Intelligent gas pipe network gas quality monitoring system and method based on internet of things

Abstract

The application provides a smart gas pipe network gas quality monitoring system and method based on the Internet of Things, and relates to the field of gas quality monitoring.The system comprises a government gas supervision management platform, a government gas supervision sensing network platform, a government gas supervision object platform, a gas company sensing network platform, a device object platform, a gas user platform and a gas company service platform which are communicatively connected.The government gas supervision management platform is configured to: determine a monitoring sampling area based on first gas data;determine a monitoring frequency of a gas monitoring device in the monitoring sampling area based on a steady-state value of the monitoring sampling area;determine terminal gas quality based on second gas data and gas input information;determine gas quality demand based on terminal user characteristics and gas adjustment data;and determine to update a mixing parameter in response to the terminal gas quality not meeting the gas quality demand.The application can achieve accurate monitoring of gas quality and dynamic adjustment of gas mixing ratio.

Description

Technical Field

[0001] The present invention relates to the field of gas quality monitoring, and in particular to a gas quality monitoring system and method for a smart gas pipeline network based on the Internet of Things. Background Art

[0002] In gas supply systems, gas quality monitoring typically relies on manual methods, which are inefficient, costly in terms of both manpower and material resources, and fail to meet the stringent requirements of modern urban management for gas safety and accuracy. With the development of the Internet of Things (IoT), intelligent monitoring and management of gas quality are becoming possible.

[0003] Therefore, it is necessary to provide a smart gas pipeline network gas quality monitoring system and method based on the Internet of Things. Through data interaction between Internet of Things platforms, accurate monitoring of gas quality and dynamic adjustment of gas mixing ratio can be achieved to ensure gas use for gas users. Summary of the Invention

[0004] One or more embodiments of the present invention provide a gas quality monitoring system for a smart gas pipeline network based on the Internet of Things, wherein the system includes a government gas supervision and management platform, a government gas supervision sensor network platform, a government gas supervision object platform, a gas company sensor network platform, a device object platform, a gas user platform, and a gas company service platform that are communicatively connected. The government gas supervision object platform includes a gas company management platform; the government gas supervision and management platform is configured to: obtain first gas data through the device object platform, determine a monitoring sampling area based on the first gas data; determine a monitoring frequency of a gas monitoring device within the monitoring sampling area based on a steady-state value of the monitoring sampling area; and The system obtains second gas data and gas input information through the device object platform, and determines the terminal gas quality based on the second gas data and the gas input information; obtains terminal user characteristics from the gas user platform through the gas company service platform; obtains gas regulation data through the device object platform, and determines the gas quality requirement based on the terminal user characteristics and the gas regulation data; determines an updated mixing parameter in response to the terminal gas quality not meeting the gas quality requirement; and generates an updated mixing instruction based on the updated mixing parameter, and sends the updated mixing instruction to the gas company management platform, so that the gas company management platform updates the mixing parameters of the gas mixing device.

[0005] One or more embodiments of the present invention provide a gas quality monitoring method for a smart gas pipeline network based on the Internet of Things. The method is executed by a government gas supervision and management platform in a gas quality monitoring system for a smart gas pipeline network based on the Internet of Things. The method includes: obtaining first gas data through a device object platform, and determining a monitoring sampling area based on the first gas data; determining a monitoring frequency of a gas monitoring device within the monitoring sampling area based on a steady-state value of the monitoring sampling area; obtaining second gas data and gas input information through the device object platform, and determining terminal gas quality based on the second gas data and the gas input information; obtaining terminal user characteristics from a gas user platform through a gas company service platform; obtaining gas regulation data through the device object platform, and determining gas quality requirements based on the terminal user characteristics and the gas regulation data; determining updated mixing parameters in response to the terminal gas quality not meeting the gas quality requirements; and generating an updated mixing instruction based on the updated mixing parameter, and issuing the updated mixing instruction to the gas company management platform, so that the gas company management platform updates the mixing parameters of the gas mixing device.

[0006] One or more embodiments of the present invention provide a computer-readable storage medium, which stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a gas quality monitoring method for a smart gas pipeline network based on the Internet of Things.

[0007] The beneficial effects of the present invention include but are not limited to: (1) The gas quality monitoring system of the smart gas pipeline network based on the Internet of Things can form an information operation closed loop between various functional platforms, coordinate and operate regularly, and realize the informationization and intelligence of the gas quality monitoring of the smart gas pipeline network. (2) By estimating the terminal gas quality and judging whether the terminal gas quality meets the gas quality requirements, it can quickly determine whether the mixing parameters of the gas mixing equipment need to be updated without involving gas pressure regulation, thereby realizing accurate monitoring of gas quality and dynamic adjustment of gas mixing ratio, while timely meeting the gas quality requirements of gas users and ensuring gas use by gas users. (3) Based on the actual use of gas and the basic calorific value obtained by theoretical calculation, the output calorific value is determined, and the terminal gas quality is evaluated based on the output calorific value, so that the terminal gas quality obtained is more in line with the actual situation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0009] Figure 1This is a schematic diagram of the platform structure of a gas quality monitoring system for a smart gas network based on the Internet of Things according to some embodiments of this specification;

[0010] Figure 2 This is an exemplary flow chart of a method for monitoring gas quality in an IoT-based smart gas network according to some embodiments of this specification;

[0011] Figure 3 is an exemplary flow chart for determining terminal gas quality according to some embodiments of this specification;

[0012] Figure 4 is an exemplary schematic diagram of an estimation model according to some embodiments of this specification. DETAILED DESCRIPTION

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. The drawings do not represent all implementation methods.

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

[0015] When operations are described in steps in the embodiments of the present invention, unless otherwise specified, the order of the steps is interchangeable, steps may be omitted, and other steps may be included in the operation process.

[0016] Figure 1 This is a schematic diagram of the platform structure of a smart gas pipeline network gas quality monitoring system based on the Internet of Things according to some embodiments of this specification.

[0017] like Figure 1 As shown, the smart gas pipeline gas quality monitoring system 100 based on the Internet of Things includes a government gas supervision management platform 110, a government gas supervision sensor network platform 120, a government gas supervision object platform 130, a gas company sensor network platform 140, an equipment object platform 150, a gas user platform 160 and a gas company service platform 170 that are communicatively connected.

[0018] The government gas regulatory management platform is a comprehensive management platform for government-managed gas quality-related information. In some embodiments, the government gas regulatory management platform is configured as a server or processor. The government gas regulatory management platform also includes a government regulatory comprehensive database 111. The government regulatory comprehensive database is configured to store information or data related to the gas pipeline network, such as pipeline information and pipeline cleaning cycles.

[0019] The government gas regulatory sensor network platform is a platform for comprehensively managing government sensor information. In some embodiments, the government gas regulatory sensor network platform is configured as a communication network or gateway. The government gas regulatory sensor network platform can interact with the government gas regulatory management platform and the government gas regulatory object platform.

[0020] The government gas regulatory platform is a platform for generating government regulatory information and controlling its execution. In some embodiments, the government gas regulatory platform includes a gas company management platform 131. The gas company management platform is a comprehensive management platform for gas company information. The gas company management platform is configured as a server or processor.

[0021] The gas company sensor network platform is a platform for comprehensively managing gas company sensor information. In some embodiments, the gas company sensor network platform is configured as a communication network or gateway. The gas company sensor network platform can interact with the gas company management platform and the device object platform.

[0022] The device object platform refers to the functional platform that generates sensing information and controls the execution of information. In some embodiments, the device object platform includes gas monitoring equipment, gas mixing equipment, odorizing equipment, hydrogen blending equipment, and household gas equipment. Gas monitoring equipment includes temperature sensors, pressure sensors, and flow meters. Gas mixing equipment is installed at pipeline branches to mix gas transported by upstream pipelines and deliver the mixed gas to users or downstream pipelines. Gas monitoring equipment, odorizing equipment, and hydrogen blending equipment can be installed at any feasible location, such as the gas mixing equipment.

[0023] In some embodiments, the household gas equipment includes a leak monitoring device, a gas meter or a gas valve, an image sensor, a carbon monoxide sensor, a carbon dioxide sensor, and the like.

[0024] The gas company service platform refers to a platform on which the gas company provides gas services to gas users. In some embodiments, the gas company service platform is configured as a server or a processor.

[0025] The gas user platform refers to a platform for interacting with gas users. In some embodiments, the gas user platform includes a terminal device. The terminal device includes a mobile device, a tablet computer, etc.

[0026] In some embodiments, the IoT-based smart gas network gas quality monitoring system 100 may further include a processor. The processor is configured to process information and / or data related to the IoT-based smart gas network gas quality monitoring system 100. The processor may include a central processing unit (CPU), an application-specific instruction processor (ASIP), a graphics processing unit (GPU), a digital signal processor (DSP), or any combination thereof.

[0027] For detailed description of the above, please refer to Figures 2 to 4 Related description.

[0028] The gas quality monitoring system of the smart gas pipeline network based on the Internet of Things can form an information operation closed loop between various functional platforms, operate in a coordinated and regular manner, and realize the informatization and intelligence of the gas quality monitoring of the smart gas pipeline network.

[0029] Figure 2 This is an exemplary flow chart of a method for monitoring gas quality of a smart gas network based on the Internet of Things according to some embodiments of this specification. In some embodiments, the process 200 of the method for monitoring gas quality of a smart gas network based on the Internet of Things can be executed by a government gas supervision and management platform (hereinafter referred to as the supervision and management platform) of a gas quality monitoring system of a smart gas network based on the Internet of Things. Figure 2 As shown, the process 200 of the gas quality monitoring method of the smart gas pipeline network based on the Internet of Things includes the following steps.

[0030] For a description of the IoT-based smart gas network gas quality monitoring system and its various platforms and devices, see Figure 1 and its related descriptions.

[0031] Step 210: Acquire first gas data through the device object platform, and determine a monitoring sampling area based on the first gas data.

[0032] The first gas data refers to the global gas data corresponding to the gas pipeline network obtained by the device object platform based on a low sampling rate. The global gas data corresponding to the gas pipeline network refers to the gas data at each location in the gas pipeline network.

[0033] Gas data refers to data related to the gas in the pipeline. In some embodiments, the gas data includes at least one of gas temperature, gas pressure, and gas flow. The gas data is obtained through gas monitoring equipment.

[0034] In some embodiments, the device object platform acquires gas data collected by gas monitoring devices at various locations in the gas pipeline network at a low sampling rate to obtain first gas data. The first gas data is then uploaded to the regulatory management platform via the gas company sensor network platform, the government gas regulatory object platform, and the government gas regulatory sensor network platform. The sampling rate refers to the frequency with which the device object platform acquires gas data from the gas monitoring devices. A low sampling rate refers to a sampling rate below a first sampling threshold. A high sampling rate refers to a sampling rate above a second sampling threshold. The first and second sampling thresholds are preset based on experience, with the second sampling threshold being higher than the first sampling threshold.

[0035] The monitoring sampling area refers to the area of ​​the gas pipeline network where the monitoring frequency of the gas monitoring equipment needs to be determined or adjusted. The monitoring frequency refers to the frequency at which the gas monitoring equipment collects gas data.

[0036] In some embodiments, the supervision and management platform clusters all gas monitoring devices based on the gas data, location, and connection relationship of each gas monitoring device to obtain multiple clusters. In response to the gas pipeline network area corresponding to a single cluster meeting the preset conditions, the supervision and management platform determines the gas pipeline network area corresponding to the single cluster as a monitoring sampling area. The gas pipeline network area refers to the portion of the gas pipeline network surrounded by multiple gas monitoring devices. The preset conditions include the length of the gas pipeline within the gas pipeline network area being greater than a length threshold. The length threshold is set based on experience. The supervision and management platform obtains the location and connection relationship of the gas monitoring equipment from the gas company management platform through the government gas supervision sensor network platform.

[0037] In some embodiments, after determining multiple monitoring sampling areas, the supervision management platform may execute steps 220 to 270 for each monitoring sampling area to determine an update hybrid instruction corresponding to each monitoring sampling area.

[0038] Step 220: Determine the monitoring frequency of the gas monitoring equipment in the monitoring sampling area based on the steady-state value of the monitoring sampling area.

[0039] The steady-state value is used to reflect the consistency of gas data collected by multiple gas monitoring devices within the monitoring sampling area.

[0040] In some embodiments, the monitoring and management platform determines the steady-state value of the monitoring and sampling area based on the first gas data using various methods. For example, the monitoring and management platform calculates the comprehensive difference value of the first gas data corresponding to multiple gas monitoring devices within the monitoring and sampling area and determines the steady-state value based on the comprehensive difference value. The smaller the comprehensive difference value, the larger the steady-state value. The comprehensive difference value is represented by the mean of the difference values ​​corresponding to each type of the multiple first gas data. The difference value can be represented by variance, etc.

[0041] In some embodiments, the monitoring management platform determines a monitoring frequency based on the steady-state value, wherein the monitoring frequency is negatively correlated to the steady-state value.

[0042] Step 230: Acquire the second gas data and the gas input information through the device object platform, and determine the terminal gas quality based on the second gas data and the gas input information.

[0043] The second gas data refers to the gas data corresponding to the monitoring sampling area obtained by the device object platform based on the high sampling rate after the monitoring frequency is determined. The gas data corresponding to the monitoring sampling area refers to the gas data collected by multiple gas monitoring devices in the monitoring sampling area.

[0044] In some embodiments, the supervisory management platform obtains the second gas data in a manner similar to the manner in which the supervisory management platform obtains the first gas data.

[0045] Gas input information refers to information related to gas input from the gas pipeline network. In some embodiments, this information includes the gas component content and gas flow rate of gas input from multiple gas sources (e.g., gas stations). The gas component content is expressed as the volume percentage of each gas component (e.g., methane, ethane, etc.).

[0046] In some embodiments, the supervision management platform obtains gas input information from the gas company management platform through the government gas supervision sensor network platform.

[0047] End-user gas quality refers to data that characterizes the quality of gas delivered to end users. End users are defined as gas users within the monitoring sampling area. End-user gas quality is expressed as a numerical value or grade. Higher values ​​or grades indicate higher end-user gas quality.

[0048] In some embodiments, the monitoring and management platform determines the terminal gas quality through various methods. For example, the monitoring and management platform constructs a gas transmission map based on the second gas data, gas input information, and the pipe network connection status within the monitoring sampling area, and determines the terminal gas quality based on the gas transmission map. The monitoring and management platform obtains the pipe network connection status through the gas company management platform.

[0049] The gas transmission graph is a graph structure used to reflect the gas transmission situation in the monitoring sampling area. The gas transmission graph consists of multiple nodes and edges.

[0050] Edges in the gas transmission graph reflect the connections between nodes. If two nodes are connected by a gas pipeline, there is an edge between them, and the direction of the edge is the direction of gas transmission. The upstream node in the gas transmission graph represents the gas source, the downstream node represents the end user, and the intermediate node between the upstream and downstream nodes represents the location of the gas mixing equipment.

[0051] In some embodiments, node characteristics include the gas component content and gas flow rate corresponding to the node. The node characteristics of the upstream node are determined based on gas input information. The gas flow rate corresponding to the intermediate node is obtained based on the second gas data. The gas flow rate of the downstream node is obtained via the gas inlet device.

[0052] In a gas transmission map, the gas at each node, except the most upstream node, is obtained by mixing the gas from the corresponding upstream node. In some embodiments, the supervisory management platform uses the nodes in the gas transmission map, except the most upstream node, as target nodes and calculates the mixing information and gas component content of each target node based on the gas flow rate and gas component content of the upstream node corresponding to each target node.

[0053] Mixing information refers to information related to gas mixing. In some embodiments, the mixing information includes a mixing source and a mixing ratio. The mixing source of a target node includes the target node's corresponding upstream node. The mixing ratio refers to the ratio of gas delivered to the target node by the target node's corresponding upstream node.

[0054] For example, the upstream nodes of target node A are nodes B and C, and the gas flow rates of nodes B and C are 10m³ / s and 15m³ / s respectively. Then the mixing information of target node A can be expressed as (B: 40%, C: 60%). If the gas component content of node B is (methane: 90%, ethane 5%), and the gas component content of node C is (methane: 85%, ethane 15%), then the methane content in target node A is Similarly, the ethane content in the target node A is , that is, the gas component content of the target node A is (methane: 87%, ethane 13%).

[0055] In some embodiments, the supervision and management platform determines the gas component content corresponding to each target node in sequence from upstream to downstream based on the above steps, and finally obtains the gas component content of the most downstream node.

[0056] In some embodiments, the supervision and management platform determines the gas component content at the most downstream node based on the gas transmission map. Based on the gas component content at the most downstream node and the calorific value of methane in the gas components, the supervision and management platform multiplies the methane content in the gas component content by the calorific value of methane as the estimated gas calorific value, and determines the terminal gas quality corresponding to the end user based on the estimated gas calorific value. The higher the estimated gas calorific value, the higher the terminal gas quality. The calorific value of gas refers to the heat released by the complete combustion of a unit volume of gas. Since the main component of gas is methane, the calorific value of gas can be expressed as the heat released by the complete combustion of a unit volume of methane. The calorific value of methane is obtained based on manual input or a third-party platform.

[0057] In some embodiments, the supervisory management platform determines the terminal gas quality based on the output calorific value. For more information, see Figure 3 And related instructions.

[0058] Step 240: Obtain end-user characteristics from the gas user platform through the gas company service platform.

[0059] End-user characteristics refer to data related to the end-users corresponding to the monitoring sampling area. In some embodiments, end-user characteristics include at least one of the following: end-user type, historical complaint information, and gas usage scale. End-user types include residential and industrial users. In some embodiments, when the end-user is an industrial user, end-user characteristics also include factory type.

[0060] In some embodiments, the end-user characteristics are collected by the gas company service platform from the gas user platform, and uploaded to the supervision management platform through the gas company management platform and the interaction of multiple platforms.

[0061] Step 250: Obtain gas regulation data through the device object platform, and determine gas quality requirements based on the end user characteristics and the gas regulation data.

[0062] Gas regulation data refers to data related to the regulation of gas by the end user. In some embodiments, the gas regulation data includes the frequency of gas flow regulation by the end user using the gas access device.

[0063] In some embodiments, the gas regulation data is stored in the device object platform, and the supervision management platform obtains the gas regulation data from the device object platform through multi-platform interaction.

[0064] The gas quality requirement refers to the end user's requirement for gas quality. In some embodiments, the gas quality requirement is represented by a reference gas calorific value. The higher the reference gas calorific value, the higher the gas quality requirement.

[0065] In some embodiments, the supervision and management platform determines gas quality requirements through various methods. For example, based on end-user characteristics, the supervision and management platform may query a first preset table for a reference gas calorific value corresponding to the end-user characteristics and adjust the reference gas calorific value upward based on gas regulation data. The magnitude of the upward adjustment is positively correlated with the frequency with which the end-user adjusts the gas flow.

[0066] In some embodiments, the first preset table is pre-set based on historical data and includes multiple end-user characteristics and reference gas calorific values ​​corresponding to different end-user characteristics. The supervision and management platform can calculate the gas calorific value of the gas actually used by the end-user at multiple historical time points and use the average of the multiple gas calorific values ​​as the reference gas calorific value. The multiple historical time points are pre-set based on experience.

[0067] In some embodiments, the supervision and management platform determines the gas usage demand through an estimation model, and determines the gas quality requirement based on the gas usage demand. For more information, see Figure 4 And related instructions.

[0068] Step 260 : In response to the terminal gas quality not meeting the gas quality requirement, determining to update the mixing parameters.

[0069] The terminal gas quality does not meet the gas quality requirements, including the estimated gas calorific value being lower than the reference gas calorific value.

[0070] Updated mixing parameters refer to newly determined mixing parameters. Mixing parameters refer to operating parameters of the gas mixing device within the monitored sampling area. In some embodiments, the mixing parameters include an updated mixing ratio. The updated mixing ratio refers to the newly determined mixing ratio. The mixing ratio refers to the ratio of gases from different sources within the gas. The updated mixing ratio is represented by the valve openings corresponding to the gas mixing device and different upstream pipelines. The greater the valve opening, the more gas the gas mixing device receives from the corresponding upstream pipeline.

[0071] In some embodiments, in response to the terminal gas quality not meeting gas quality requirements, the supervisory management platform can determine and update mixing parameters based on the gas transmission map. For example, the supervisory management platform can obtain node characteristics of one or more upstream nodes of the most downstream node in the gas transmission map, adjust the gas flow rates of the multiple upstream nodes based on the gas component content in the multiple node characteristics, and adjust the valve opening of the gas mixing device based on the adjusted gas flow rates. In this manner, the supervisory management platform sequentially adjusts the gas flow rates of the upstream nodes and the valve opening of the gas mixing device. When adjusting to the most upstream node, the supervisory management platform can directly adjust the gas input rate of the most upstream node (the gas source). Specifically, the supervisory management platform can increase the gas flow rate of upstream nodes with higher calorific value (e.g., higher methane content) and increase the valve opening of the pipeline between the gas mixing device and the upstream node to increase the proportion of gas with higher calorific value in the mixture, thereby improving the terminal gas quality.

[0072] In some embodiments, the supervisory management platform may also obtain gas usage information through the device object platform; determine user priority based on the gas usage information and end-user characteristics; and determine to update mixing parameters based on the user priority.

[0073] Gas usage information refers to information related to end-user gas usage. In some embodiments, gas usage information includes gas usage volume, image information during gas usage, and concentration information. Image information refers to images of burning gas flames. Concentration information refers to carbon monoxide and carbon dioxide concentration sequences during gas usage.

[0074] In some embodiments, the supervision management platform obtains gas usage, image information, and concentration information through the gas meter, image sensor, carbon monoxide sensor, and carbon dioxide sensor of the device object platform.

[0075] The user priority reflects the importance of the end user. In some embodiments, the supervision management platform obtains the initial priority of the end user through manual input or other methods, wherein the initial priority of the industrial user is greater than the initial priority of the residential user.

[0076] In some embodiments, the supervision and management platform adjusts the initial priority based on gas usage information to determine the user priority. The greater the gas usage and the smaller the fluctuation in gas usage, the higher the user priority. The fluctuation in gas usage is represented by the variance of gas usage at multiple historical time points.

[0077] In some embodiments, the supervision management platform determines the updated mixing parameters of the gas mixing equipment upstream of the terminal users with high user priority based on the user priority through the above method to meet the gas quality requirements of the terminal users with high user priority.

[0078] By determining user priorities, the gas quality requirements of high-priority end users can be met first, thereby ensuring the gas use of industrial users or users with large gas consumption.

[0079] Step 270: Generate an updated mixing instruction based on the updated mixing parameters, and send the updated mixing instruction to the gas company management platform, so that the gas company management platform updates the mixing parameters of the gas mixing device.

[0080] An update mixing instruction is an instruction for updating the mixing parameters of the gas mixing equipment within the monitoring and sampling area. In some embodiments, the supervisory management platform generates a machine instruction based on the updated mixing parameters. The updated mixing instruction generated based on the machine instruction is then sent to the gas company management platform, which then updates the mixing parameters of the gas mixing equipment within the monitoring and sampling area.

[0081] By collecting data at a high sampling rate within a defined monitoring sampling area, redundant data acquisition can be reduced. By estimating terminal gas quality and determining whether it meets gas quality requirements, it is possible to quickly determine whether the mixing parameters of the gas mixing equipment need to be updated without involving gas pressure regulation. This enables precise monitoring of gas quality and dynamic adjustment of the gas mixing ratio, ensuring gas users' gas quality needs are met promptly while ensuring their gas availability.

[0082] Figure 3 FIG. 1 is an exemplary flow chart for determining the terminal gas quality according to some embodiments of this specification. Figure 3 As shown, the process 300 for determining the terminal gas quality includes the following steps.

[0083] Step 310: Obtain original odorization information and hydrogen doping information through the device object platform.

[0084] The original odorization information refers to information related to the odor added to the fuel gas by the odorization device. In some embodiments, the original odorization information includes the type and amount of odorized gas added by each odorization device.

[0085] The hydrogen doping information refers to information related to the addition of hydrogen into the fuel gas by the hydrogen doping equipment. In some embodiments, the hydrogen doping information includes the amount of hydrogen doped by each hydrogen doping equipment.

[0086] In some embodiments, the supervision management platform obtains original odorization information and hydrogen doping information through the hydrogen doping equipment and odorization equipment of the device object platform.

[0087] Step 320: Obtain gas usage information through the device object platform.

[0088] For information on gas usage, see Figure 2 And related instructions.

[0089] Step 330 : Determine the terminal gas information and basic calorific value based on the original odorization information, hydrogen admixture information, and original gas information.

[0090] The raw gas information is information related to the gas received by the end user when odor and hydrogen are not added to the gas. In some embodiments, the raw gas information includes the gas component content of the gas received by the end user. The raw gas information is obtained through the gas transmission map. For an explanation of the gas transmission map, see Figure 2 and its related descriptions.

[0091] The terminal gas information refers to information related to the gas received by the terminal user after odor and hydrogen are added to the gas. In some embodiments, the terminal gas information includes the gas component content of the gas received by the terminal user after odor and hydrogen are added to the gas.

[0092] In some embodiments, the supervision and management platform adds the original odorization information and hydrogen doping information as node features of the gas mixing equipment nodes corresponding to odorization and hydrogen doping to the gas transmission map, and recalculates the gas component content of each target node based on the original odorization information and hydrogen doping information through the method of determining the gas component content of the target node in step 230, and uses the gas component content of the most downstream node as the terminal gas information.

[0093] Basic calorific value refers to the estimated calorific value of the gas after odorization and hydrogen addition.

[0094] In some embodiments, the monitoring and management platform can perform a weighted average of the calorific values ​​of all components contained in the gas to obtain a basic calorific value. The calorific value of each component is obtained through manual input or other methods. The weight of each component is positively correlated with the content of the component.

[0095] In some embodiments, the supervision and management platform obtains gas leakage information within the monitoring sampling area, determines terminal odorization information based on the original odorization information and gas leakage information, and determines terminal gas information and basic calorific value based on the terminal odorization information, hydrogen doping information and original gas information.

[0096] Gas leak information refers to information related to gas leaks at end users. In some embodiments, this information includes average gas leakage volume, average gas leakage duration, and other information. The monitoring and management platform obtains gas leak information through leak monitoring equipment on the gas user platform. Average gas leakage duration refers to the average duration of multiple gas leaks.

[0097] The terminal odorization information refers to the odorization information corresponding to the gas actually received by the terminal user.

[0098] In some embodiments, the supervision and management platform constructs a first target vector based on the original odorization information and the gas leak information. The first target vector is matched against a first reference vector that meets a first matching condition in a first vector library, and the tag corresponding to the first reference vector is determined as the terminal odorization information. The first matching condition includes having the highest vector similarity with the first target vector. Vector similarity is negatively correlated with vector distance, which includes, for example, Euclidean distance.

[0099] In some embodiments, the first vector database is constructed based on historical data or experimental data. For example, the supervision and management platform constructs a first reference vector based on historical original odorization information and historical gas leakage information in the historical data or experimental data, and determines the historical terminal odorization information as a label corresponding to the first reference vector.

[0100] In some embodiments, the supervision and management platform may replace the original odorization information in the above method for determining the terminal gas information and basic calorific value with the terminal odorization information, and recalculate the terminal gas information and basic calorific value.

[0101] In some embodiments, the terminal odorization information is related to regional environment information.

[0102] Regional environment information refers to information related to the environment that the end user is in. In some embodiments, the supervision management platform can obtain regional environment information through manual input or a third-party platform, and incorporate the regional environment information into the construction of the first target vector.

[0103] Under different environmental conditions, the movement speed and diffusion rate of the gas will be different. Adding regional environmental information to the first reference vector will help to more accurately evaluate the odorization effect and thus more accurately determine the terminal odorization information.

[0104] Effective odorization can make users more aware of gas leaks and take action accordingly. Determining terminal odorization information based on gas leakage information at the end user can make the terminal odorization information more reasonable, which is conducive to making the obtained terminal gas information closer to the actual situation, and thus more accurately determining the terminal gas information and basic calorific value.

[0105] Step 340: Determine the output calorific value based on the second gas data, the gas usage information, the terminal gas information, and the basic calorific value.

[0106] For information about the second gas data and gas usage information, see Figure 2 And related instructions.

[0107] The output calorific value refers to the calorific value of the gas after odorization and hydrogen addition actually received by the end user.

[0108] In some embodiments, the supervision and management platform determines the output calorific value in a variety of ways. For example, the supervision and management platform determines the output calorific value using a calorific value estimation model based on the second gas data, gas usage information, terminal gas information, and basic calorific value.

[0109] The calorific value estimation model is a model used to determine the output calorific value. In some embodiments, the calorific value estimation model is a machine learning model, for example, a convolutional neural network (CNN) model.

[0110] In some embodiments, the supervisory management platform trains a calorific value estimation model based on multiple calorific value training samples with calorific value labels. For example, the supervisory management platform may input multiple calorific value training samples into an initial calorific value estimation model, construct a loss function based on the output of the initial calorific value estimation model and the calorific value labels, iteratively update the parameters of the initial calorific value estimation model based on the loss function, and terminate the iteration when the iteration completion condition is met, thereby obtaining a trained calorific value estimation model. Iterative update methods include gradient descent, etc. Iterative completion conditions include convergence of the loss function or reaching a threshold number of iterations.

[0111] Calorific value training samples include sample second gas data, sample gas usage information, sample terminal gas information, and sample basic calorific value for the sample end user. Calorific value training samples are obtained based on historical data. The calorific value label is the calorific value of the gas actually used by the sample end user. The calorific value label is obtained through manual measurement.

[0112] In some embodiments, the supervision and management platform can also determine the amount of impurity accumulation in the gas pipeline based on pipeline information, pipeline cleaning cycle and second gas data; determine impurity information based on the impurity accumulation amount; and determine the output calorific value based on impurity information, second gas data, gas usage information and basic calorific value.

[0113] Pipeline information refers to information related to the gas pipeline. In some embodiments, the pipeline information includes at least one of pipeline material, pipeline shape and size, and pipeline length.

[0114] The pipeline cleaning cycle refers to the cycle for cleaning impurities in the gas pipeline.

[0115] In some embodiments, the supervision management platform obtains pipeline information and pipeline cleaning cycles through a government supervision comprehensive database.

[0116] The amount of impurity accumulation reflects the impurity accumulation in the gas pipeline.

[0117] In some embodiments, the supervision and management platform constructs a second target vector based on the pipeline information, the pipeline cleaning cycle, and the second gas data, matches the second target vector to a second reference vector that meets a second matching condition in a second vector library, and determines the label corresponding to the second reference vector as the amount of impurity accumulation. The second matching condition includes having the highest vector similarity with the second target vector.

[0118] The second vector database is constructed in a similar manner to the first vector database. For implementation, refer to the first vector database construction method. The supervision and management platform obtains historical impurity accumulation through manual input and other methods, and uses this historical impurity accumulation as the label corresponding to the second reference vector. This historical impurity accumulation is obtained through manual measurement.

[0119] Impurity information refers to information related to impurities in the gas pipeline network. In some embodiments, the impurity information includes the total amount of impurities at multiple locations in the gas pipeline network. The supervision and management platform can use the sum of the impurity accumulation amount and the impurity increase amount as the total impurity amount.

[0120] In some embodiments, the amount of impurity increase is related to the amount of impurity accumulation, pipeline length, and gas flow rate. The monitoring and management platform can generate a fitting function based on a large amount of historical data on impurity accumulation, pipeline length, gas flow rate, and impurity increase. The fitting function reflects the corresponding relationship between the amount of impurity increase and the amount of impurity accumulation, pipeline length, and gas flow rate. Fitting methods include least squares methods, etc.

[0121] In some embodiments, the supervision and management platform brings the impurity accumulation amount corresponding to the most upstream node in the gas transmission map, the pipeline length between the downstream node and the gas flow rate into the fitting function to calculate the impurity increase amount corresponding to the most upstream node.

[0122] In some embodiments, when determining the impurity increase corresponding to each target node in the gas transmission map, the impurity increase can be treated as a gas component. The impurity increase corresponding to each target node can be calculated using the method for calculating the gas component content of the target node in step 230. The impurity increase corresponding to the most downstream node (representing the end user) is determined using this method.

[0123] In some embodiments, the supervisory management platform can input impurity information into a calorific value estimation model to obtain an output calorific value. When the calorific value estimation model input includes impurity information, the calorific value training sample also includes sample impurity information corresponding to the sample gas user. The sample impurity information is obtained through manual measurement.

[0124] Taking into account the impact of impurities in the fuel gas on the calorific value of the fuel gas, a more accurate output calorific value is determined based on the impurity information.

[0125] Step 350: Determine the terminal gas quality based on the output calorific value.

[0126] For information on terminal gas quality, see Figure 2 And related instructions.

[0127] In some embodiments, the supervisory management platform determines the terminal gas quality as the reference gas quality based on the output calorific value and impurity information by querying a second preset table for a reference gas quality corresponding to the output calorific value. The second preset table is pre-set based on manual experience, where a higher output calorific value indicates a higher reference gas quality.

[0128] In some embodiments, the terminal gas quality is also negatively correlated with the total amount of impurities corresponding to the terminal user.

[0129] In some embodiments of this specification, the impact of odorization and hydrogen addition on the gas component content is taken into account, and the basic calorific value determined based on the updated gas component content is more accurate. The output calorific value is determined based on actual gas usage and the basic calorific value obtained through theoretical calculations, and the terminal gas quality is evaluated based on the output calorific value, resulting in a terminal gas quality that is more consistent with actual conditions.

[0130] Figure 4 is an exemplary schematic diagram of an estimation model according to some embodiments of this specification.

[0131] In some embodiments, as Figure 4 As shown, the supervision and management platform determines the gas usage demand 430 through the estimation model 420 based on the end user characteristics 411, historical gas data 412, the type of gas equipment 413, the regional environmental information 414 and the regional activity information 415, and determines the gas quality demand based on the gas usage demand 430.

[0132] For information on end-user characteristics, gas quality requirements, and regional environmental information, see Figure 2 and Figure 3 And related instructions.

[0133] The estimation model is a model used to estimate gas usage demand. In some embodiments, the estimation model is a machine learning model, for example, a Long Short-Term Memory (LSTM) model.

[0134] Historical gas data refers to data related to end-user gas usage over time. For example, historical gas usage and historical output calorific value are obtained based on historical data.

[0135] Gas equipment refers to gas equipment used by end users. Types of gas equipment include water heaters, gas stoves, etc. In some embodiments, the supervision and management platform obtains the type of gas equipment through the device object platform.

[0136] Regional activity information refers to information related to activities in the region where the end user is located, such as energy-saving policies, special holidays, etc. In some embodiments, the supervision and management platform obtains regional activity information through manual input or other methods.

[0137] Gas demand refers to the end-user's demand for gas energy in the future. Gas energy refers to the total heat generated by the amount of gas required by the end-user after combustion.

[0138] In some embodiments, the gas usage demand includes the gas energy required by the end user at the current time point and in a future time period. The future time period is preset based on experience and includes multiple future time points.

[0139] In some embodiments, the estimation model is trained based on a sample dataset, which includes multiple training samples and labels corresponding to the multiple training samples. The training process of the estimation model is similar to the training process of the calorific value estimation model. For implementation details, refer to the training process of the calorific value estimation model.

[0140] Training samples include the corresponding end-user characteristics, historical gas data, gas equipment type, activity information, and environmental information. The labels for these training samples are the actual gas energy consumed by the end-user at the time of training sample collection and for a period of time thereafter. Training samples are derived from historical data. The monitoring and management platform calculates the actual gas energy consumed by the end-user by multiplying the end-user's historical gas usage by the historical output calorific value.

[0141] In some embodiments, the training process includes an initial phase and an intensive training phase. In the initial phase, the sample data set is obtained based on the general data set. In the intensive training phase, the sample data set is obtained based on the historical data of the target user.

[0142] The initial stage refers to the stage when the estimation model is not trained for target users. Target users include the end users corresponding to the current monitoring sampling area.

[0143] In some embodiments, the general data set includes historical data of end users in multiple other cities or other regions, etc. The general data set is obtained through a cloud platform or a third-party platform.

[0144] The intensive training phase refers to the phase in which the estimation model is trained for target users.

[0145] In some embodiments, the proportion of training samples corresponding to different user types among the target users meets a preset training condition. The preset training condition includes that the proportion of training samples corresponding to different user types among the target users is greater than a preset threshold.

[0146] In some embodiments, the preset threshold is related to the historical gas usage of target users of different user types. For example, the larger the average historical gas usage of multiple target users corresponding to each user type, the larger the preset threshold.

[0147] Mixing complexity is used to characterize the complexity of gas at the target user. In some embodiments, for each downstream node in the gas transmission map, the supervision and management platform counts the upstream nodes that provide gas to the downstream node and uses the number of nodes between the upstream and downstream nodes as the mixing complexity of the target user corresponding to the downstream node. If multiple upstream nodes provide gas to the downstream node, the node with the largest number of nodes is used as the mixing complexity of the target user corresponding to the downstream node.

[0148] In some embodiments, the preset threshold is also related to the mixing complexity of target users of different user types. For example, the larger the average value of the historical mixing complexity of multiple target users corresponding to each user type is, the larger the preset threshold is.

[0149] The initial training phase uses pre-training on a common dataset, ensuring the model's strong generalization and adaptability to a variety of application scenarios. The enhanced training phase personalizes the model to the characteristics of specific user groups, improving prediction accuracy for specific users. By adjusting the preset threshold based on the user type's average historical gas usage and mixture complexity, we can avoid inaccurate predictions for certain user types due to insufficient training samples.

[0150] In some embodiments, as Figure 4 As shown, the input of the estimation model 420 also includes impurity information 416 .

[0151] For information on impurities, see Figure 3 And related instructions.

[0152] In some embodiments, when the input of the estimation model includes impurity information, the training sample also includes sample impurity information. The sample impurity information is obtained through manual measurement.

[0153] Impurities affect the calorific value of gas, and the presence of impurities can also affect the performance of gas equipment. Therefore, considering impurity information in the estimation model can more accurately determine gas usage demand.

[0154] In some embodiments, the monitoring and management platform calculates the average gas energy required by the end user for the future time period based on the gas energy at multiple future time points in the gas usage demand. The platform then calculates the ratio of the average gas energy to the current gas usage to obtain the future gas calorific value. The monitoring and management platform then searches a first preset table for a reference gas calorific value that is identical to the future gas calorific value and determines the gas quality requirement based on the reference gas calorific value. For a description of the first preset table and determining the gas quality requirement based on the reference gas calorific value, see step 250 and its related description.

[0155] Taking into account that end users have different demands for gas energy at different time points, and that it takes a certain amount of time for the actual gas mixture to change, the estimation model is used to determine the average gas energy in the future period, and based on the average gas energy, the future gas calorific value is determined and the gas quality requirements are further determined, so as to meet the changing demands of end users for gas energy as much as possible.

[0156] Some embodiments of this specification also provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the gas quality monitoring method for an IoT-based smart gas pipeline network as described in any one of the above embodiments.

[0157] Furthermore, certain features, structures, or characteristics of one or more embodiments of the present invention may be appropriately combined.

[0158] If there is any inconsistency or conflict between the descriptions, definitions, and / or usage of terms in the referenced materials of this invention and the contents of this invention, the descriptions, definitions, and / or usage of terms in this invention shall prevail.

Claims

1. A smart gas pipe network gas quality monitoring system based on the Internet of Things, characterized by: The system includes a government gas supervision management platform, a government gas supervision sensor network platform, a government gas supervision object platform, a gas company sensor network platform, a device object platform, a gas user platform and a gas company service platform, wherein the government gas supervision object platform includes a gas company management platform; The government gas supervision and management platform is configured to: Determining a monitoring sampling area based on first gas data, where the first gas data is global gas data corresponding to the gas pipeline network acquired by the device object platform based on a low sampling rate, where the low sampling rate is a sampling rate lower than a first sampling threshold; Determining a monitoring frequency of a gas monitoring device within the monitoring sampling area based on a steady-state value of the monitoring sampling area, wherein the steady-state value is used to reflect the consistency of gas data collected by the gas monitoring device within the monitoring sampling area; determining the terminal gas quality based on second gas data and gas input information, wherein the second gas data is gas data corresponding to the monitoring sampling area acquired by the device object platform based on a high sampling rate after determining the monitoring frequency, wherein the high sampling rate is a sampling rate higher than the second sampling threshold; Determine gas quality requirements based on end-user characteristics and gas regulation data; In response to the terminal gas quality not meeting the gas quality requirement, determining to update the mixing parameter; generating an updated mixing instruction based on the updated mixing parameters, and sending the updated mixing instruction to the gas company management platform, so that the gas company management platform updates the mixing parameters of the gas mixing device; The government gas supervision and management platform is further configured to: Acquiring original odorization information and hydrogen doping information through the device object platform, wherein the original odorization information is information related to the odor added to the fuel gas by the odorization device; Obtaining gas usage information through the device object platform; determining terminal gas information and a basic calorific value based on the original odorization information, the hydrogen doping information, and the original gas information, wherein the original gas information is information related to the gas received by the terminal user before the odor and hydrogen are added to the gas, and the terminal gas information is information related to the gas received by the terminal user after the odor and hydrogen are added to the gas, and the basic calorific value is an estimated calorific value of the gas after odorization and hydrogen doping; determining an output calorific value based on the second gas data, the gas usage information, the terminal gas information, and the basic calorific value; The terminal gas quality is determined based on the output calorific value.

2. The system according to claim 1, wherein The government gas supervision and management platform is further configured to: Determining user priority based on the gas usage information and the end user characteristics; Based on the user priority, the updated mixing parameter is determined.

3. The system according to claim 1, wherein: The government gas supervision and management platform is further configured to: determining an amount of impurities accumulated in the gas pipeline based on the pipeline information, the pipeline cleaning cycle, and the second gas data; determining impurity information based on the impurity accumulation amount; The output calorific value is determined based on the impurity information, the second gas data, the gas usage information, and the basic calorific value.

4. The system according to claim 1, wherein: The government gas supervision and management platform is further configured to: Determining gas usage demand using an estimation model based on the end user characteristics, historical gas data, type of gas equipment, regional activity information, and regional environmental information, wherein the estimation model is a machine learning model; The gas quality requirement is determined based on the gas usage requirement.

5. A gas quality monitoring method for a smart gas pipeline network based on the Internet of Things, characterized in that: The method is executed by a government gas supervision and management platform in a gas quality monitoring system of a smart gas pipeline network based on the Internet of Things, and the method includes: Determining a monitoring sampling area based on first gas data, where the first gas data is global gas data corresponding to the gas pipeline network acquired by the device object platform based on a low sampling rate, where the low sampling rate is a sampling rate lower than a first sampling threshold; Determining a monitoring frequency of a gas monitoring device within the monitoring sampling area based on a steady-state value of the monitoring sampling area, wherein the steady-state value is used to reflect the consistency of gas data collected by the gas monitoring device within the monitoring sampling area; determining the terminal gas quality based on second gas data and gas input information, wherein the second gas data is gas data corresponding to the monitoring sampling area acquired by the device object platform based on a high sampling rate after determining the monitoring frequency, wherein the high sampling rate is a sampling rate higher than the second sampling threshold; Determine gas quality requirements based on end-user characteristics and gas regulation data; In response to the terminal gas quality not meeting the gas quality requirement, determining to update the mixing parameter; generating an updated mixing instruction based on the updated mixing parameters, and sending the updated mixing instruction to the gas company management platform, so that the gas company management platform updates the mixing parameters of the gas mixing device; The method further comprises: Acquiring original odorization information and hydrogen doping information through the device object platform, wherein the original odorization information is information related to the odor added to the fuel gas by the odorization device; Obtaining gas usage information through the device object platform; determining terminal gas information and a basic calorific value based on the original odorization information, the hydrogen doping information, and the original gas information, wherein the original gas information is information related to the gas received by the terminal user before the odor and hydrogen are added to the gas, and the terminal gas information is information related to the gas received by the terminal user after the odor and hydrogen are added to the gas, and the basic calorific value is an estimated calorific value of the gas after odorization and hydrogen doping; determining an output calorific value based on the second gas data, the gas usage information, the terminal gas information, and the basic calorific value; The terminal gas quality is determined based on the output calorific value.

6. The method according to claim 5, wherein The method further comprises: Determining user priority based on the gas usage information and the end user characteristics; Based on the user priority, the updated mixing parameter is determined.

7. The method according to claim 5, wherein The determining of the output calorific value based on the second gas data, the gas usage information, the terminal gas information, and the basic calorific value includes: determining an amount of impurities accumulated in the gas pipeline based on the pipeline information, the pipeline cleaning cycle, and the second gas data; determining impurity information based on the impurity accumulation amount; The output calorific value is determined based on the impurity information, the second gas data, the gas usage information, and the basic calorific value.

8. The method according to claim 5, wherein The method further comprises: Determining gas usage demand using an estimation model based on the end user characteristics, historical gas data, type of gas equipment, regional activity information, and regional environmental information, wherein the estimation model is a machine learning model; The gas quality requirement is determined based on the gas usage requirement.

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