristics 340, and meteorological data 350.

[0083] The pipeline equipment data 330 refers to the relevant information of gas pipelines. 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 can be obtained based on historical pipeline laying records.

[0084] The meteorological data 350 refers to the meteorological data of the region where the end - user is located at the current and future time points. For example, temperature, humidity, wind speed, weather conditions, etc. In some embodiments, the meteorological data can be obtained from a third - party platform, such as the weather forecast website.

[0085] In some embodiments, the nodes of the gas demand map 360 may include at least one of pipeline nodes and user nodes. For example, the nodes of the gas demand map 360 may include pipeline nodes and user nodes.

[0086] In some embodiments, the node characteristics of pipeline nodes may include historical monitoring data and pipeline equipment data. The node characteristics of user nodes may include historical usage data, end - user characteristics, and meteorological data.

[0087] In some embodiments, the node characteristics of user nodes may further include the gas stability demand. The gas stability demand is determined based on end - user characteristics and usage equipment parameters.

[0088] The usage device parameters refer to the relevant parameters of gas-using devices. In some embodiments, the usage device parameters may include the type of gas-using device and the service life of the gas device, etc. Among them, the type of gas-using device may be, for example, a wall-mounted boiler, a gas stove, a water heater, etc. In some embodiments, the usage device parameters can be obtained by the user uploading them by themselves.

[0089] The gas stability requirement is used to characterize the requirement of end-users for the stability of gas transmission. For example, for industrial users, gas is needed for product manufacturing, and the stability of gas flow has a great impact on product quality, so their gas stability requirement is relatively high.

[0090] In some embodiments, the intelligent gas company management platform can determine the gas stability requirement based on the characteristics of end-users and usage device parameters in various ways.

[0091] For example, the intelligent gas company management platform can construct a vector database based on a large number of historical end-user characteristics and a large number of historical usage device parameters, and determine the corresponding gas stability requirement based on matching vector retrieval. Among them, the vector database can include multiple reference feature vectors and the reference gas stability requirements corresponding to the reference feature vectors. Each reference feature vector can be constructed based on the historical end-user characteristics and historical usage device parameters actually collected during historical daily gas use. For example, a reference feature vector can be constructed based on a historical end-user characteristic and its corresponding historical usage device parameter.

[0092] The intelligent gas company management platform can determine the reference gas stability requirements corresponding to each reference feature vector based on historical feedback data. For example, during the gas supply process of the end-users corresponding to the respective reference feature vectors, when there are fluctuations in gas transmission, if feedback such as user complaints about gas fluctuations is obtained, or fluctuations in product quality or unstable equipment status during the corresponding period are obtained, it can be considered that the reference gas stability requirement corresponding to this reference feature vector is higher. Among them, gas transmission fluctuations refer to the unstable situation of gas during gas transmission in the target pipeline. For example, gas flow fluctuations, pressure fluctuations, etc. In some embodiments, gas transmission fluctuations can be calculated based on the gas data read by the meter deployed in the end-user's household pipeline. For example, the meter can be a flow meter or a pressure meter, etc.

[0093] The intelligent gas company management platform can construct a usage feature vector based on the characteristics of end-users and the usage device parameters corresponding to the characteristics of the end-users. The construction method of the reference feature vector is similar to that of the usage feature vector. In some embodiments, the intelligent 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, the 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 maximum similarity, or the similarity is greater than a threshold, etc.

[0094] An edge is used to connect nodes with a connectivity relationship.

[0095] The edge feature can include the gas flow direction.

[0096] 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.

[0097] In some embodiments, the input of the demand determination model 370 is the gas demand map 360, and the output is the subtransmission demand sequence 380.

[0098] In some embodiments, the demand determination model 370 is obtained by training with a training sample data set. The training process of the demand determination model 370 includes an initial training stage and a reinforcement training stage. The initial training stage refers to the pre-training stage when a large amount of general data is used as training data before being connected to the target pipeline. The reinforcement training stage refers to the stage of personalized customized training based on the corresponding data of the target pipeline as training data.

[0099] In some embodiments, the training data in the training sample data set includes training samples and their corresponding training labels. In some embodiments, the training samples can include sample gas demand maps, and the training labels can include the subtransmission demand sequences actually collected for the training samples at future sample times.

[0100] During training, the intelligent gas company management platform can input multiple training samples with training labels into the initial demand determination model, construct a loss function through the training labels and the results of the initial demand determination model, and iteratively update the parameters of the initial demand determination model based on the loss function by gradient descent or other methods. When the preset condition is met, the training of the demand determination model is completed, and a trained demand determination model is obtained. Among them, the preset condition can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0101] In the initial training stage, the above training is performed on the initial demand determination model using the training sample data set in the initial stage. In the reinforcement training stage, the above training is performed on the initial demand determination model trained in the initial training stage using the training sample data set in the reinforcement stage.

[0102] In some embodiments, in the initial training stage, the training sample data set in the initial stage is obtained based on a number of general data on a cloud platform. In some embodiments, the general data on the cloud platform may include the corresponding data of pipelines in multiple regions of the same city and the corresponding data of pipelines in other cities.

[0103] In some embodiments, in the reinforcement training stage, the training sample data set in the reinforcement stage is generated from the historical data actually collected by the target pipeline, and the proportion of the training samples corresponding to a time period is not less than a preset threshold. Wherein, the preset threshold is positively correlated with the total gas transmission volume in this time period. The training samples corresponding to a time period refer to the training samples composed of the historical data collected in the corresponding time period.

[0104] In some embodiments of this specification, the training process of the demand determination model includes an initial training stage and a reinforcement training stage. Training in stages can not only accelerate the training process of the model, improve the performance of the model, but also improve the prediction accuracy of the model. The model after reinforcement training can obtain a more accurate subtransmission demand sequence according to the actual situation in the target pipeline. And, the distribution of gas demand may be very uneven in different time periods. For example, the gas consumption during the morning and evening rush hours may be much higher than other 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 in 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.

[0105] In some embodiments of this specification, the intelligent gas company management platform determines the subtransmission demand sequence through the demand determination model, and makes full use of the characteristic that the demand determination model can accurately predict the future gas demand by learning historical patterns. So that the prediction result is closer to the actual situation. In addition, the model can integrate multiple data sources, including pipeline equipment data, user characteristics and meteorological data, to improve the comprehensiveness and accuracy of the prediction. For example, meteorological data can help predict the impact of extreme weather on gas demand.

[0106] Figure 4 It is an exemplary flowchart for controlling the standby gas source to supply gas according to the target calling parameters shown in some embodiments of this specification. As Figure 4 shown, process 400 includes the following steps.

[0107] In some embodiments, in order to further meet the gas usage requirements of users and reduce the gas supply pressure during peak periods, a gas storage reservoir can be set up in the pipeline network and gas storage devices can be set up on some or all of the most downstream distribution pipelines. Among them, the most downstream distribution pipeline refers to the distribution pipeline directly connected to the end users, and the most downstream distribution pipeline equipped with a gas storage device can be simply referred to as the end gas storage pipeline. The above-mentioned gas storage reservoir and end gas storage pipeline can be collectively referred to as the standby gas sources.

[0108] In some embodiments, the peak shaving parameters can also include target call parameters, and the peak shaving distribution instruction can also include a gas call instruction.

[0109] The target call parameter refers to the parameters related to the use of the standby gas source to be called and the gas to be called. In some embodiments, the target call parameters can include the target call object, the target call time, the target call gas volume, and the gas transmission object, etc. Among them, the target call object refers to the standby gas source to be called; the target call time refers to the specific time to call the gas; the target call gas volume refers to the gas output volume to be called; the gas transmission object refers to the end users to which the called standby gas source needs to be transported.

[0110] In some embodiments, the target call parameters are confirmed by the intelligent gas government safety supervision and management platform, and the intelligent gas government safety supervision and management platform can adjust the target call parameters based on the actual situation, and then return the adjusted target call parameters to the intelligent gas company management platform.

[0111] The gas call instruction refers to the gas call control instruction determined based on the target call parameters. For example, the gas call flow rate, the gas call flow velocity, etc.

[0112] Step 410, determine the initial call parameters based on the distribution demand sequence and the initial gas supply parameters.

[0113] The initial call parameter refers to the data related to the call of the standby gas source initially set. In some embodiments, the initial call parameters can include the call object, the call time, the call gas volume, and the gas transmission object, etc.

[0114] In some embodiments, the intelligent gas company management platform can determine the initial call parameters in various ways.

[0115] For example, based on the distribution demand sequence and the initial gas supply parameters, determine the net gas transmission demand of the end users, and determine the corresponding initial call parameters of the standby gas source based on the net gas transmission demand of the end users.

[0116] Among them, the net gas transmission demand = the gas demand corresponding to the end users - the actual supply of the target pipeline corresponding to the end users.

[0117] In some embodiments, the gas storage of different alternative gas sources has different call priorities. For example, the call priority of the end gas storage pipeline is greater than that of the gas storage reservoir. The intelligent gas company management platform can first control the end gas storage pipeline to replenish the corresponding terminal users with insufficient supply. If the gas storage in the end gas storage pipeline still cannot meet the gas demand of its corresponding terminal users, the intelligent gas company management platform then calls the gas storage reservoir to replenish gas for each terminal user.

[0118] In some embodiments, an end gas storage pipeline may correspond to (i.e., be directly connected to) multiple users. Among them, the users with insufficient gas supply (hereinafter referred to as the users to be replenished) are the objects that the end gas storage pipeline needs to replenish gas for.

[0119] Among the initial call parameters corresponding to the end gas storage pipeline, the gas transmission object is the user to be replenished corresponding to the end gas storage pipeline, the call object is the end gas storage pipeline, the call time is the time when the net gas transmission demand is greater than 0, and the call gas volume can be determined based on various methods.

[0120] For example, when the gas storage volume in the end gas storage pipeline is greater than or equal to the net gas transmission demand of the user to be replenished, the call gas volume in the initial call parameters of the end gas storage pipeline is the gas volume corresponding to the net gas transmission demand.

[0121] Another example is that when the gas storage volume in the end gas storage pipeline is less than the net gas transmission demand of the user to be replenished, the call gas volume in the initial call parameters of the end gas storage pipeline can be the gas volume when the end gas storage pipeline supplies its gas storage to the corresponding users to be replenished in a way of evenly dividing its gas storage or distributing its gas storage according to the distribution weight.

[0122] Among them, the distribution 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 demand of the user to be replenished. In some embodiments, if the gas replenishment provided by the end gas storage pipeline corresponding to the user to be replenished still cannot meet the gas demand of the user to be replenished, that is, when the gas replenishment volume provided by the end 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.

[0123] In some embodiments, the gas storage reservoir is connected to the gas distribution pipeline and is set at an upstream position close to the main pipeline, so as to enable gas replenishment to more terminal users based on the gas storage reservoir.

[0124] In some embodiments, the intelligent gas company management platform may obtain the user information of users with the demand for supplementary gas supply from gas storage facilities (hereinafter referred to as additional supplementary users). The user information may include information such as the additional supplementary volume and the demand time point. Among them, the additional supplementary volume may be the difference between the above-mentioned net gas transmission demand and the gas volume that the end gas storage pipeline can supply, and the demand time point may be the time when the net gas transmission demand is greater than 0.

[0125] In some embodiments, the number of gas storage facilities may be one or more. If there is only 1 gas storage facility, the additional supplementary volumes of all additional supplementary users are provided by this gas storage facility. In the initial call parameters corresponding to this gas storage facility, the gas transmission object is the additional supplementary user, the call object is this gas storage facility, the call time is the above-mentioned demand time point, and the call gas volume of each gas transmission object can be determined based on various methods. For example, if the storage volume of the gas storage facility is greater than or equal to the sum of the additional supplementary volumes of all additional supplementary users, the call gas volume of each gas transmission object is its corresponding additional supplementary volume.

[0126] If there are multiple gas storage facilities, the intelligent gas company management platform may summarize the storage volumes of all gas storage facilities and the additional supplementary volumes of all additional supplementary users. If the total storage volume is greater than or equal to the total additional supplementary volume, the intelligent gas company management platform may determine the additional supplementary users that each gas storage facility can supply and the corresponding call gas volume according to the principle of the shortest transmission path or the lowest transmission cost, etc., and then obtain the initial call parameters of each gas storage facility.

[0127] If the storage volumes of multiple gas storage facilities (including one gas storage facility) are less than the total additional supplementary volume, the intelligent gas company management platform may, in accordance with the principle of preferentially satisfying additional supplementary users with higher priorities, first conduct gas supplementary supply to additional supplementary users with higher priorities. If there is still gas remaining, it may further conduct gas supplementary supply to additional supplementary users with the next highest priority until the gas storage volumes of all gas storage facilities are allocated. Each time gas is supplemented to an additional supplementary user, if the remaining available call gas volume is greater than or equal to the additional supplementary volume of this additional supplementary user, the call gas volume of this additional supplementary user is this additional supplementary volume; if the remaining available call gas volume is less than the additional supplementary volume of this additional supplementary user, the call gas volume of this additional supplementary user is the remaining available call gas volume. Among them, the remaining available call gas volume = gas storage volume - the determined call gas volume, and then the initial call parameters of each gas storage facility are obtained.

[0128] Step 420: Upload the initial call parameters to the intelligent gas government safety supervision management platform and obtain the target call parameters feedback by the intelligent gas government safety supervision management platform.

[0129] In some embodiments, the intelligent gas government safety supervision and management platform can adjust the initial call parameters according to the actual situation, and then determine the target call parameters. The adjustment of the initial call parameters can include adjusting the gas consumption or priority of some users to be supplemented or additional users, adjusting the supply volume of some end gas storage pipelines or gas storage facilities, etc. If the target call parameters feedback by the intelligent gas government safety supervision and management platform only adjust the supply volume of the end gas storage pipelines or gas storage facilities, then the target call object, target call time, target gas consumption and gas transmission object in the target call parameters can be updated, and the gas storage volume of the gas storage facility and the end pipeline can be replaced with the supply volume of the end gas storage pipeline or gas storage facility adjusted by the intelligent gas government safety supervision and management platform.

[0130] Step 430: Based on the target call parameters and the initial gas supply parameters, determine the peak shaving and distribution parameters of the distribution pipeline.

[0131] The peak shaving and distribution parameters of the distribution pipeline refer to the control parameters of the distribution pipeline, including the supply sequence of each corresponding terminal user and the gas distribution volume supplied to each terminal user, etc.

[0132] In some embodiments, the intelligent gas company management platform can determine the peak shaving and distribution parameters of the distribution pipeline based on the target call parameters and the initial gas supply parameters through various methods.

[0133] For example, the intelligent gas company management platform can determine the supply sequence of terminal users based on the priority of terminal users. The higher the priority, the earlier the supply sequence. The gas distribution volume supplied to each terminal user can be the sum of the gas volume obtained by the terminal user from the gas supply source and the gas supplementary supply volume obtained from the standby gas source. Among them, the gas supplementary supply volume obtained from the standby 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 specific descriptions, please refer to Figure 2 the corresponding content.

[0134] In some embodiments, the intelligent gas company management platform can determine the distribution priority based on the gas stability requirement and the importance of users; based on the distribution priority, target call parameters and initial gas supply parameters, determine the peak shaving and distribution parameters of the distribution pipeline.

[0135] The distribution priority refers to the priority of the supply object determined based on the gas stability requirement and the importance of users.

[0136] For more content about the gas stability requirement, please refer to Figure 3 and its related descriptions.

[0137] In some embodiments, the user importance level can be determined based on preset rules. For example, the preset rule can be that the importance level of industrial users is greater than that of residential users; the greater the average daily gas consumption of a user, the greater the monthly payment amount of the user, the longer the time the user has been connected to the gas system, and the more times the user pays the fee on time, the higher the importance level of the user.

[0138] In some embodiments, the intelligent gas company management platform can determine the subtransmission priority through various methods based on the gas stability requirement and the user importance level. For example, for users with strict gas stability requirements, the subtransmission priority is relatively high; or for users with a relatively high importance level, the subtransmission priority is determined to be relatively high.

[0139] In some embodiments, the method for determining the peak shaving subtransmission parameters of the subtransmission pipeline based on the subtransmission priority, the target call parameters, and the initial gas supply parameters is the same as the method for determining the peak shaving subtransmission parameters of the subtransmission pipeline based on the target call parameters and the initial gas supply parameters described above. The difference is that when the priority is required to determine information accordingly, the subtransmission priority is used instead of the original priority.

[0140] In some embodiments of this specification, the intelligent gas company management platform determines the peak shaving subtransmission parameters of the subtransmission pipeline based on the subtransmission priority and the like, which can make the determined peak shaving subtransmission parameters better meet the actual requirements and realize the reasonable distribution of gas.

[0141] In some embodiments, when the intelligent gas company management platform performs the subtransmission peak shaving operation, it determines whether the actual subtransmission parameters and the peak shaving subtransmission parameters meet the preset difference condition, and / or whether the actual call parameters and the target call parameters meet the preset difference condition based on the actual monitoring data; in response to meeting the preset difference condition, it generates a correction instruction based on the actual monitoring data, the actual subtransmission parameters, and the actual call parameters; and issues the correction instruction to the intelligent gas equipment object platform to correct the operating parameters of the subtransmission control device.

[0142] The actual monitoring data refers to the relevant data of the gas monitored in real time. In some embodiments, the actual monitoring data can include the actual subtransmission parameters and the actual call parameters.

[0143] The preset difference condition refers to the allowable difference between the actual subtransmission parameters and the peak shaving subtransmission parameters and / or the actual call parameters and the target call parameters set in advance. In some embodiments, the preset difference condition can be set manually or based on historical experience. For example, the preset difference condition can be that there is a difference or the difference exceeds the preset range, etc.

[0144] The actual subtransmission parameters refer to the relevant data of the gas subtransmission monitored in real time. For example, the actual supply order of each terminal user and the actual gas subtransmission volume supplied to each terminal user.

[0145] The intelligent gas management platform of the gas company can obtain the actual supply sequence of each end-user in real time and the actual gas distribution volume supplied to each end-user, and compare the obtained actual distribution parameters with the peak-shaving distribution parameters determined above. If there are differences or the differences exceed the preset range, a correction instruction is generated.

[0146] The existence of differences in the gas distribution volume or the differences exceeding the preset range may mean that the value of (the gas distribution volume in the peak-shaving distribution parameters - the actual gas distribution volume supplied) / the gas distribution volume in the peak-shaving distribution parameters is greater than the preset difference threshold. Among them, the preset difference threshold can be obtained by querying the preset table based on the corresponding information of the end-user. For example, end-users with high gas stability requirements can set a lower preset difference threshold.

[0147] The actual call parameters refer to the actual call situation of the standby gas sources. For example, it includes the actual call time, actual gas volume called, and actual gas transmission object of the gas storage reservoir and the end gas storage pipeline. The intelligent gas management platform of the gas company can obtain the actual call situation of each standby gas source in real time as the actual call parameters, and compare the obtained actual call parameters with the target call parameters determined above. If there are differences, a correction instruction is generated.

[0148] The correction instruction refers to the instruction to modify and adjust the operating parameters of the gas distribution control device.

[0149] In some embodiments, modifying the operating parameters of the distribution control device may include modifying the valve opening, pressure regulating parameters, gas flow rate, gas velocity, etc. of the distribution control device.

[0150] In some embodiments, the intelligent gas management platform of the gas company can generate correction instructions in various ways based on the 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 most downstream distribution pipeline of the corresponding end-user can be adjusted accordingly.

[0151] In some embodiments, if there are objective limitations such as equipment aging and pipeline blockage that prevent the actual distribution parameters from conforming to the peak-shaving distribution parameters, or the actual call parameters cannot conform to the target call parameters, the actual call parameters can be used as the 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 are determined, and the actual monitoring data is obtained again for the above correction judgment.

[0152] In some embodiments of this specification, the intelligent gas management platform of the gas company generates a correction instruction based on whether the actual monitoring data, actual gas transmission parameters, and actual call parameters respectively meet the preset difference conditions with the peak shaving gas transmission parameters and target call parameters, so as to correct the operating parameters of the gas transmission control device, ensuring that during the actual gas transmission process, the operating parameters of the equipment can be detected and adjusted in a timely manner.

[0153] In some embodiments, the intelligent gas management platform of the gas company can determine candidate gas transmission parameters based on the target call parameters, gas transmission demand sequence, and initial gas supply parameters; determine the evaluation score of the candidate gas transmission parameters through a peak shaving evaluation model based on the candidate gas transmission parameters, end-user characteristics, and pipeline characteristic map; and determine the peak shaving gas transmission parameters based on the evaluation score.

[0154] Candidate gas transmission parameters refer to the data to be determined as peak shaving gas transmission parameters.

[0155] In some embodiments, the intelligent gas management platform of the gas company uses the peak shaving gas transmission parameters of the gas transmission pipeline determined based on the target call parameters and initial gas supply parameters as the initial parameters, and based on a preset step size, makes random adjustments within a preset limit to obtain multiple groups of candidate gas transmission parameters. Among them, the preset limit can be determined based on the gas transmission demand sequence. For example, the gas transmission volume in the candidate gas transmission parameters cannot be greater than the total gas demand of the end-users corresponding to the gas transmission pipeline.

[0156] In some embodiments, the intelligent gas management platform of the gas company can determine the evaluation score of the candidate gas transmission parameters through a peak shaving evaluation model based on the candidate gas transmission parameters, end-user characteristics, and pipeline characteristic map.

[0157] In some embodiments, the peak shaving evaluation model is a machine learning model. In some embodiments, the peak shaving evaluation model is a Convolutional Neural Network (CNN) model.

[0158] In some embodiments, the input of the peak shaving evaluation model can include candidate gas transmission parameters, end-user characteristics, pipeline characteristic map, meteorological data, and gas transmission demand sequence.

[0159] In some embodiments, the output of the peak shaving evaluation model is the evaluation score of the candidate gas transmission parameters.

[0160] In some embodiments, the peak shaving evaluation model is obtained by training with a training sample data set. The training process of the peak shaving evaluation model includes an initial training stage and a reinforcement training stage. The initial training stage refers to the pre-training stage when the corresponding data of the target pipeline has not been accessed as training data, and the reinforcement training stage refers to the stage of personalized customized training based on the corresponding data of the target pipeline as training data.

[0161] 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, sample distribution demand sequences; the training label is the evaluation score corresponding to the sample.

[0162] During training, the intelligent gas company management platform can input multiple training samples with training labels into the initial peak shaving evaluation model, construct a loss function through the training labels and the results of the initial peak shaving evaluation model, and iteratively update the parameters of the initial peak shaving evaluation model based on the loss function by gradient descent or other methods. When the preset conditions are met, the training of the peak shaving evaluation model is completed, and a trained peak shaving evaluation model is obtained. Among them, the preset conditions can be that the loss function converges, the number of iterations reaches a threshold, etc.

[0163] In the initial training stage, the above training is performed on the initial peak shaving evaluation model using the training sample dataset in the initial stage. In the reinforcement training stage, the above training is performed on the initial peak shaving evaluation model trained in the initial training stage using the training sample dataset in the reinforcement stage.

[0164] In some embodiments, the label can be obtained by actually collecting the actual feedback of end-users. For example, the gas usage experience, user complaint rate, production efficiency, etc. are used as the basis for scoring to determine the evaluation score. For another example, corresponding monitoring devices are used to obtain product quality changes, gas supply stability, production efficiency, etc. to evaluate the evaluation score of the peak shaving and distribution parameters.

[0165] In some embodiments of this specification, the intelligent gas company management platform determines the evaluation score of the candidate distribution parameters based on the candidate distribution parameters, end-user characteristics, and pipeline characteristic maps through the peak shaving evaluation model, and then determines the peak shaving and distribution parameters. Combining with the machine model, more accurate peak shaving and distribution parameters are obtained, making the operation of the distribution control device more in line with actual requirements.

[0166] In some embodiments, the intelligent gas company management platform determines the peak shaving and distribution parameters in various ways based on the evaluation score. For example, the candidate distribution parameter with the largest evaluation score is determined as the peak shaving and distribution parameter.

[0167] Step 440, generate a gas call instruction based on the target call parameter, and send the gas call instruction to the intelligent gas equipment object platform to control the standby gas source to supply gas according to the target call parameter.

[0168] In some embodiments of this specification, the initial call parameters are uploaded to the intelligent gas government safety supervision and management platform, the target call parameters fed back by the intelligent gas government safety supervision and management platform are obtained, the peak shaving and distribution parameters of the subtransmission pipeline are determined, and a gas call instruction is generated to achieve real-time government monitoring and processing and reasonably adjust the gas subtransmission operation.

[0169] One or more embodiments of this specification provide a computer-readable storage medium, and the storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the intelligent gas pipeline subtransmission control method.

[0170] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0171] At the same time, this specification uses specific words to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0172] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numbers and letters, or the use of other names in this specification are not used to limit the order of the processes and methods in this specification. Although some currently useful invention embodiments are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. 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 conform to the essence 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 through software solutions, such as installing the described system on existing servers or mobile devices.

[0173] Similarly, it should be noted that, in order to simplify the presentation disclosed in this specification and thus assist in the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, various features are sometimes grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0174] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used to describe the embodiments are modified by the modifiers "about", "approximate", or "substantially" in some examples. Unless otherwise specified, "about", "approximate", or "substantially" indicate that the said numbers allow a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values may vary according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, such numerical settings are made as precise as possible within the feasible range.

[0175] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. Except for the application history documents that are inconsistent with or conflict with the content of this specification, and except for the documents that limit the broadest scope of the claims of this specification (currently or subsequently attached to this specification). It should be noted that if there are inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0176] Finally, it should be understood that the embodiments described in this specification are only used 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 can be considered to be in accordance with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A smart gas pipeline distribution control method, characterized in that: Executed by the Smart Gas Company Management Platform, including: Obtain 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 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; Determine the 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 the peak shaving parameters of the target pipeline based on the distribution demand sequence and the initial gas supply 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 peak-shaving operations according to the peak-shaving parameters.

2. The method according to claim 1, characterized in that The determining of the distribution demand sequence based on the historical usage data comprises: Acquire end user characteristics of the end user of the target pipeline; Constructing a gas demand map based on the historical usage data, the historical monitoring data, the pipeline equipment data, the end-user characteristics and the meteorological data; Based on the gas demand map, the distribution demand sequence is determined by a demand determination model, and the demand determination model is a machine learning model.

3. The method according to claim 1, characterized in that The peak-shaving parameters also include target call parameters, and the peak-shaving distribution instructions also include gas call instructions; The method further comprises: Determining initial call parameters based on the distribution demand sequence and the initial gas supply parameters; Upload the initial call parameters to the smart gas government safety supervision and management platform, and obtain the target call parameters fed back by the smart gas government safety supervision and management platform; Determining peak load distribution parameters of the distribution pipeline based on the target call parameters and the initial gas supply parameters; and Based on the target calling parameters, the gas calling instruction is generated to control the backup gas source to supply gas according to the target calling parameters.

4. The method according to claim 3, characterized in that The step of determining the peak load distribution parameters of the distribution pipeline based on the target call parameters and the initial gas supply parameters includes: Determining candidate distribution parameters based on the target call parameter, the distribution demand sequence and the initial gas supply parameter; Based on the candidate distribution parameters, the terminal user characteristics, and the pipeline characteristic map, an evaluation score of the candidate distribution parameters is determined by a peak shaving evaluation model; the peak shaving evaluation model is a machine learning model; Based on the evaluation score, the peak load distribution parameter is determined.

5. A smart gas pipeline distribution control Internet of Things system, characterized in that: 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.

6. The smart gas pipeline distribution control Internet of Things system according to claim 5 is characterized in that: The Internet of Things 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, which are respectively configured on the same or different servers. 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.

7. The smart gas pipeline distribution control Internet of Things system according to claim 5 is characterized in that: The smart gas company management platform is configured as follows: Obtaining, through the smart gas government safety supervision sensor network platform, terminal user characteristics of the terminal user of the target pipeline from the smart gas government safety supervision management platform; Constructing a gas demand map based on the historical usage data, the historical monitoring data, the pipeline equipment data, the end-user characteristics and the meteorological data; Based on the gas demand map, the distribution demand sequence is determined by a demand determination model, and the demand determination model is a machine learning model.

8. The smart gas pipeline distribution control Internet of Things system according to claim 5 is characterized in that: The peak-shaving parameters also include target call parameters, and the peak-shaving instructions also include gas call instructions; The smart gas company management platform is further configured as follows: Determining initial call parameters based on the distribution demand sequence and the initial gas supply parameters; Uploading the initial call parameters to the smart gas government safety supervision and management platform to obtain target call parameters fed back by the smart gas government safety supervision and management platform; Determining peak load distribution parameters of the distribution pipeline based on the target call parameters and the initial gas supply parameters; as well as Based on the target call parameters, the gas call instruction is generated, 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.

9. The smart gas pipeline distribution control Internet of Things system according to claim 8 is characterized in that: The smart gas company management platform is further configured as follows: Determining candidate distribution parameters based on the target call parameter, the distribution demand sequence and the initial gas supply parameter; Based on the candidate distribution parameters, the terminal user characteristics, and the pipeline characteristic map, an evaluation score of the candidate distribution parameters is determined by a peak shaving evaluation model; the peak shaving evaluation model is a machine learning model; Based on the evaluation score, the peak load distribution parameter is determined.

10. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the method according to any one of claims 1 to 4.

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