Intelligent hydrogen-mixed gas delivery method and internet of things system

Abstract

The application provides a smart hydrogen-doped gas delivery method and an Internet of Things system. The method is executed by a gas company management platform of the smart hydrogen-doped gas delivery Internet of Things system, and comprises the following steps: obtaining gas data of a preset gas pipeline and terminal demand data of a gas terminal corresponding to the preset gas pipeline; determining hydrogen-doping data of the preset gas pipeline based on the gas data and the terminal demand data; determining injection parameters based on the hydrogen-doping data and sending the injection parameters to a hydrogen input device corresponding to the preset gas pipeline to control a pressure regulating unit of the hydrogen input device and inject hydrogen into the preset gas pipeline according to the injection parameters. The above method can better ensure the safety of the hydrogen-doping process on the premise of meeting the heat value demand of users.

Description

TECHNICAL FIELD

[0001] The present specification relates to the field of gas delivery, in particular to a smart gas hydrogen-doped gas delivery method and an Internet of Things system. BACKGROUND

[0002] The use of hydrogen-doped gas not only increases the calorific value of natural gas, but also greatly reduces carbon dioxide emissions. However, the life cycle management of hydrogen-doped gas also faces many challenges. The life cycle of hydrogen-doped gas includes the whole process of hydrogen-doped gas production, transportation and use. During the production and transportation of hydrogen-doped gas, due to the addition of flammable and explosive hydrogen gas, special attention needs to be paid to the amount of hydrogen gas added to the gas to ensure the safety of the gas pipeline network during the production and transportation of hydrogen-doped gas. In the use stage of hydrogen-doped gas, since the heat value of the main component of gas, methane, is different from that of hydrogen, the proportion of hydrogen will also affect the combustion effect of hydrogen-doped gas. SUMMARY

[0003] In order to better ensure the safety and stability of the production and transportation process of hydrogen-doped gas, and better ensure the combustion effect of hydrogen-doped gas, the present application provides a smart gas hydrogen-doped gas delivery method and an Internet of Things system and a medium.

[0004] The invention includes a smart gas hydrogen-doped gas delivery Internet of Things system, which comprises a user platform, a government regulation service platform, a government regulation management platform, a government regulation sensing network platform, a government regulation object platform, a gas company sensing network platform, and a smart gas equipment object platform. The user platform is configured to obtain terminal demand data of a preset gas pipeline and upload the terminal demand data to the government regulation management platform through the government regulation service platform. The government regulation management platform comprises a government safety regulation management platform. The government regulation service platform comprises a government safety regulation service platform. The government regulation sensing network platform comprises a government safety regulation sensing network platform. The government regulation object platform comprises a gas company management platform. The smart gas equipment object platform comprises a hydrogen input device and a gas monitoring device. The hydrogen input device is arranged in the preset gas pipeline. The hydrogen input device comprises a hydrogen storage unit, a hydrogen buffer unit, a hydrogen pressure regulating unit, and a delivery pipeline. The hydrogen storage unit, the hydrogen buffer unit, and the hydrogen pressure regulating unit are connected through the delivery pipeline. The hydrogen storage unit stores hydrogen input into the pipeline network. The hydrogen buffer unit is configured to buffer the hydrogen input into the pipeline network. The hydrogen pressure regulating unit is configured to adjust the pressure of the output hydrogen based on injection parameters. The gas monitoring device is arranged in the gas pipeline of the gas pipeline network to obtain gas data of the gas pipeline. The gas company management platform is configured to obtain the gas data of the preset gas pipeline from the smart gas equipment object platform through the gas company sensing network platform, determine hydrogen-doped data of the preset gas pipeline based on the gas data and the terminal demand data, determine injection parameters based on the hydrogen-doped data, and send the injection parameters to the hydrogen input device corresponding to the preset gas pipeline to control the pressure regulating unit of the hydrogen input device and inject hydrogen into the preset gas pipeline according to the injection parameters.

[0005] The invention also includes a smart gas hydrogen-doped gas delivery method, which is executed by a gas company management platform of a smart gas hydrogen-doped gas delivery Internet of Things system. The method comprises obtaining gas data of a preset gas pipeline and terminal demand data of a gas terminal corresponding to the preset gas pipeline, determining hydrogen-doped data of the preset gas pipeline based on the gas data and the terminal demand data, determining injection parameters based on the hydrogen-doped data, and sending the injection parameters to a hydrogen input device corresponding to the preset gas pipeline to control a pressure regulating unit of the hydrogen input device and inject hydrogen into the preset gas pipeline according to the injection parameters.

[0006] By the above method and the Internet of Things system, the following beneficial effects can be achieved, including but not limited to: forming an information running closed loop between various functional platforms, coordinating and regularly running, realizing the safety and stability of the hydrogen-doped gas production and transportation process on the premise of meeting the user's heat value demand, and better ensuring the combustion effect of the hydrogen-doped gas. BRIEF DESCRIPTION OF DRAWINGS

[0007] The present specification will be further illustrated in the manner of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same numbers represent the same structures, in which:

[0008] Figure 1 is a schematic diagram of a platform structure of a smart gas hydrogen-doped gas delivery Internet of Things system according to some embodiments of the present specification;

[0009] Figure 2 is an exemplary flowchart of a smart gas hydrogen-doped gas delivery method according to some embodiments of the present specification;

[0010] Figure 3 is a schematic diagram of determining hydrogen-doped data according to some embodiments of the present specification;

[0011] Figure 4 is a schematic diagram of determining injection parameters according to some embodiments of the present specification.

[0012] BRIEF DESCRIPTION OF DRAWINGS: 100 - smart gas hydrogen-doped gas delivery Internet of Things system; 110 - user platform; 120 - government regulatory service platform; 121 - government safety regulatory service platform; 130 - government regulatory management platform; 131 - government safety regulatory management platform; 140 - government regulatory sensing network platform; 141 - government safety regulatory sensing network platform; 150 - government regulatory object platform; 151 - gas company management platform; 160 - gas company sensing network platform; 170 - smart gas equipment object platform; 311 - historical data; 321 - maximum hydrogen-doped ratio; 322 - gas data; 323 - gas usage data; 331 - node; 332 - edge; 330 - gas atlas; 340 - estimation model; 351 - hydrogen-doped data; 352 - heat value fluctuation range; 410 - gas sequence data; 411 - gas pressure; 420 - original pressure mean value; 430 - pressure fluctuation value; 440 - determination model; 450 - injection effective value; 460 - injection parameters; 470 - candidate injection parameters; 480 - environmental data; 490 - gas usage data sequence. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, the present specification can also be applied to other similar scenarios without creative labor on the basis of these drawings. Unless it is clear from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0014] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0015] Unless the context clearly indicates otherwise, the words "one", "an", "a", and / or "the" do not mean singular, but can also include plural. Generally, the terms "comprise" and "include" only indicate that the steps and elements explicitly identified are included, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0016] Flowcharts are used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily performed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more steps of the operation can be removed from these processes.

[0017] Figure 1 is an exemplary schematic diagram of a smart gas hydrogen blending gas delivery Internet of Things system according to some embodiments of the present specification. The smart gas hydrogen blending gas delivery Internet of Things system according to the embodiments of the present specification will be described in detail below. It should be noted that the following embodiments are only used to explain the present specification and do not constitute a limitation on the present specification.

[0018] In some embodiments, as shown in Figure 1 The smart gas hydrogen blending gas delivery Internet of Things system 100 includes a user platform 110, a government regulation service platform 120, a government regulation management platform 130, a government regulation sensing network platform 140, a government regulation object platform 150, a gas company sensing network platform 160, and a smart gas equipment object platform 170.

[0019] The user platform 110 refers to a platform for interacting with users. In some embodiments, the user platform is configured to obtain terminal demand data of a preset gas pipeline, and upload the terminal demand data to the government regulation management platform through the government regulation service platform. In some embodiments, the user platform is configured as a terminal device.

[0020] In some embodiments, the user platform 110 can interact with the government regulation service platform 120.

[0021] The government regulation service platform 120 refers to a platform for receiving and processing perception information by the government. In some embodiments, the government regulation service platform 120 includes a government security regulation service platform 121.

[0022] In some embodiments, the government regulation service platform 120 can interact with the government regulation management platform 130.

[0023] The government security regulation service platform 121 refers to a platform for receiving and processing security-related perception information by the government.

[0024] In some embodiments, the government security regulation service platform 121 can interact with the user platform 110.

[0025] The government regulation management platform 130 refers to a comprehensive management platform for processing and regulating information by the government.

[0026] In some embodiments, the government regulation management platform 130 is configured to regulate the gas company management platform 151 to execute the smart gas hydrogen-blended gas delivery method. Details of the smart gas hydrogen-blended gas delivery method can be found in the later part of the specification.

[0027] In some embodiments, the government regulation management platform can further include a processor. The processor can process data and / or information obtained from other platforms. The processor can execute program instructions based on the data, information and / or processing results to perform one or more functions described in the specification.

[0028] In some embodiments, the government regulation management platform 130 can also be configured on a server used by the government. The server can process data and / or information obtained from other platforms. The server can execute program instructions based on the data, information and / or processing results to perform one or more functions described in the specification.

[0029] In some embodiments, the government regulation management platform 130 interacts with the government security regulation service platform 121 and the government regulation sensing network platform 140.

[0030] In some embodiments, the government regulatory management platform includes a government safety regulatory management platform 131. The government safety regulatory management platform 131 refers to a platform for safety regulatory management of gas pipe networks.

[0031] In some embodiments, the government safety regulatory management platform 131 can coordinate the contact and cooperation between various functional platforms, and gather all information of the Internet of Things to provide sensing management and control management functions for the Internet of Things operation system.

[0032] In some embodiments, the government safety regulatory management platform 131 can interact with the government safety regulatory sensing network platform 141.

[0033] The government regulatory sensing network platform 140 refers to a platform for interaction between the government regulatory management platform 130 and the gas company management platform 151, which is configured as a communication device and / or a server.

[0034] In some embodiments, the government regulatory sensing network platform 140 can interact with the government regulatory management platform 130 upwardly and with the gas company management platform 151 downwardly.

[0035] The government safety regulatory sensing network platform 141 refers to a functional platform for management of sensing communication of the government. In some embodiments, the government safety regulatory sensing network platform 141 can be configured as a communication network or a gateway, etc., and can realize the functions of sensing communication of sensing information and control information.

[0036] In some embodiments, the government safety regulatory sensing network platform 141 can interact with the government safety regulatory management platform 131 upwardly and with the gas company management platform 151 downwardly. For example, the gas company management platform 151 can send gas data, gas supply data, environmental data, etc., and related data of hydrogen-blended gas production and transportation to the government safety regulatory management platform 131 through the government safety regulatory sensing network platform 141.

[0037] The government regulatory object platform 150 refers to a platform for generation of government regulatory information and execution of control information. In some embodiments, the government regulatory object platform includes the gas company management platform 151 and key gas using enterprises.

[0038] In some embodiments, the government regulatory object platform 150 interacts with the government regulatory sensing network platform 140 upwardly and with the gas company sensing network platform 160 downwardly.

[0039] The gas company management platform 151 refers to a comprehensive management platform for gas company information.

[0040] In some embodiments, the gas company management platform 151 is configured to perform a smart gas hydrogen-blended gas delivery method. Details of the smart gas hydrogen-blended gas delivery method can be found in the latter part of the specification.

[0041] In some embodiments, the gas company management platform 151 can include a processor. The processor can process data and / or information obtained from other platforms. The processor can execute program instructions based on the data, information, and / or processing results to perform one or more functions described in the specification.

[0042] The gas company sensing network platform 160 refers to a platform for managing sensing information of the gas company. In some embodiments, the gas company sensing network platform can be configured as a communication network or a gateway, etc.

[0043] In some embodiments, the gas company sensing network platform 160 interacts with the government regulatory object platform 150 upwardly, and with the smart gas equipment object platform 170 downwardly.

[0044] The smart gas equipment object platform 170 refers to a functional platform for generating and controlling information based on sensing information of the gas company. In some embodiments, the smart gas equipment object platform 170 includes a hydrogen input device and a gas monitoring device.

[0045] The hydrogen input device refers to a device or equipment for inputting hydrogen into a gas pipeline.

[0046] In some embodiments, the hydrogen input device includes a hydrogen storage unit, a hydrogen buffer unit, a hydrogen pressure regulating unit, and a delivery pipeline.

[0047] The hydrogen storage unit is a structure for storing hydrogen. In some embodiments, the hydrogen storage unit can be a storage tank or other equipment that can be used to store hydrogen.

[0048] The hydrogen buffer unit is a structure for pressure in the hydrogen storage unit. The hydrogen buffer unit can be arranged inside the hydrogen storage unit, or in communication with the hydrogen storage unit through a delivery pipeline.

[0049] In some embodiments, the hydrogen buffer unit can include a pressure regulating valve, a pressure sensor. The hydrogen buffer unit can automatically adjust the pressure in the hydrogen storage unit based on the feedback signal of the pressure sensor, so as to keep it within a safe range.

[0050] The hydrogen pressure regulating unit is used to adjust the pressure and flow rate of hydrogen output to the pipeline network based on injection parameters. In some embodiments, the hydrogen pressure regulating unit can be in communication with the hydrogen storage unit through a delivery pipeline, or in communication with the hydrogen buffer unit connected to the hydrogen storage unit through a delivery pipeline.

[0051] In some embodiments, the hydrogen pressure regulating unit can include a flow control valve, a pressure control valve, a pressure sensor, etc. The hydrogen pressure regulating unit can adjust the amount of hydrogen output by the hydrogen storage unit through the flow control valve, and adjust the pressure of the output hydrogen through the pressure control valve.

[0052] The gas monitoring device is a device for obtaining various monitoring data in the gas pipeline network. In some embodiments, the gas monitoring device can include a gas component analyzer, a pressure sensing device, a gas flow meter, etc.

[0053] In some embodiments, the gas monitoring device is arranged in the gas pipeline of the gas pipeline network or the gas terminal.

[0054] In some embodiments, the platform in the smart gas hydrogen-blended gas delivery Internet of Things system can be divided into a smart gas primary network and a smart gas secondary network. The smart gas primary network refers to a network for government users to supervise the operation of the gas pipeline network, and the smart gas secondary network is a network including the operation of the gas pipeline network. In some embodiments, the same platform in the smart gas hydrogen-blended gas delivery Internet of Things system can assume different roles in the smart gas primary network and the smart gas secondary network.

[0055] In some embodiments, the smart gas primary network at least includes a smart gas primary network user platform, a smart gas primary network service platform, a smart gas primary network management platform, a smart gas primary network sensing network platform, and a smart gas primary network object platform. The smart gas primary network user platform includes a user platform, the smart gas primary network service platform includes a government supervision service platform, the smart gas primary network management platform includes a government supervision management platform, the smart gas primary network sensing network platform includes a government supervision sensing network platform, and the smart gas primary network object platform includes a government supervision object platform. The government supervision object platform can be a gas company management platform.

[0056] In some embodiments, the smart gas secondary network includes a smart gas secondary network user platform, a smart gas secondary network service platform, a smart gas secondary network management platform, a smart gas secondary network sensing network platform, and a smart gas secondary network object platform. The smart gas secondary network user platform includes a gas user platform, the smart gas secondary network service platform includes a gas user service platform, the smart gas secondary network management platform includes a gas company management platform, the smart gas secondary network sensing network platform includes a gas company sensing network platform, and the smart gas secondary network object platform includes a gas equipment object platform.

[0057] For more detailed description of the smart gas hydrogen-blended gas delivery Internet of Things system and its execution of the smart gas hydrogen-blended gas delivery method, please refer to the related description of Figures 2 to 4 .

[0058] In some embodiments of the present specification, the smart gas hydrogen-doped gas delivery Internet of Things system can form an information operation closed loop between various functional platforms, coordinate and operate regularly, realize the safety and stability of the hydrogen-doped gas production and transportation process, and better ensure the combustion effect of hydrogen-doped gas.

[0059] It should be noted that the above description of the smart gas hydrogen-doped gas delivery Internet of Things system and its platform is for the convenience of description, and cannot limit the present specification to the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, various platforms can be combined or connected with other platforms without departing from the principle.

[0060] In some embodiments, when implementing the smart gas hydrogen-doped gas delivery method, the government regulatory management platform can obtain gas data of a preset gas pipeline and terminal demand data of a terminal corresponding to the preset gas pipeline; based on the gas data and the terminal demand data, determine hydrogen-doping data of the preset gas pipeline; based on the hydrogen-doping data, determine injection parameters and send the injection parameters to a hydrogen input device corresponding to the preset gas pipeline to control a pressure regulating unit of the hydrogen input device to inject hydrogen into the preset gas pipeline according to the injection parameters.

[0061] Figure 2 is an exemplary flowchart of the smart gas hydrogen-doped gas delivery method according to some embodiments of the present specification. As shown in Figure 2 , the flow 200 includes the following steps. In some embodiments, the flow 200 can be executed by the government regulatory management platform of the smart gas hydrogen-doped gas delivery Internet of Things system.

[0062] Step 210, obtaining gas data of a preset gas pipeline and terminal demand data of a terminal corresponding to the preset gas pipeline.

[0063] The preset gas pipeline is the most upstream gas pipeline among the gas pipelines that need to be injected with hydrogen. The downstream of the preset gas pipeline includes one or more gas terminals that need to use hydrogen-doped gas. By doping hydrogen in the preset gas pipeline, the gas terminals that need to use hydrogen-doped gas in the downstream of the preset gas pipeline can obtain hydrogen-doped gas. The direction from upstream to downstream is the direction of gas transmission.

[0064] The gas data is data representing different substances in the gas and their contents.

[0065] In some embodiments, the government regulation management platform obtains the gas data of the preset gas pipeline from the gas company management platform through the government regulation sensing network platform. The gas data in the gas company management platform is collected by the smart gas equipment object platform through the gas composition analyzer in the gas monitoring device and uploaded through the gas company sensing network platform.

[0066] The gas terminal refers to the gas equipment corresponding to the terminal user of the gas. The gas terminal includes at least one of the gas equipment corresponding to the civil user, the gas equipment corresponding to the industrial user, and the like, and can also be the gas equipment corresponding to other types of users, which can be determined according to actual conditions.

[0067] The terminal demand data refers to the demand of the gas terminal for the gas. The terminal demand data includes but is not limited to the heat value demand.

[0068] The heat value refers to the heat released when a unit mass or volume of fuel is completely combusted. The higher the heat value, the better the quality of the fuel. In some embodiments, the gas terminals corresponding to different types of users have different demands for the heat value of the gas. For example, the heat value demand of the industrial user is usually higher than that of the civil user.

[0069] In some embodiments, the government regulation management platform can obtain the terminal demand data in various ways. For example, the government regulation management platform can obtain the gas supply data in the gas company management platform through the government regulation sensing network platform, determine the minimum value of the heat value corresponding to the evaluation higher than the evaluation threshold in the foregoing gas supply data, and determine the minimum value as the terminal demand data. For another example, the government regulation management platform can obtain the terminal demand data uploaded by the user through the user platform through the government regulation service platform.

[0070] In step 220, the hydrogen blending data of the preset gas pipeline is determined based on the gas data and the terminal demand data.

[0071] The hydrogen blending data is data representing the amount of hydrogen injected into the preset gas pipeline. In some embodiments, the hydrogen blending data at least includes the hydrogen blending ratio, such as the volume percentage of hydrogen in the gas.

[0072] In some embodiments, the government regulation management platform can query the preset heat value table based on the gas data to obtain the heat value of the gas in the preset gas pipeline before the hydrogen is injected.

[0073] The preset heat value table includes the correspondence between the reference gas data and the reference heat value. In some embodiments, the preset heat value table can be obtained based on experiments or theoretical calculations.

[0074] In some embodiments, in response to the gas in the preset gas pipeline, the calorific value before the hydrogen injection is not higher than the calorific value requirement in the terminal demand data, the government regulatory management platform can set the hydrogen mixing ratio to 0, that is, no hydrogen is injected into the preset gas pipeline.

[0075] In some embodiments, in response to the gas in the preset gas pipeline, the calorific value before the hydrogen injection is higher than the calorific value requirement in the terminal demand data, the government regulatory management platform can set the hydrogen mixing ratio to a preset value. The preset value can be set based on prior experience and / or actual demand.

[0076] In some embodiments, the government regulatory management platform can also determine the maximum hydrogen mixing ratio based on the terminal demand data, and determine the hydrogen mixing data based on the maximum hydrogen mixing ratio, the gas data and the terminal demand data. More detailed description can be referred to the related description in Figure 3 .

[0077] Step 230, determining the injection parameter based on the hydrogen mixing data, and sending the injection parameter to the hydrogen input device corresponding to the preset gas pipeline to control the pressure regulating unit of the hydrogen input device to inject hydrogen into the preset gas pipeline according to the injection parameter.

[0078] The injection parameter refers to the working parameter of the hydrogen input device when hydrogen is injected. In some embodiments, the injection parameter can include hydrogen pressure, hydrogen flow rate and other parameters corresponding to the hydrogen input device, and other parameters can be determined according to actual demand.

[0079] In some embodiments, the government regulatory management platform can query the reference input parameter table based on the hydrogen mixing data to determine the injection parameter.

[0080] The reference input parameter table includes the corresponding relationship between the reference hydrogen mixing data and the reference injection parameter, and the injection parameter reference table can be obtained based on experiments or theoretical calculations.

[0081] In some embodiments, the government regulatory management platform can also determine the original pressure mean value and the pressure fluctuation value of the gas in the preset gas pipeline based on the gas sequence data corresponding to the preset gas pipeline, and determine the injection parameter based on the original pressure mean value, the pressure fluctuation value and the hydrogen mixing data.

[0082] In some embodiments, the government regulatory management platform can send the injection parameter to the hydrogen input device to control the pressure regulating unit of the hydrogen input device to inject hydrogen into the preset gas pipeline according to the injection parameter.

[0083] In some embodiments of the present specification, the hydrogen mixing data is determined based on the terminal demand data, and the hydrogen pressure and hydrogen flow rate when hydrogen is mixed into the preset gas pipeline are reasonably determined according to the hydrogen mixing data, which can better ensure the safety of the hydrogen mixing process on the premise of meeting the calorific value requirement of the user.

[0084] Figure 3 is an example schematic diagram of determining hydrogen blending data according to some embodiments of the present specification. As shown in some embodiments, the government regulation management platform can determine a maximum hydrogen blending ratio corresponding to the preset gas pipeline based on historical data; and determine the hydrogen blending data based on the maximum hydrogen blending ratio, the gas data and the terminal demand data. Figure 3

[0085] The maximum hydrogen blending ratio 321 refers to the maximum value of the volume percentage of hydrogen in hydrogen-blended gas under the premise of ensuring normal use of gas.

[0086] In some embodiments, the government regulation management platform can determine the maximum hydrogen blending ratio 321 based on the historical data 311. For example, the government regulation management platform can determine at least one historical hydrogen blending ratio of the downstream users of the preset gas pipeline when the gas is normally used in the historical data, and take the maximum value of the historical hydrogen blending ratio as the maximum hydrogen blending ratio corresponding to the preset gas pipeline. Wherein, the normal use of gas refers to that the preset gas pipeline and its downstream pipelines do not need to be repaired, and the gas users corresponding to the preset gas pipeline have no complaints or negative comments within a period of time (for example, a week, a month, etc.).

[0087] In some embodiments, the government regulation management platform can determine at least one set of first reference data based on the data of the normal use of gas in the historical data, and the set of first reference data includes the historical maximum hydrogen blending ratio, the historical gas data, the historical terminal demand data and the corresponding historical hydrogen blending data; cluster the at least one set of first reference data to determine a plurality of cluster centers, construct a plurality of first reference vectors based on the historical maximum hydrogen blending ratio, the historical gas data and the historical terminal demand data corresponding to each of the plurality of cluster centers, and take the historical hydrogen blending data corresponding to the cluster center as the label of the first reference vector corresponding to the cluster center.

[0088] In some embodiments, the government regulation management platform can construct a first to-be-matched vector based on the maximum hydrogen blending ratio of the preset gas pipeline, the gas data and the terminal demand data; match the first to-be-matched vector with the plurality of first reference vectors respectively, and according to the calculation result of the similarity, take the label corresponding to the second reference vector with the highest similarity with the first to-be-matched vector as the hydrogen blending parameter corresponding to the preset gas pipeline. Wherein, the similarity can be determined based on the vector distance, and the vector distance can include but is not limited to the Euclidean distance, the cosine distance, etc.

[0089] In some embodiments, the government regulation management platform constructs a gas atlas 330 based on the maximum hydrogen blending ratio 321, the gas data 322 and the gas data 323 of the gas terminal within the preset time; and determines the hydrogen blending data 351 by the prediction model 340 based on the gas atlas 330.

[0090] ​The gas usage data 323 is data representing the gas usage of the gas usage terminal. For example, the gas usage data can include the gas usage amount, the gas usage purpose, or other data related to the gas usage. The gas usage purpose can include, but is not limited to, at least one of domestic gas usage, commercial gas usage, and industrial gas usage.

[0091] In some embodiments, the government regulation management platform can obtain the gas usage amount from the gas company management platform through the government regulation sensing network platform, and determine the gas usage purpose based on the type of the gas usage terminal. The smart gas equipment object platform collects the gas usage amount through the gas flow meter, and uploads the gas usage amount to the gas company management platform through the gas company sensing network platform.

[0092] The gas graph 330 is a graph used to represent the gas conditions in the plurality of gas pipelines in the gas pipeline network and the connection relationship between the pipelines. For example, the gas graph 330 can include nodes 331 and edges 332.

[0093] In some embodiments, the gas graph includes a plurality of nodes, and each node corresponds to a gas pipeline.

[0094] In some embodiments, the nodes in the gas graph have node features.

[0095] In some embodiments, when the node corresponds to a preset gas pipeline, the node feature corresponding to the node includes the maximum hydrogen blending ratio and the gas data of the preset gas pipeline; when the node corresponds to a gas usage terminal pipeline, the node feature corresponding to the node includes the gas usage data of the gas usage terminal; and when the node is a pipeline other than the preset gas pipeline or the gas usage terminal pipeline, the node feature corresponding to the node includes the gas data of the pipeline. The gas usage terminal pipeline refers to a gas pipeline connected with a gas usage terminal.

[0096] For details about the acquisition of the maximum hydrogen blending ratio, the gas data, and the gas usage data, refer to the relevant description above.

[0097] In some embodiments, the node feature corresponding to the node representing the preset gas pipeline in the gas graph can further include at least one of the original pressure mean value and the pressure fluctuation value.

[0098] The original pressure mean value refers to the mean value of the gas pressure in the gas pipeline within a period of time before hydrogen blending. The pressure fluctuation value reflects the fluctuation of the gas pressure in the gas pipeline within a period of time before hydrogen blending.

[0099] In some embodiments, the original pressure value and the pressure fluctuation value can be determined based on the gas sequence data. For more details, refer to Figure 4 and the related description.

[0100] Some embodiments of the present specification consider the average value and fluctuation value of the original pressure in the gas pipeline before hydrogen mixing when constructing the gas atlas, can pay attention to the dynamic characteristics of the pressure change in the gas pipeline, and are beneficial to more accurately determining the hydrogen mixing data based on the gas atlas.

[0101] In some embodiments, the node features corresponding to the nodes representing the preset gas pipeline or other gas pipelines in the gas atlas can further include environmental data of the positions where the nodes corresponding gas pipelines are located.

[0102] The environmental data is the environmental feature data representing the position where the gas pipeline is located. For example, the environmental data can include at least one of the temperature, humidity, atmospheric pressure, etc. of the position where the gas pipeline is located.

[0103] In some embodiments, the government regulation management platform obtains the environmental data from the gas company management platform through the government regulation sensing network platform. The environmental data in the gas company management platform can be obtained from the smart gas equipment object platform through the gas company sensing network platform. For example, the smart gas equipment object platform collects the temperature of the position where the gas pipeline is located through a temperature sensor, collects the humidity of the position where the gas pipeline is located through a humidity sensor, collects the atmospheric pressure of the position where the gas pipeline is located through a barometer, and uploads the environmental data to the gas company management platform through the gas company sensing network platform.

[0104] Some embodiments of the present specification consider the environmental data of the position where the gas pipeline is located when constructing the gas atlas, so that the constructed gas atlas contains more information affecting gas delivery, is more in line with the actual situation, and is beneficial to more accurately determining the hydrogen mixing data based on the gas atlas.

[0105] In some embodiments, the gas atlas further includes a plurality of edges connecting the nodes. The edge reflects the connection relationship between the gas pipelines corresponding to two nodes, and if two gas pipelines are connected, there is an edge between the nodes corresponding to the two gas pipelines. The edge of the gas atlas is a directed edge, and the direction of the edge is consistent with the flow direction of the gas.

[0106] In some embodiments, the edge in the gas atlas has edge features.

[0107] In some embodiments, the edge features corresponding to the edge in the gas atlas include the flow direction of the gas.

[0108] In some embodiments, the government regulation management platform can determine the hydrogen mixing parameter through the estimation model.

[0109] The estimation model can be a machine learning model. For example, the estimation model can be a graph neural network (GNN) model, or other trained machine learning models.

[0110] In some embodiments, the input of the estimation model comprises the gas profile, and the output comprises the hydrogen blending data corresponding to the output of the node of the preset gas pipeline.

[0111] In some embodiments, the output of the preset model can further comprise a calorific value range corresponding to the output of the node of the gas terminal pipeline. The calorific value range can represent the calorific value range corresponding to the hydrogen-blended gas in the terminal gas pipeline based on the aforementioned hydrogen blending data.

[0112] The calorific value range refers to the calorific value fluctuation range 352 corresponding to the combustion of hydrogen-blended gas. For example, the calorific value fluctuation range can include the maximum calorific value and the minimum calorific value corresponding to the hydrogen-blended gas.

[0113] In some embodiments, the government regulation management platform can determine whether the hydrogen blending data is available based on the calorific value range corresponding to the output of the node of the gas terminal pipeline. For example, the government regulation management platform can obtain at least one gas data corresponding to the user terminal at at least one preset time point; determine at least one calorific value range based on the at least one gas data by using the preset model; in response to the coverage of the terminal demand data corresponding to the calorific value being higher than a threshold value based on the at least one calorific value range, determine that the aforementioned hydrogen blending data is available; otherwise, determine that the aforementioned hydrogen blending data is not available, determine new hydrogen blending data according to the aforementioned method, and perform the next round of determination.

[0114] In some embodiments, the government regulation management platform trains the initial estimation model based on a plurality of sample data sets with sample labels by using gradient descent method or other feasible training methods for at least one iteration to obtain the estimation model. For example, the at least one iteration process can include: the government regulation management platform can input the plurality of sample data sets into the initial estimation model, construct a loss function based on the output of the initial estimation model and the sample labels, and update the parameters of the initial estimation model in reverse according to the value of the loss function; when the iteration end condition is triggered, the training ends, and a trained estimation model is obtained. The iteration end condition can include at least one of the convergence of the loss function and the number of iterations reaching a threshold value.

[0115] In some embodiments, the sample data set comprises a sample gas profile, which can be constructed based on historical data when the gas is normally used, with reference to the aforementioned method of constructing the gas profile.

[0116] In some embodiments, the sample label can include historical hydrogen blending data corresponding to the node of the preset gas pipeline in the sample gas profile. The historical hydrogen blending data can be determined based on the actual hydrogen blending data in the historical data when the gas is normally used.

[0117] In some embodiments, the sample label can further include a historical heat value range corresponding to the node of the gas terminal pipeline in the sample gas spectrum, wherein the historical heat value range refers to a heat value fluctuation range corresponding to the hydrogen-doped gas combustion in the historical data.

[0118] In some embodiments, the historical heat value fluctuation range can be determined in multiple ways.

[0119] When the hydrogen-doped gas in the gas terminal pipeline can be obtained, the government regulatory management platform can determine the historical heat value range based on the results of multiple sampling measurements. For example, the government regulatory management platform can sample the hydrogen-doped gas in the gas terminal pipeline multiple times in a preset historical period, measure the heat value of the sampled gas by actual combustion, and determine the historical heat value range based on the highest heat value and the lowest heat value obtained by multiple sampling measurements.

[0120] When the hydrogen-doped gas in the gas terminal pipeline cannot be obtained, the government regulatory management platform can obtain the historical gas consumption of the gas terminal in the first historical period and the second historical period from the gas company management platform through the government regulatory sensing network platform. The first historical period is the period before hydrogen doping, and the historical gas heat value at this time can be obtained by testing the gas collected at the gas gate station. The second historical period is the period after hydrogen doping, and the length of the second historical period is the same as that of the first historical period. For example, the length can be one week, ten days, etc., which can be determined based on actual conditions.

[0121] Theoretically, the heat demand of the gas terminal remains stable, and the heat demand of the gas terminal in the same length of time is the same. Therefore, the government regulatory management platform can solve the historical gas heat value after hydrogen doping based on the following formula:

[0122]

[0123] wherein, is the gas consumption in the first historical period; is the historical gas heat value before hydrogen doping; is the gas consumption in the first historical period; is the historical gas heat value after hydrogen doping to be solved.

[0124] In some embodiments, the government regulatory management platform can obtain the historical gas heat value after hydrogen doping corresponding to at least one historical period by the above-mentioned method, and determine the historical heat value range after hydrogen doping based on the maximum value and the minimum value thereof.

[0125] In some embodiments, the government regulatory management platform can split the sample data set according to a preset proportion to obtain a training set, a validation set, and a test set; and train the initial estimation model using the training set, the validation set, and the test set to obtain the estimation model.

[0126] The preset proportion refers to the proportion of the training set, the validation set, and the test set that is preset. For example, the proportion of the number of samples contained in the training set, the validation set, and the test set can be 8:1:1.

[0127] In some embodiments, the preset proportion can be preset by the government regulation management platform based on a default setting or prior experience.

[0128] In some embodiments, the government regulation management platform can split the sample data set based on the preset proportion to obtain the training set, the validation set, and the test set.

[0129] The splitting method can include sampling statistics, which can include but is not limited to random sampling, stratified sampling, etc. In some embodiments, the gas company management platform can also split the sample data set in other ways.

[0130] In some embodiments, the training set is a data set used to adjust the learning parameters of the model during the model training process. The learning parameters include parameters such as weights, biases, etc. The validation set is a data set used to adjust the model hyperparameters during the model training process. The hyperparameters include the number of network layers, the number of network nodes, the number of iterations, and the learning rate, etc. The test set is a data set used to evaluate the performance of the final model.

[0131] The training set, the validation set, and the test set obtained by splitting do not have data cross, i.e., there is no repeated data between any two of the training set, the validation set, and the test set.

[0132] In some embodiments, the government regulation management platform can train the initial estimation determination model based on the training set, the validation set, and the test set to obtain an estimation model. The training process includes multiple stages of training. In one stage of training, the training set is input into the initial estimation determination model, a loss function is constructed based on the sample label and the output of the initial estimation model, and the parameters of the initial estimation determination model are updated through multiple rounds of iterations based on the loss function. During the foregoing training process, the trained initial estimation model is verified based on the validation set based on a preset verification frequency, the initial learning rate or the learning rate in the initial estimation model training process after the round of training is adjusted based on the verification result, the performance of the obtained estimation model is evaluated by testing the obtained estimation model based on the test set when a preset condition is triggered, multiple stages of training are performed, and the estimation model with the best performance is taken as the trained estimation model. The learning rate can be adjusted using various strategies, such as one or more of learning rate decay strategy, learning rate warm-up, cyclic learning rate, and adaptive learning rate adjustment algorithm. The preset condition can include one or more of the number of iterations reaching a threshold, the loss function converging, and the value of the loss function being less than a preset threshold.

[0133] The above process of training the model using the training set, the validation set and the test set is only an example. Other processes known to those skilled in the art can also be used when training the model based on the training set, the validation set and the test set.

[0134] In some embodiments, the sample data set can include multiple groups of sample data, and each group of sample data can be divided into a training set, a validation set and a test set according to the aforementioned preset ratio. The government regulatory management platform can train the initial estimation model based on the multiple groups of divided sample data.

[0135] In some embodiments, the learning rate corresponding to each group of sample data is related to the sample confidence of the group of sample data. The higher the sample confidence, the greater the learning rate corresponding to the group of sample data.

[0136] In some embodiments, the sample confidence of a group of sample data can be determined based on the availability of sample labels. The availability of sample labels refers to whether the labels in the sample data can be obtained by sampling and measuring. For example, the sample confidence can be the number of user terminal pipeline corresponding nodes with availability, which accounts for a percentage of the total number of user terminal pipeline corresponding nodes. It can be understood that the sample labels that can be obtained by sampling and measuring have higher accuracy, and the greater the proportion of such labels in a group of sample data, the higher the sample confidence.

[0137] In some embodiments of the present specification, the estimation model is trained based on the training set, the test set and the validation set, which is beneficial to improve the robustness of the estimation model and prevent overfitting of the estimation model. For samples with high confidence, the learning rate is appropriately increased to enable the model to be fully learned, which is beneficial to accurate determination of the hydrogen doping data.

[0138] In some embodiments of the present specification, the hydrogen doping data is determined by the estimation model, which can accurately determine the hydrogen doping data by using the learning ability of the machine learning model, thereby better ensuring the safety and effectiveness of hydrogen injection.

[0139] In some embodiments of the present specification, the hydrogen doping data is determined based on the gas data and the terminal demand data, which can determine more reasonable hydrogen doping data under the premise of meeting the terminal demand data, so as to better ensure the safe transmission and normal use of gas.

[0140] Figure 4 is an exemplary schematic diagram of determining injection parameters according to some embodiments of the present specification.

[0141] As Figure 4In some embodiments, the government regulatory management platform can also determine the original pressure mean 420 and the pressure fluctuation value 430 of the gas in the preset gas pipeline based on the preset gas pipeline corresponding gas sequence data 410, and determine the injection parameter 460 based on the original pressure mean 420, the pressure fluctuation value 430 and the hydrogen blending data 351.

[0142] The gas sequence data 410 refers to a sequence composed of multiple gas data within a preset time period.

[0143] The preset time period can be a system default value or specified by the user. For example, the preset time period is the past 24 hours.

[0144] In some embodiments, the government regulatory management platform can obtain multiple gas pressures 411 of the preset gas pipeline at multiple time points within a preset time period based on historical data, and sort the multiple gas pressures 411 in chronological order to obtain the gas sequence data 410.

[0145] In some embodiments, the government regulatory management platform can calculate the original pressure mean and the pressure fluctuation value based on the multiple gas pressures in the gas sequence data. For example, the government regulatory management platform can determine the original pressure mean based on the average of the multiple gas pressures in the gas sequence data, and the government regulatory management platform can determine the pressure fluctuation value based on the ratio of the original pressure mean to the standard deviation of the multiple gas pressures in the gas sequence data.

[0146] In some embodiments, the government regulatory management platform can determine at least one set of second reference data based on data without faults in the historical data, and a set of second reference data includes historical pressure mean, historical pressure fluctuation value, historical hydrogen blending data and corresponding historical injection parameter; cluster at least one set of second reference data to determine multiple cluster centers, construct multiple second reference vectors based on the historical pressure mean, the historical pressure fluctuation value and the historical hydrogen blending data corresponding to each of the multiple cluster centers, and use the historical injection parameter corresponding to the cluster center as the label of the second reference vector corresponding to the cluster center.

[0147] In some embodiments, the government regulatory management platform can construct a second to-be-matched vector based on the original pressure mean, the pressure fluctuation value and the hydrogen blending data of the preset gas pipeline; match the second to-be-matched vector with the aforementioned multiple second reference vectors respectively, and determine the label corresponding to the second reference vector with the highest similarity to the second to-be-matched vector as the injection parameter of the preset gas pipeline according to the calculation result of the similarity. The similarity can be determined based on the vector distance, and the vector distance can include but is not limited to Euclidean distance, cosine distance, etc.

[0148] In some embodiments, the government regulatory management platform determines, based on the candidate injection parameter 470, the original pressure mean value 420, the pressure fluctuation value 430, and the hydrogen blending data 351, an injection effective value 450 corresponding to the candidate injection parameter by determining the determination model 440; and determines, based on the injection effective value 450, an injection parameter 460 from the candidate injection parameter.

[0149] The determination model 440 can be a machine learning model. For example, the determination model is a neural network (NN) model, a deep-learning neural network (DNN) model, or other trained machine learning model.

[0150] In some embodiments, the input of the determination model 440 includes the candidate injection parameter 470, the original pressure mean value 420, the pressure fluctuation value 430, and the hydrogen blending data 351, and the output is the injection effective value 450 corresponding to the candidate injection parameter.

[0151] The candidate injection parameter refers to a plurality of alternative injection parameters.

[0152] In some embodiments, the government regulatory management platform determines, as the candidate injection parameter, the N injection parameters with the highest usage frequency in the historical data. Wherein, N can be set based on prior experience and / or actual demand.

[0153] The injection effective value is a numerical value for measuring the effect of injecting hydrogen based on the candidate injection parameter. The greater the injection effective value, the better the effect of injecting hydrogen based on the candidate injection parameter, and the more the hydrogen-blended gas can meet the user's demand under the premise of ensuring safety.

[0154] In some embodiments, the government regulatory management platform can train the initial determination model by gradient descent method or other methods based on a plurality of training samples with training labels to obtain a trained determination model. The training process is similar to training the initial prediction model, and more detailed content can be referred to the related description in Figure 3 .

[0155] The training sample includes a sample injection parameter, a sample pressure mean value, a sample pressure fluctuation value, and sample hydrogen blending data. The training sample can be constructed based on the historical data when the gas is normally used. For more information about the normal use of gas, please refer to the related description in Figure 3 .

[0156] The training label is an actual injection effective value corresponding to the training sample.

[0157] In some embodiments, the government regulatory management platform can determine the actual injection effective value based on matching the measured data in the preset gas pipeline after the hydrogen injection with the standard data within a period of time (e.g., one week), and determining the actual injection effective value based on the matching result.

[0158] The measured data can include actual gas pressure, actual gas calorific value, and actual gas flow rate. The standard data can include a standard pressure interval, a standard calorific value interval, and a standard flow rate interval.

[0159] For example, the government regulatory management platform can determine the matching value of the actual gas pressure with the standard pressure interval, the actual gas calorific value with the standard calorific value interval, and the actual gas flow rate with the standard flow rate interval, respectively. If the value of the measured data is within the interval corresponding to the standard data, it is determined that the matching value is 1. If the value of the measured data is not within the interval corresponding to the standard data, the matching degree is determined based on the minimum distance between the value of the measured data and the end point of the interval of the standard data. The smaller the minimum distance, the higher the matching value.

[0160] The government regulatory management platform can weight and sum the matching values based on preset weights, and determine the result of the weighted sum as the actual injection effective value. The preset weights can be determined based on prior experience and / or actual demand.

[0161] In some embodiments, the input of the determination model further includes environmental data 480 of the location where the preset gas pipeline is located. For more information about the environmental data, see the related description in Figure 3 .

[0162] In some embodiments, when the input of the determination model includes environmental data, the training sample further includes sample environmental data, and the government regulatory management platform can obtain the sample environmental data based on historical data.

[0163] In some embodiments of the present specification, when determining the injection effective value, the influence of environmental data on hydrogen injection is considered, which can better predict under the premise of conforming to the actual environmental conditions, so that the obtained injection effective value is more accurate.

[0164] In some embodiments, the input of the determination model further includes a gas consumption data sequence 490 of the gas terminal in a preset period.

[0165] The gas consumption data sequence refers to a sequence composed of gas consumption data in a preset period. For more information about the gas consumption data, see the related description in Figure 3 .

[0166] In some embodiments, the government regulatory management platform can obtain multiple gas consumption data of the gas terminal in a preset period based on historical data, and sort the multiple gas consumption data in chronological order to obtain the gas consumption data sequence.

[0167] In some embodiments, when it is determined that the input of the model comprises the gas consumption data sequence, the training sample further comprises a sample gas consumption data sequence, and the government regulatory management platform can obtain the sample gas consumption data sequence based on historical data.

[0168] Some embodiments of the present specification consider the influence of uncertain factors of the gas consumption terminal on the injected hydrogen when determining the injection effective value, which can more effectively and safely regulate the pressure.

[0169] In some embodiments, the government regulatory management platform can determine the candidate injection parameter with the highest injection effective value 450 as the final injection parameter 460.

[0170] Some embodiments of the present specification determine the injection effective value of the candidate injection parameter by the determination model, and determine the injection parameter based on the injection effective value, which can obtain more accurate injection parameters, make the process of injecting hydrogen more safe, and better guarantee the combustion effect of the obtained hydrogen-doped gas.

[0171] Some embodiments of the present specification consider the influence of gas pressure and gas pressure fluctuation when determining the injection parameter, fully consider the change of pressure in the gas pipeline, and can better guarantee the safe transmission and use of gas.

[0172] Some embodiments of the present 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 smart gas hydrogen-doped gas conveying method as described above.

[0173] The above has described the basic concept, and it is obvious that the above detailed disclosure is only used as an example and does not limit the present specification. Although it is not explicitly stated here, those skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0174] Meanwhile, the present specification uses specific words to describe the embodiments of the present specification. As “one embodiment”, “an embodiment”, and / or “some embodiments” means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the “an embodiment” or “one embodiment” or “one alternative embodiment” mentioned in different positions in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0175] Furthermore, the order of the processing elements and sequences described in this specification are not intended to be construed as a limitation, unless specifically stated, but are included to provide a complete description of one or more embodiments of the present specification. Regardless of the particular sequence of processing elements and sequences, however, the description herein of a process should be understood to include any and all combinations of one or more elements, and sequences that can be perceived as either open-ended or specific.

[0176] Similarly, it is to be noticed that the term "comprising", used in the description, should not be interpreted as being restricted only to the elements or steps listed thereafter; it does not exclude other elements or steps. It is thus to be interpreted as specifying the presence of the stated elements or steps as well as the presence of yet unrecited elements or steps. Furthermore, the word "a" or "an" preceding an element or step of the description does not exclude the presence of a plurality of such elements or steps, that is, "a" or "an" means "one or more".

[0177] Some embodiments use numerical descriptors of components, quantities of attributes. It should be understood that such numerical descriptors used in the description of embodiments, in some examples, are modified by the adjectives "about", "approximately", or "substantially". Unless otherwise stated, "about", "approximately", or "substantially" indicates that the described numerical value is permitted to vary by ±20%. Accordingly, in some embodiments, numerical values used in the specification and claims are approximations that can vary depending on the desired characteristics of the individual embodiments. In some embodiments, numerical values should be considered in the context of the number of significant digits and errors inherent to some measuring techniques. Although the numerical ranges and parameters setting forth the broadest scope of some embodiments of the specification are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values set forth in the specific examples are provided to be as precise as reasonably possible. However, some variations may

[0178] Each patent, patent application, patent publication, and other material, articles, books, instructions, documents, that has been identified herein, is hereby incorporated herein by reference in its entirety for all purposes pursuant to 37 C.F.R. § 1.57. Except to the extent of any contradictory content or prior art document that is inconsistent with the content of the present specification, the content of the incorporated references is hereby superseded by the content of the present specification. In the event of a discrepancy between the content of the incorporated references and the content of the present specification, the content of the present specification shall prevail. It is specifically noted that, if the use of a term in the description, definitions, and / or terminology of the materials incorporated by reference herein is inconsistent with the use of the term in the present specification, the use of the term in the present specification shall prevail.

[0179] Finally, it should be understood that the embodiments described herein are only given by way of example and that other modifications can occur to persons skilled in the art. Therefore, the scope of the present description is not intended to be limited to the embodiments described herein but is only limited by the claims that follow.

Claims

1. A method for intelligent hydrogen-blended gas transportation, characterized in that, The method, executed by the gas company management platform of the smart gas hydrogen-blended gas transmission IoT system, includes: Obtain gas data of a preset gas pipeline, and terminal demand data of the gas-using terminals corresponding to the preset gas pipeline; Based on the gas data and the terminal demand data, the hydrogen blending data of the preset gas pipeline is determined; Based on the hydrogen blending data, injection parameters are determined and sent to the hydrogen input device corresponding to the preset gas pipeline to control the pressure regulating unit of the hydrogen input device to inject hydrogen into the preset gas pipeline according to the injection parameters. The gas data also includes gas pressure; the method further includes: Based on the gas sequence data corresponding to the preset gas pipeline, the original average pressure and pressure fluctuation value of the gas in the preset gas pipeline are determined; the gas sequence data is determined based on the gas pressure of the preset gas pipeline in a preset time period; The injection parameters are determined based on the original average pressure, the pressure fluctuation value, and the hydrogen doping data.

2. The method as described in claim 1, characterized in that, The step of determining the hydrogen blending data for the preset gas pipeline based on the gas data and the terminal demand data includes: Based on historical data, the maximum hydrogen blending ratio corresponding to the preset gas pipeline is determined; The hydrogen blending data is determined based on the maximum hydrogen blending ratio, the gas data, and the terminal demand data.

3. The method as described in claim 2, characterized in that, The method further includes: Based on the maximum hydrogen doping ratio, the gas data, and the gas consumption data of the gas terminal within a preset time, a gas spectrum is constructed. Based on the gas spectrum, the hydrogen doping data and calorific value fluctuation range are determined by a prediction model, which is a machine learning model.

4. The method as described in claim 1, characterized in that, The determination of the injection parameters based on the original average pressure, the pressure fluctuation value, and the hydrogen doping data includes: Based on the candidate injection parameters, the original average pressure, the pressure fluctuation value, and the hydrogen doping data, the effective injection value corresponding to the candidate injection parameters is determined by a determination model, wherein the determination model is a machine learning model; Based on the effective value of the injection, the injection parameter is determined from the candidate injection parameters.

5. A smart gas-hydrogen blending gas transmission IoT system, characterized in that, This includes user platforms, government regulatory service platforms, government regulatory management platforms, government regulatory sensor network platforms, government regulatory object platforms, gas company sensor network platforms, and smart gas equipment object platforms. The government-regulated platform includes the gas company management platform; The intelligent gas equipment platform includes a hydrogen input device and a gas monitoring device, wherein the hydrogen input device is installed in a preset gas pipeline; The hydrogen input device includes a hydrogen storage unit, a hydrogen buffer unit, a hydrogen pressure regulating unit, and a delivery pipeline; the hydrogen storage unit, the hydrogen buffer unit, and the hydrogen pressure regulating unit are connected through the delivery pipeline; the hydrogen storage unit stores hydrogen from the input pipeline network, the hydrogen buffer unit is configured to buffer the hydrogen from the input pipeline network, and the hydrogen pressure regulating unit is configured to adjust the output hydrogen pressure based on the injection parameters; The gas monitoring device is installed in the gas pipeline of the gas pipeline network and is used to acquire gas data of the gas pipeline. The gas company management platform is configured to execute the intelligent gas hydrogen-blended gas delivery method as described in claim 1, to control the pressure regulating unit of the hydrogen input device to inject hydrogen into the preset gas pipeline according to the injection parameters; The gas data also includes gas pressure; the gas company management platform is further configured as follows: Based on the gas sequence data corresponding to the preset gas pipeline, the original average pressure and pressure fluctuation value of the gas in the preset gas pipeline are determined; the gas sequence data is determined based on the gas pressure of the preset gas pipeline in a preset time period; The injection parameters are determined based on the original average pressure, the pressure fluctuation value, and the hydrogen doping data.

6. The Internet of Things system as described in claim 5, characterized in that, The gas company management platform is further configured as follows: Based on historical data, the maximum hydrogen blending ratio corresponding to the preset gas pipeline is determined; Based on the maximum hydrogen blending ratio, the gas data, and the terminal demand data, the hydrogen blending data is determined.

7. The Internet of Things system as described in claim 6, characterized in that, The gas company management platform is further configured as follows: Based on the maximum hydrogen doping ratio, the gas data, and the gas consumption data of the gas terminal within a preset time, a gas spectrum is constructed. Based on the gas spectrum, the hydrogen doping data and calorific value fluctuation range are determined by a prediction model, which is a machine learning model.

8. The Internet of Things system as described in claim 5, characterized in that, The gas company management platform is further configured as follows: Based on the candidate injection parameters, the original average pressure, the pressure fluctuation value, and the hydrogen doping data, the effective injection value corresponding to the candidate injection parameters is determined by a determination model, wherein the determination model is a machine learning model; Based on the effective value of the injection, the injection parameter is determined from the candidate injection parameters.

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