A smart gas flow monitoring method and system based on the Internet of Things

Abstract

The application provides a kind of wisdom gas flow monitoring method and system based on Internet of Things, it is related to gas monitoring field.The method comprises: based on the sensing data uploaded by pipeline sensor and the historical sampling data uploaded by gas gate station and / or pressure regulating station, generate gas transmission information with time label;Acquire the gas volume flow collected at multiple time nodes in the current period;Based on the gas volume flow, generate the gas standard flow of the gas transmission pipeline at multiple time nodes in the current period;Based on the gas standard flow, generate the gas cumulative flow of the gas transmission pipeline in the current period;Based on the gas cumulative flow, control the opening and closing state and / or opening amplitude of the regulating valve of the gas transmission pipeline to determine the gas flow into the gas generating device.The method can ensure the accuracy of gas flow monitoring under complex environment and complex fluid regulation, and further ensure the accuracy of gas metering and charging in gas pipeline network.

Description

Technical Field

[0001] This manual relates to the field of gas monitoring, and in particular to a smart gas flow monitoring method and system based on the Internet of Things. Background Technology

[0002] With the acceleration of urbanization and the increasing demand for clean energy, natural gas, as a clean and efficient energy source, is being used more and more widely in daily life and industrial production. However, the accuracy of gas metering directly affects the economic benefits of gas supply companies and the user experience. Traditional gas metering methods are mainly based on volumetric flow rate measurement, but this method often fails to provide accurate measurement results when faced with fluctuations in gas pressure, changes in gas composition, or differences in impurity content. Especially in complex environments, factors such as differences in pipe inner diameter and the influence of pipe deposits can lead to data distortion using traditional metering methods.

[0003] This specification aims to provide an IoT-based smart gas flow monitoring method and system. By combining information obtained from sensors configured in gas transmission pipelines and utilizing big data analysis methods and machine learning models to dynamically adjust metering parameters, it not only improves the accuracy of gas metering but also provides strong support for smart city construction and energy management. Summary of the Invention

[0004] The invention includes an IoT-based intelligent gas flow monitoring method. The method is executed by a gas company's management platform and includes: generating time-stamped gas delivery information based on sensor data uploaded by pipeline sensors and historical sampling data uploaded by gas gate stations and / or pressure regulating stations, and storing this information on the company's server; acquiring gas volumetric flow rates collected by gas metering devices deployed on the gas delivery pipeline at multiple time points within the current period; generating standard gas flow rates for the gas delivery pipeline at multiple time points within the current period through data preprocessing based on the gas volumetric flow rates; generating cumulative gas flow rates for the gas delivery pipeline within the current period based on the standard gas flow rates; and controlling the opening and closing status and / or opening range of regulating valves in the gas delivery pipeline based on the cumulative gas flow rates to determine the gas flow rate entering the gas generating device.

[0005] In order to improve the accuracy and timeliness of gas metering and enhance the user experience of gas users, this invention provides a smart gas flow monitoring method and system based on the Internet of Things.

[0006] The invention includes an IoT-based intelligent gas flow monitoring system. The system comprises a gas user platform, a gas service platform, a gas company management platform, a gas company sensor network platform, and a gas equipment object platform; wherein the gas company management platform is configured on one or more company servers; the gas company management platform is configured to execute an IoT-based intelligent gas flow monitoring method.

[0007] Beneficial effects: (1) Ensure the applicability and accuracy of the gas metering device, so that the system platform can adapt to gas flow monitoring under complex environment and complex fluid control, thereby ensuring the accuracy of gas metering and billing in the gas pipeline network; (2) Under reasonable premise (e.g., overcharging), the opening range of the regulating valve corresponding to the gas user can be appropriately reduced to avoid greater billing losses or to avoid affecting the user's gas consumption. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 This is a schematic diagram of the platform structure of an IoT-based smart gas flow monitoring system according to some embodiments of this specification;

[0010] Figure 2 This is an exemplary flowchart of an IoT-based smart gas flow monitoring method according to some embodiments of this specification;

[0011] Figure 3 This is an exemplary flowchart illustrating the generation of standard gas flow rates at multiple sampling time points, as shown in some embodiments of this specification.

[0012] Figure 4 This is an exemplary flowchart of another flowchart for generating standard gas flow rates at multiple sampling time points, as shown in some embodiments of this specification.

[0013] Figure 5 These are exemplary schematic diagrams of transformation models shown according to some embodiments of this specification;

[0014] Figure 6 This is an exemplary schematic diagram illustrating the determination of a faulty device according to some embodiments of this specification. Detailed Implementation

[0015] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0016] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0017] Unless the context clearly indicates an exception, words such as "a," "an," "a kind," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

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

[0019] Figure 1 This is a platform structure diagram of an IoT-based smart gas flow monitoring system, as shown in some embodiments of this specification.

[0020] In some embodiments, such as Figure 1 As shown, the IoT-based smart gas flow monitoring system 100 may include a gas company management platform 130, a gas company sensor network platform 140, a gas equipment object platform 150, a gas service platform 120, and a gas user platform 110.

[0021] In some embodiments, data interaction can be achieved between all platforms of the IoT-based smart gas flow monitoring system 100. For example, the gas company sensor network platform 140 acquires the gas volume flow rate collected by the gas equipment object platform 150 at multiple time nodes in the current period, and sends the collected gas volume flow rate to the gas company management platform 130.

[0022] The gas company management platform 130 refers to a comprehensive platform that coordinates and integrates the connections and collaborations between various functional platforms of a gas company, gathers all information from the Internet of Things, and generates and executes instructions by analyzing and processing data and information generated during the operation of the gas company. In some embodiments, the gas company management platform 130 is configured on one or more sets of company servers, which include memory and processors.

[0023] In some embodiments, the gas company management platform 130 can be configured to determine the gas flow rate entering the gas generator based on sensor data uploaded by pipeline sensors and historical sampling data uploaded by gas gate stations and / or pressure regulating stations. More information on this section can be found in [link to relevant documentation]. Figure 2 Related descriptions.

[0024] The gas company sensor network platform 140 refers to a comprehensive management platform for the gas company's sensor information. In some embodiments, the gas company sensor network platform 140 can be configured as a communication device and / or gateway, etc. For example, the gas company sensor network platform 140 can be configured as a communication network and gateway to realize data management functions, data transmission functions, etc.

[0025] The gas equipment platform 150 refers to a functional platform for real-time monitoring and intelligent control of gas pipeline networks. In some embodiments, the gas equipment platform 150 includes at least various pipeline sensors, gas gate stations and / or pressure regulating stations, gas metering devices, and gas control devices arranged in the gas pipeline network. For more information on gas metering devices, see [link to relevant documentation]. Figure 2 And its related descriptions.

[0026] A gas control device refers to equipment used to control and regulate the flow of gas in a gas pipeline network. In some embodiments, a gas control device may include regulating valves in the gas delivery pipeline.

[0027] The gas service platform 120 refers to a platform used to communicate user needs and control information. In some embodiments, the gas service platform 120 may be configured as a processor and / or server, etc.

[0028] The gas user platform 110 refers to a platform used for interacting with users. In some embodiments, the gas user platform 110 may be configured as a terminal device and / or a server. The terminal device may include the user's terminal device, such as a smartphone, tablet, etc.

[0029] For more information about the above platforms, please refer to [link / reference]. Figures 2-4 And related descriptions.

[0030] Some embodiments of this specification, the Internet of Things-based smart gas flow monitoring system 100, can form an information operation closed loop between various functional platforms, and operate in a coordinated and regular manner under the unified management of the gas company's management platform, realizing the informatization and intelligentization of gas flow status monitoring and management in gas pipelines.

[0031] Figure 2 This is an exemplary flowchart illustrating an IoT-based smart gas flow monitoring method according to some embodiments of this specification. Figure 2 As shown, process 200 includes the following steps. In some embodiments, process 200 may be executed by a gas company management platform in an IoT-based smart gas flow monitoring system. For example, it may be executed by a processor in the company server where the gas company management platform resides.

[0032] Step 210: Based on the sensing data uploaded by the pipeline sensor and the historical sampling data uploaded by the gas gate station and / or pressure regulating station, generate gas delivery information with time tags and store it in the company's server.

[0033] Pipeline sensors may include pressure sensors, temperature sensors, etc., deployed inside gas transmission pipelines to acquire sensing data of the gas being transported inside the pipeline, such as gas pressure and gas temperature.

[0034] A gas gate station is a receiving station for gas entering the city's gas network from a long-distance pipeline. In some embodiments, a gas gate station is equipped with hardware facilities such as filters, separators, sampling devices, and gas analyzers.

[0035] A pressure regulating station is a facility node that regulates the pressure of gas in a gas pipeline during the gas pipeline transportation process. In some embodiments, a pressure regulating station may be equipped with hardware facilities such as pressure reducing valves and pressure regulators.

[0036] Historical sampling data refers to gas-related data obtained from the analysis of gas components and impurity content at key nodes such as gas gate stations and / or pressure regulating stations over the current and past periods. This data can be obtained through analysis of collected gas samples using analytical devices such as gas analyzers configured at gas gate stations and / or pressure regulating stations.

[0037] Gas delivery information refers to the physical state data of gas collected and recorded based on changes over time during the gas delivery process. In some embodiments, gas delivery information includes gas delivery pressure, gas composition data, gas impurity content, and corresponding time stamps for these data, wherein the time stamps are used to record the specific time information when these data were collected.

[0038] Step 220: Obtain the gas volumetric flow rate collected by the gas metering device deployed on the gas transmission pipeline at multiple time points within the current period.

[0039] A gas metering device refers to equipment used to collect and record the volumetric flow rate of gas in a gas pipeline. In some embodiments, a gas metering device may include an ultrasonic flow meter, a turbine flow meter, etc., deployed in the gas pipeline.

[0040] The current time period refers to the time period to which the current moment belongs. In some embodiments, the length and time nodes of the current time period can be preset manually. For example, the current time period may be the 9 days from the current moment to the present moment, with a time node set every 3 days.

[0041] Gas volumetric flow rate refers to the volume of gas passing through the cross-section of a pipeline per unit time. In some embodiments, gas volumetric flow rate can be collected based on a gas metering device.

[0042] In some embodiments, the gas volume flow rate collected by the gas metering device at multiple time points can be uploaded to the gas equipment object platform via the network, and then the gas equipment object platform aggregates and uploads it to the gas company's management platform.

[0043] Step 230: Based on the gas volumetric flow rate, generate the standard gas flow rate of the gas transmission pipeline at multiple time points within the current period through data preprocessing.

[0044] In some embodiments, data preprocessing may include data format conversion, data value transformation, data sieving, etc.

[0045] Standard flow rate of natural gas refers to the volumetric flow rate of natural gas passing through the cross-section of a pipeline per unit time under standard conditions.

[0046] In some embodiments, the gas company management platform can obtain the standard gas flow rate based on the gas volumetric flow rate. For example, the gas company management platform can query a first preset table based on the gas volumetric flow rate and the gas pressure and gas temperature monitored when obtaining the gas volumetric flow rate to obtain the standard gas flow rate. The first preset table refers to a table that includes multiple sets of one-to-one correspondences between [gas pressure, gas temperature, gas volumetric flow rate] and the standard gas flow rate. In some embodiments, the first preset table can be constructed based on the gas volume (i.e., standard gas flow rate) measured under standard conditions (e.g., standard atmospheric pressure, 25°C) for different masses of gas, and the gas volume (i.e., gas volumetric flow rate) measured under different gas pressure and gas temperature conditions.

[0047] In some embodiments, the gas company management platform can also obtain upstream and downstream difference information of the gas transmission pipeline in the current time period based on the gas equipment object platform; generate gas characteristic changes of the gas transmission pipeline in the current time period based on gas transmission information at multiple sampling time points in the current time period; generate gas standard flow rates at multiple sampling time points based on upstream and downstream difference information and gas characteristic changes in the current time period, as well as gas volume flow rates at multiple sampling time points; and adjust the sampling time point interval of the gas transmission pipeline based on the gas standard flow rates at multiple sampling time points.

[0048] Upstream-downstream difference information refers to information reflecting the differences between an upstream gas pipeline and its adjacent downstream gas pipeline. In some embodiments, upstream-downstream difference information may include the difference in the thickness of pipe attachments and the difference in the inner diameter of the pipes between the upstream gas pipeline and its adjacent downstream gas pipeline.

[0049] In some embodiments, the gas company's management platform can obtain the thickness of the pipe attachments for each pipe section based on the recorded data from the gas company's regular maintenance and inspection of the gas pipelines, and calculate the difference in the thickness of the pipe attachments. In some embodiments, the gas company's management platform can obtain the inner diameter of each gas pipeline section from the pipeline specification data uploaded during pipeline installation, and calculate the difference in the inner diameter of the pipe.

[0050] A sampling time point refers to a specific point in time set during the gas transportation process to obtain gas transportation information. The interval between adjacent sampling time points is smaller than the interval between adjacent time nodes.

[0051] Gas characteristic changes refer to the changes in various characteristic indicators in gas delivery information at different sampling time points. Gas characteristic changes can be represented by a vector sequence consisting of changes in gas delivery pressure, gas composition, and gas impurity content at corresponding adjacent sampling time points within multiple sampling time intervals.

[0052] In some embodiments, the gas company's management platform can obtain the standard gas flow rate at multiple sampling time points based on the gas volumetric flow rate at multiple sampling time points. For more details on determining the standard gas flow rate based on the gas volumetric flow rate, please refer to the foregoing.

[0053] In some embodiments, the processor can determine the standard gas flow rate based on historical metering data from different gas delivery pipelines. More details on this section can be found in [link to relevant documentation]. Figure 3 and Figure 4 Related descriptions.

[0054] The sampling time interval refers to the time interval between adjacent sampling time points. In some embodiments, the gas company management platform can adjust the sampling time interval based on the rate of change of the standard gas flow rate corresponding to adjacent sampling time points. For example, the adjusted sampling time interval can be calculated as: Adjusted sampling time interval = Unadjusted sampling time interval The adjustment is made using (1 - the rate of change of the standard gas flow rate). Wherein, the rate of change of the standard gas flow rate = |standard gas flow rate at sampling time point 1 - standard gas flow rate at sampling time point 2| / standard gas flow rate at sampling time point 1.

[0055] In some embodiments of this specification, the gas company's management platform can dynamically adjust the relevant parameters of data preprocessing involved in generating the standard gas flow rate based on the gas volumetric flow rate by considering the changes in gas characteristics at multiple sampling time points, thereby ensuring the reliability of gas flow rate measurement and its applicability in complex environments. Furthermore, by dynamically adjusting the sampling time interval according to changes in the standard gas flow rate, the effectiveness of the sampling data can be improved.

[0056] Step 240: Based on the standard gas flow rate, generate the cumulative gas flow rate of the gas transmission pipeline for the current time period.

[0057] The cumulative flow of gas refers to the total volumetric flow of gas passing through the cross-section of a pipe over a period of time.

[0058] In some embodiments, the gas company's management platform can interpolate and calculate the cumulative gas flow rate by using the standard gas flow rates corresponding to multiple adjacent time points within the current time period. For example, the current time period is 24 hours long and includes time points 1 and 2. During gas delivery, at time point 1, the standard gas flow rate is 2 m³ / s. 3 / h; at time point 2, the standard gas flow rate is 3m³ / h. 3 / h, then the cumulative gas flow rate in the current 24-hour period can be calculated as follows: Cumulative gas flow rate = (2m 3 / h+3m 3 / h) / 2×24h=60m 3 .

[0059] Step 250: Based on the cumulative gas flow rate, control the opening and closing status and / or opening range of the regulating valve in the gas transmission pipeline to determine the gas flow rate entering the gas generator.

[0060] In some embodiments, when the historical cumulative flow plus the current gas cumulative flow is about to reach a preset flow threshold, the gas company management platform can determine the opening range of the regulating valve based on the gas cumulative flow, the historical cumulative flow, and the preset flow threshold, and generate a regulating command based on the opening range of the regulating valve. The regulating command is then sent to the gas equipment object platform to instruct the gas equipment object platform to control the opening and closing state and / or opening range of the regulating valve according to the opening range of the regulating valve.

[0061] Historical cumulative flow refers to the total volumetric flow of gas consumed by gas users before the current time period. The preset flow threshold is a set upper limit for gas volumetric flow usage; this threshold can be manually preset or determined based on users' prepaid fees. For example, the gas company's management platform can determine the valve opening range based on the following formula:

[0062] ,

[0063] in, To adjust the valve opening range, For the cumulative gas flow, This is the cumulative historical traffic. To preset the traffic threshold, This represents the threshold score. Its size is between 0 and 1, and can be preset manually.

[0064] A gas generating device is a device or system that maintains its operation by consuming gas. In some embodiments, a gas generating device may include a gas stove, a gas turbine, or other similar equipment.

[0065] Gas flow rate refers to the amount of gas passing through a pipeline per unit time, which can be expressed as gas volumetric flow rate (e.g., cubic meters per hour, m³ / h) or gas standard flow rate (e.g., cubic meters per Nm³). For more information on gas volumetric flow rate, gas standard flow rate, and their acquisition methods, please refer to the aforementioned content.

[0066] In some embodiments of this specification, the gas company management platform can, when gas pressure, gas composition, or impurity content (affecting gas density, calorific value, etc.) frequently change or fluctuate, reasonably transform the data collected by gas metering and set reasonable metering parameters to ensure the applicability and accuracy of the gas metering device. This enables the system platform to adapt well to gas flow monitoring under complex environments and complex fluid control conditions, thereby ensuring the accuracy of gas metering and billing in the gas pipeline network. Furthermore, under reasonable premises (e.g., when overcharging is imminent), it can appropriately reduce the opening range of the regulating valve corresponding to the gas user to avoid greater billing losses or to avoid affecting the user's gas usage.

[0067] Figure 3 This is an exemplary flowchart illustrating the generation of standard gas flow rates at multiple sampling time points, according to some embodiments of this specification. Figure 3 As shown, process 300 includes the following steps. In some embodiments, process 300 may be executed by a gas company management platform in an IoT-based smart gas flow monitoring system. For example, it may be executed by a processor in the company server where the gas company management platform resides.

[0068] Step 310: Based on historical metering data of different gas transmission pipelines, generate a transformation database and set it on the company's server; the transformation database includes multiple upstream and downstream difference information, gas transmission information, gas characteristic changes and corresponding first transformation algorithms.

[0069] Historical metering data refers to information related to gas flow rate recorded in historical records. Gas flow rate includes gas volumetric flow rate and gas standard flow rate.

[0070] In some embodiments, historical metering data can be obtained from the database of the gas company's management platform.

[0071] A transformation database refers to a database used for gas metering. In some embodiments, the transformation database is constructed based on a large amount of historical gas delivery data.

[0072] In some embodiments, the transformation database includes multiple upstream and downstream difference information, gas transmission information, gas characteristic changes, and a corresponding first transformation algorithm. For details regarding upstream and downstream difference information, gas transmission information, and gas characteristic changes, please refer to [link to relevant documentation]. Figure 2 And related explanations.

[0073] The first transformation algorithm refers to the relevant algorithm used for gas metering. The independent variables in the first transformation algorithm can be upstream and downstream difference information, gas transmission information, gas volumetric flow rate data corresponding to changes in gas characteristics, and standard gas flow rate data; the dependent variable in the first transformation algorithm can be a combination of corresponding weighting coefficients. In some embodiments, the gas company management platform can determine the first transformation algorithm in multiple ways.

[0074] In some embodiments, the gas company management platform utilizes a processor in the company server. The processor is based on historical gas delivery data, including multiple identical or similar (e.g., differences of no more than 5%) upstream and downstream differences, gas delivery information, and gas volume flow data corresponding to changes in gas characteristics; and gas standard flow data acquired based on a sampling device; and determines the parameters in each first transformation algorithm based on a fitting method.

[0075] In some embodiments, for a set of identical or similar upstream and downstream difference information, gas transmission information, and gas characteristic changes, the standard gas flow rate can be calculated and determined by formula (1).

[0076] (1)

[0077] Where a, b, and c are weighting coefficients; m1, m2, and m3 are upstream and downstream difference information, gas transmission information, and gas characteristic changes after normalization or standardization, respectively.

[0078] In some embodiments, the methods used for normalization or standardization may include Z-score standardization, Min-Max standardization, etc.

[0079] Formula (1) can be considered as the first transformation algorithm corresponding to the same or similar upstream and downstream differences, gas transmission information, and gas characteristic changes. Different first transformation algorithms have different weight coefficients.

[0080] In some embodiments, for each first transformation algorithm, the gas company management platform determines the weighting coefficients involved in the first transformation algorithm based on a fitting method using a set of identical or similar historical metering data.

[0081] In some embodiments, the above method is used to generate a large number of first transformation algorithms and construct a transformation database by using historical metering data and its corresponding multiple upstream and downstream difference information, gas transmission information, and gas characteristic changes.

[0082] It should be understood that formula (1) is only an exemplary implementation of the first transformation algorithm. In other embodiments, the first transformation algorithm may also be other mathematical models trained based on historical data, such as multivariate nonlinear regression models, support vector machine models, etc., which are not limited in this application specification.

[0083] Step 320: Based on the upstream and downstream difference information, gas characteristic changes and gas transmission information in the current time period, the target first transformation algorithm is determined by searching the transformation database.

[0084] The target first transformation algorithm refers to the most suitable first transformation algorithm selected by comprehensively considering current upstream and downstream differences, gas transmission information, and changes in gas characteristics. For example, the transformation database contains the first transformation algorithms corresponding to current upstream and downstream differences, gas characteristic changes, and gas transmission information.

[0085] Step 330: Based on the gas volumetric flow rate, generate the gas standard flow rate using the target first transformation algorithm.

[0086] In some embodiments, the gas company management platform can input the gas volume flow rate, current upstream and downstream differences, gas transportation information, and gas characteristic changes into the target first transformation algorithm to obtain the standard gas flow rate.

[0087] In some embodiments, in response to upstream and downstream differences and changes in gas characteristics satisfying preset stability conditions, a standard gas flow rate is generated based on the gas volumetric flow rate using a target first transformation algorithm.

[0088] The preset stability condition refers to the difference between upstream and downstream discrepancies and changes in gas characteristics being less than a second difference threshold. Here, "difference" refers to the value between two data points. For example, the difference could be the value between two data points in upstream and downstream discrepancies or the value between two data points in changes in gas characteristics.

[0089] In some embodiments, the preset stability conditions may be set by the processor by default or preset by a technician based on experience.

[0090] In some embodiments of this specification, preset stability conditions are limited by upstream and downstream difference information and the changes in gas characteristics, thereby ensuring the accuracy of the gas volume flow rate in generating the standard gas flow rate through the first transformation algorithm.

[0091] In some embodiments of this specification, a transformation database is generated using historical metering data from different gas transmission pipelines. A target first transformation algorithm is determined by searching the transformation database, and the standard gas flow rate is then determined using the gas volume flow rate through the target first transformation algorithm. This allows for faster and more accurate measurement of gas flow rate, saving manpower and resources.

[0092] Figure 4 This is another exemplary flowchart illustrating the generation of standard gas flow rates at multiple sampling time points, as shown in some embodiments of this specification. Figure 4 As shown, process 400 includes the following steps. In some embodiments, process 400 may be executed by a gas company management platform in an IoT-based smart gas flow monitoring system. For example, it may be executed by a processor in the company server where the gas company management platform resides.

[0093] Step 410: Based on historical metering data of different gas transmission pipelines, generate a transformation model and set it on the company server; the transformation model is a machine learning model.

[0094] Step 420: Based on the upstream and downstream difference information of the current time period, the gas delivery information and gas volume flow rate of multiple sampling time points, the standard gas flow rate of multiple sampling time points is generated by transforming the model.

[0095] A transformation model refers to a combination of procedures that learn patterns from and apply them to a large amount of historical gas delivery data. For example, a transformation model can be generated based on historical metering data of different gas delivery pipelines and used to determine the standard gas flow rate. In some embodiments, the transformation model can be a machine learning model, such as a Long Short-Term Memory (LSTM) artificial neural network.

[0096] Figure 5 This is an exemplary schematic diagram of a transformation model shown according to some embodiments of this specification.

[0097] In some embodiments, such as Figure 5 As shown, the input of the transformation model can include upstream and downstream difference information 510 for the current time period, gas delivery information 520 for multiple sampling time points, and gas volume flow rate 530. The output of the transformation model 540 can be the standard gas flow rate 550 for multiple sampling time points.

[0098] The parameters of the transformation model 540 can be obtained through training.

[0099] In some embodiments, a set of training samples for training the transformation model may include upstream and downstream difference information, sample gas delivery information, and sample gas volumetric flow rate for a sample time period. The training labels corresponding to the training samples are the standard flow rates of sample gas at multiple sample sampling time points during the sample time period. In some embodiments, both the training samples and their corresponding labels can be determined based on historical gas delivery data.

[0100] In some embodiments, training samples and training labels are determined based on historical metering data of different gas transmission pipelines; the training samples are divided into multiple different training sets based on upstream and downstream differences in the samples and changes in the gas characteristics of the samples; and the transformation model is trained based on the multiple different training sets.

[0101] The training samples include upstream and downstream differences, gas delivery information, gas volumetric flow rate, and gas characteristic variations. The training label corresponding to the training samples is the actual standard gas flow rate.

[0102] In some embodiments, both the training samples and their corresponding labels can be determined based on historical gas delivery data.

[0103] In some embodiments, the processor divides the training samples into multiple different training sets based on upstream and downstream differences in the samples and changes in the gas characteristics of the samples, according to preset conditions.

[0104] Preset conditions can be set by technicians based on experience. For example, training set 1 can be a training sample set where the difference between upstream and downstream pipelines is 0 and the change in gas characteristics is 0; training set 2 can be a training sample set where the difference between upstream and downstream pipelines and the change in gas characteristics are both less than 5%; and training set 3 can be a training sample set where the difference between upstream and downstream pipelines and the change in gas characteristics are between 5% and 15%.

[0105] In some embodiments, the processor trains the transformation model on multiple different training sets in a preset order. The preset order can be set by a technician based on experience.

[0106] In some embodiments, the learning rates for multiple different training sets are different. The corresponding learning rates for different training sets can be set by technicians based on experience. For example, the greater the differences in upstream and downstream pipelines and the greater the variations in gas characteristics among the samples in the training set, the lower the learning rate can be set.

[0107] In some embodiments, the processor acquires a training dataset, which includes upstream and downstream difference information of several samples, sample gas transport information, sample gas volumetric flow rate, sample gas characteristic changes, and training labels corresponding to the training samples. In some embodiments, the processor performs multiple iterations, at least one iteration including: selecting a training sample from the training set, inputting the training sample into the transformation model, and obtaining the transformation model prediction output corresponding to the training sample. In some embodiments, based on the model prediction output corresponding to the training sample and the label of the training sample, the value of the loss function is calculated by substituting it into a predefined loss function formula; and the model parameters in the transformation model are updated in reverse based on the value of the loss function. In some embodiments, various methods can be used to update the model parameters in the transformation model. For example, the model parameters in the transformation model can be updated based on the gradient descent method. When the iteration termination condition is met, the iteration ends, and the trained transformation model is obtained. The iteration termination condition may be the convergence of the loss function, the number of iterations reaching a threshold, etc.

[0108] In some embodiments, the processor can also obtain new samples and new labels corresponding to the newly installed gas metering device or the modified gas pipeline, update the transformation model, and thus effectively improve the applicability of the model.

[0109] In some embodiments of this specification, by dividing the training samples based on the processor to obtain multiple different training sets, the accuracy and reliability of the transformation model can be significantly improved.

[0110] Step 430: Generate a sampling instruction and send it to the sampling device deployed around the gas metering device to obtain gas sampling data around the gas metering device.

[0111] A sampling command is a command used to obtain a sample of gas from a gas pipeline. In some embodiments, the processor can generate a sampling command based on the standard gas flow rates at multiple sampling time points determined above.

[0112] A sampling device is an auxiliary facility installed on a gas pipeline for the temporary sampling of gas in the pipeline. For example, a sampling device can be installed in a gas valve on a gas pipeline.

[0113] Gas sampling data refers to relevant data obtained from samples of gas in gas pipelines. This includes gas sampling flow rate.

[0114] In some embodiments, the processor acquires gas sampling data via a sampling device.

[0115] In some embodiments of this specification, when generating the standard gas flow rate through the transformation model, a sampling instruction is generated to obtain gas sampling data. This can effectively shorten the time interval between obtaining gas sampling data and generating the standard gas flow rate, improve the reliability of obtaining gas sampling data and generating the standard gas flow rate, and enhance the accuracy of the transformation model.

[0116] In some embodiments of this specification, a transformation model is generated using historical metering data from different gas transmission pipelines. The trained transformation model can quickly process a large amount of gas metering data and accurately generate standard gas flow rates at multiple sampling time points, demonstrating good applicability.

[0117] In some embodiments, the transformation confidence level of the transformation model is determined based on the gas sampling flow rate and the gas standard flow rate output by the transformation model; the activation frequency of the transformation model is determined based on the transformation confidence level.

[0118] Gas sampling flow rate refers to the relevant flow rate of sampled gas in a gas pipeline. In some embodiments, the processor can collect and acquire the gas sampling flow rate near the gas metering device using a sampling device. For example, the actual standard gas flow rate determined by sampling using the sampling device can be 2.3 m³ / s. 3 / h.

[0119] Transform confidence is a numerical value that reflects the accuracy of the output of a transformed model. In some embodiments, transform confidence can be represented by a value between 0 and 1, with a higher value indicating a higher transform confidence.

[0120] In some embodiments, the processor determines the transformation confidence level of the transformation model based on the gas sample flow rate and the standard gas flow rate output by the transformation model using a formula. By way of example only, the transformation confidence level can be calculated using formula (2).

[0121] (2)

[0122] For details regarding the gas sampling flow rate and the standard gas flow rate output by the transformation model, please refer to the above description.

[0123] In some embodiments, the processor determines the activation frequency of the transformation model based on the transformation confidence using formula (3).

[0124] (3)

[0125] The reference activation frequency refers to the standard frequency at which the transform model is activated. In some embodiments, the reference activation frequency can be determined based on the sampling time point. For details regarding the sampling time point, please refer to [link to relevant information]. Figure 2 And its related descriptions.

[0126] In some embodiments, the activation frequency of the transformation model can also be adjusted according to user needs.

[0127] In some embodiments of this specification, there are significant differences between different gas pipelines, and the environments in which gas pipelines are located are complex. The transformation model does not always operate reliably. When the output results of the transformation model are inaccurate, the activation frequency of the transformation model is reduced. Therefore, the processor determines the activation frequency of the transformation model by obtaining the transformation confidence level based on the gas sample flow rate and the standard gas flow rate output by the transformation model, which can reduce the data processing burden on the gas company's management platform.

[0128] In some embodiments, the processor acquires assembly data of the gas delivery pipeline and the gas metering device; based on the assembly data and the transformation confidence level, it determines the activation status of the transformation model.

[0129] Assembly data refers to data related to the installation, use, and maintenance of gas transmission pipelines, supporting facilities, and gas metering devices. For example, supporting facilities may include sensors and gas metering devices. Assembly data includes the initial installation time and the last repair / maintenance time of the gas transmission pipelines, supporting facilities, and gas metering devices.

[0130] In some embodiments, the gas company's management platform can obtain assembly data of gas transmission pipelines and associated ancillary facilities based on an online database.

[0131] The activation status of a transformation model refers to whether or not the transformation model is used.

[0132] In some embodiments, the processor determines the activation status of the transformation model based on assembly data of the gas pipeline and gas metering device, using a preset time interval and an average transformation confidence level within that preset time interval. For example, if the interval between the last repair / maintenance time of the gas pipeline, ancillary facilities, and gas metering device and the current time is less than the processor's preset time interval, and the average transformation confidence level within the preset time interval is lower than a preset transformation confidence level threshold, then the transformation model is not activated. The preset time interval can be pre-set by the processor. For example, the preset time interval can be one week. The transformation confidence level threshold can be pre-set by a technician.

[0133] In some embodiments, the preset time period can be determined based on the initial installation time, the last repair / maintenance time, and the current time. For example, the smaller of the time interval between the initial installation time and the current time, and the time interval between the last repair / maintenance time and the current time, can be used as a reference time period, and the ratio of the reference time period to the number of sampling time points per unit time can be used as the preset time period. For details regarding sampling time points, please refer to... Figure 2 And its related descriptions.

[0134] In some embodiments of this specification, by acquiring assembly data of the gas transmission pipeline and the gas metering device, and by changing the confidence level, the activation status of the transformation model can be determined, thereby improving the reliability of gas metering. Using the above method, when dealing with newly installed or renovated pipelines and ancillary facilities, since the accuracy of the transformation model may be inaccurate, it is appropriate to choose not to activate it.

[0135] It should be noted that the above descriptions of processes 200, 300, and 400 are for illustrative purposes only and do not limit the scope of this specification. Those skilled in the art can make various modifications and changes to processes 200, 300, and 400 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0136] Figure 6 This is an exemplary schematic diagram illustrating the determination of a faulty device according to some embodiments of this specification.

[0137] In some embodiments, the gas company management platform can also generate a fault judgment result 630 for the gas pipeline where the gas metering device is located based on the gas reference flow rate 610 and the gas standard flow rate 620 corresponding to the gas metering device; in response to the fault judgment result 630 indicating that there is a device fault, a troubleshooting instruction 650 is issued to control the auxiliary equipment of the gas pipeline to perform a self-test to confirm the faulty equipment.

[0138] Fault diagnosis result 630 refers to the result of determining whether there is a fault in the auxiliary equipment of the gas transmission pipeline. In some embodiments, fault diagnosis result 630 may include the judgment result of whether the pressure regulating parameters of the gas pressure regulating equipment are set incorrectly, or whether the cold energy recovery and utilization equipment of the gas pipeline has stopped operating, etc.

[0139] In some embodiments, the gas company management platform can determine the absolute value of the gas metering device's difference based on the absolute value of the difference between the gas reference flow rate 610 and the gas standard flow rate 620 corresponding to the gas metering device, and determine whether the absolute value of the difference is greater than a first difference threshold preset by the system (the first difference threshold can be manually preset), in order to determine whether there is an equipment fault in the gas transmission pipeline where the gas metering device is located. Specifically, if the absolute value of the difference is greater than the first difference threshold, a fault is determined to have occurred.

[0140] Wherein, the gas reference flow rate 610 refers to the gas volumetric flow rate under standard conditions obtained by the gas company's management platform based on the gas density measured by sampling and analysis of the gas flowing through the gas gate station and / or pressure regulating station in real time. In some embodiments, the gas company's management platform can query a second preset table based on the gas pressure in the sensor data to obtain the gas density at the corresponding gas pressure. The second preset table is a table that includes the correspondence between gas pressure and gas density. In some embodiments, the second preset table can be constructed based on the gas density collected and recorded at different gas pressures.

[0141] In some embodiments, the gas company management platform can obtain the gas reference flow rate based on gas density and gas volumetric flow rate using a second transformation algorithm. The second transformation algorithm can be expressed as formula (4):

[0142] (4)

[0143] Where e is the weighting coefficient; standard gas density refers to the gas density collected under standard conditions (such as standard atmospheric pressure, 25°C).

[0144] In some embodiments, the weighting coefficient e can be obtained by querying a third preset table based on the average gas impurity content and gas density. The average gas impurity content can be obtained based on gas delivery information. For example, the gas company management platform can obtain gas impurity content data from multiple time points collected within the current period from the gas delivery information, and perform an arithmetic average or weighted average calculation based on the gas impurity content data from multiple time points to determine the average gas impurity content.

[0145] In some embodiments, the third preset table refers to a table that includes the correspondence between the gas impurity content and the weighting coefficient e. In some embodiments, the third preset table can be constructed based on the weighting coefficient e actually recorded under different gas impurity contents. For example, gas with different impurity contents under different conditions can be recorded. The gas volume flow rate and the standard density and reference flow rate of gas under standard conditions are calculated. Based on these data, different weight coefficients e are solved in reverse by formula (4) as the corresponding weight coefficients e for different gas impurity contents, so as to construct the third preset table.

[0146] For more information on gas volumetric flow rate and gas delivery, please refer to [link / reference]. Figure 2 And its related descriptions.

[0147] The troubleshooting instruction 650 is a control command issued by the gas company's management platform when it determines that a device malfunction has occurred. It triggers self-checks on the auxiliary equipment of the gas pipeline to identify the specific faulty device. For example, if the fault determination for a gas pipeline containing a gas metering device indicates a malfunction, the gas company's management platform can issue troubleshooting instruction 650, requiring all auxiliary equipment (such as pressure regulators and valves) on that gas pipeline to perform self-checks to determine which specific device has the problem.

[0148] Auxiliary equipment for gas transmission pipelines refers to various hardware devices installed on gas transmission pipelines for monitoring, regulating, and protecting gas flow. In some embodiments, auxiliary equipment for gas transmission pipelines may include pressure regulators, gas temperature control devices, and cold energy recovery and utilization devices, etc.

[0149] In some embodiments, the gas company management platform can issue a troubleshooting instruction 650 to the gas equipment object platform to instruct the gas equipment object platform to initiate a self-test procedure for the auxiliary equipment of the gas pipeline that has a fault. For example, the gas equipment object platform can initiate a self-test procedure for the pressure regulator to check whether the pressure regulating parameters of the pressure regulator are correct.

[0150] In some embodiments, when the gas company's management platform can determine that a fault has occurred in the gas pipeline where the current gas metering device is located, the auxiliary equipment corresponding to that gas pipeline may be faulty.

[0151] In some embodiments of this specification, the gas company management platform can effectively determine whether there may be equipment failures in the auxiliary facilities of the relevant gas transmission pipeline based on the difference between the gas reference flow rate and the gas standard flow rate, thereby enabling targeted fault self-inspection and troubleshooting to improve the reliability of gas transmission.

[0152] In some embodiments, the gas company management platform can generate a fault judgment result 630 based on the gas reference flow rate 610, the gas standard flow rate 620, and the transformation confidence level 640 of the transformation model; in response to the fault judgment result 630 indicating that there is a equipment fault, the platform determines the fault type 660 of the faulty equipment based on the gas reference flow rate 610, the gas standard flow rate 620, and the transformation confidence level 640 of the transformation model.

[0153] In some embodiments, the gas company management platform can obtain a product by multiplying the absolute value of the difference between the gas metering device and the transformation confidence level 640 of the transformation model, and determine that there is an equipment fault in the gas transmission pipeline where the gas metering device is located when the product is greater than the first difference threshold.

[0154] For more information on gas reference flow rate and gas standard flow rate, please refer to [link / reference]. Figure 2 And related descriptions. For more information on the first difference threshold and the absolute value of the difference in gas metering devices, please refer to the aforementioned content. For more information on the transformation confidence of the transformation model, please refer to... Figure 4 And its related descriptions.

[0155] Fault type 660 refers to the type of each category in the classification results obtained by classifying equipment faults based on specific abnormal situations.

[0156] In some embodiments, the gas company management platform can query a fault table to determine the fault type 660 based on the absolute value of the difference between the gas metering devices.

[0157] In some embodiments, the fault table includes a combination of data consisting of the absolute value of the difference, the transformation confidence level 640 of the transformation model, the numbers of the gas transmission pipeline and the gas metering device (the numbers reflect the location and type), and the fault investigation time, as well as the fault type corresponding to the data combination (a set of data combinations may correspond to one or more fault types).

[0158] In some embodiments, the gas company management platform extracts all past equipment failures from historical gas delivery data, records the gas baseline flow rate 610, gas standard flow rate 620, transformation confidence level 640 of the transformation model, pipeline number, metering device number, and failure investigation time at the time of each failure to construct a data combination, and classifies the failure data according to different failure types, storing the classification results and their corresponding data combinations in a database or table to construct the failure table. The transformation confidence level recorded for each failure can be set as the probability of occurrence of the corresponding failure type.

[0159] In some embodiments of this specification, the gas company management platform may consider using the transformation confidence of the transformation model for equipment fault diagnosis. This can improve the accuracy of the diagnosis, reduce the impact and interference caused by the error in the output of the transformation model, avoid misdiagnosis, and reasonably determine the fault type can clarify the focus of fault self-inspection and avoid delaying fault repair time.

[0160] In some embodiments, the gas company management platform may adjust the activation frequency of the transformation model and / or the data transmission volume of the gas metering device based on the difference between the gas reference flow rate 610 and the gas standard flow rate 620.

[0161] The difference refers to the absolute value of the difference between the gas reference flow rate 610 and the gas standard flow rate 620 corresponding to the gas metering device.

[0162] The data transmission volume of a gas metering device refers to the amount of data transmitted from the gas metering device to the gas equipment object platform via a network per unit time. In some embodiments, the gas company's management platform can record the amount of data transmitted per unit time to determine the data transmission volume.

[0163] In some embodiments, the gas company management platform can set the frequency of model activation to be lower if the difference between the gas reference flow rate 610 and the gas standard flow rate 620 corresponding to the gas metering device is larger. In some embodiments, the gas company management platform can determine the adjusted data transmission volume of the gas metering device based on the data transmission volume before adjustment and the difference between the gas reference flow rate 610 and the gas standard flow rate 620 corresponding to the gas metering device, and instruct the gas equipment object platform to adjust the data acquisition frequency and / or data upload frequency of the gas metering device based on the adjusted data transmission volume, thereby adjusting the data transmission volume of the gas metering device. For example, the gas company management platform determines the adjusted data transmission volume of the gas metering device according to the following formula:

[0164]

[0165] in, This is the adjusted data transmission volume. This represents the data transmission volume before adjustment. This is the difference between the gas reference flow rate of 610 and the gas standard flow rate of 620 corresponding to the gas metering device. The reference flow rate for the gas metering device is 610.

[0166] In some embodiments of this specification, the gas company management platform may consider the actual standard gas flow rate to adjust the activation frequency of the transformation model and / or the data transmission volume of the gas metering device. Adjusting and reducing the activation frequency of the transformation model can reduce unnecessary computational burden and avoid wasting system resources. By adjusting the data transmission volume of the gas metering device, network load can be reduced while ensuring critical data transmission, thereby improving the system's response speed and stability.

[0167] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0168] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0169] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0170] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0171] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0172] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0173] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and are considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A smart gas flow monitoring method based on the Internet of Things, characterized in that, The method is implemented by an IoT-based smart gas flow monitoring system, which includes a gas user platform, a gas service platform, a gas company management platform, a gas company sensor network platform, and a gas equipment object platform. The gas company management platform is configured on one or more sets of company servers; The method is executed by the gas company's management platform, and the method includes: Based on the sensor data uploaded by the pipeline sensor and the historical sampling data uploaded by the gas gate station and / or pressure regulating station, gas transportation information with time tags is generated and stored in the company's server; the gas transportation information is the gas physical state data collected and recorded based on time changes during the gas transportation process, including gas transportation pressure, gas composition data, gas impurity content and corresponding time tags. Obtain the gas volume flow rate collected by gas metering devices deployed on gas transmission pipelines at multiple time points within the current period; Based on the gas equipment object platform, the upstream and downstream difference information of the gas transmission pipeline in the current time period is obtained; the upstream and downstream difference information is information reflecting the difference between the upstream gas pipeline and the adjacent downstream gas pipeline, including the difference in the thickness of the pipe attachment and the difference in the inner diameter of the pipe between the upstream gas pipeline and the adjacent downstream gas pipeline. Based on the gas delivery information from multiple sampling time points within the current time period, the gas characteristic changes of the gas delivery pipeline during the current time period are generated; Based on the upstream and downstream difference information and the changes in gas characteristics during the current time period, as well as the gas volume flow rate at the multiple sampling time points, the standard gas flow rate at the multiple sampling time points is generated; the standard gas flow rate is the volume flow rate of gas passing through the cross-section of the conveying pipeline per unit time under standard conditions. Based on the standard flow rate of the gas at the multiple sampling time points, the sampling time interval of the gas transmission pipeline is adjusted. Based on the standard gas flow rate, the cumulative gas flow rate of the gas transmission pipeline during the current time period is generated; Based on the cumulative gas flow rate, the opening and closing status and / or opening range of the regulating valve of the gas delivery pipeline are controlled to determine the gas flow rate entering the gas generating device.

2. The method according to claim 1, characterized in that, The step of generating the standard flow rate of the gas at the multiple sampling time points based on the upstream and downstream difference information and the gas characteristic changes during the current time period, as well as the gas volume flow rate at the multiple sampling time points, includes: Based on historical metering data from different gas transmission pipelines, a transformation database is generated and set up on the company's server. The transformation database includes multiple upstream and downstream difference information, gas transmission information, gas characteristic changes, and corresponding first transformation algorithms. The first transformation algorithm is a related algorithm for gas metering. The independent variables in the first transformation algorithm are the gas volume flow rate and gas standard flow rate corresponding to the upstream and downstream difference information, the gas transmission information, and the gas characteristic changes. The dependent variable is a combination of weighting coefficients corresponding to the upstream and downstream difference information, the gas transmission information, and the gas characteristic changes, respectively. Based on the upstream and downstream difference information, the gas characteristic changes, and the gas transmission information during the current time period, the target first transformation algorithm is determined by searching the transformation database; Based on the gas volume flow rate, the gas standard flow rate is generated through the target first transformation algorithm.

3. The method according to claim 1, characterized in that, The step of generating the standard flow rate of the gas at the multiple sampling time points based on the upstream and downstream difference information and the gas characteristic changes during the current time period, as well as the gas volume flow rate at the multiple sampling time points, includes: Based on historical metering data from different gas transmission pipelines, a transformation model is generated and set in the company's server; the transformation model is a machine learning model. Based on the upstream and downstream difference information of the current time period, the gas delivery information of the multiple sampling time points, and the gas volume flow rate, the standard gas flow rate of the multiple sampling time points is generated through the transformation model; A sampling instruction is generated and sent to a sampling device deployed around the gas metering device to obtain gas sampling data around the gas metering device.

4. The method according to claim 1, characterized in that, The method further includes: Based on the gas reference flow rate and the gas standard flow rate corresponding to the gas metering device, a fault judgment result is generated for the gas transmission pipeline where the gas metering device is located. In response to the fault determination result indicating the presence of equipment fault, a troubleshooting command is issued to control the auxiliary equipment of the gas transmission pipeline to perform equipment self-checks in order to confirm the faulty equipment.

5. A smart gas flow monitoring system based on the Internet of Things, characterized in that, The IoT-based smart gas flow monitoring system includes a gas user platform, a gas service platform, a gas company management platform, a gas company sensor network platform, and a gas equipment object platform. The gas company management platform is configured on one or more sets of company servers; The gas company management platform is configured as follows: Based on the sensor data uploaded by the pipeline sensor and the historical sampling data uploaded by the gas gate station and / or pressure regulating station, gas transportation information with time tags is generated and stored in the company's server; the gas transportation information is the gas physical state data collected and recorded based on time changes during the gas transportation process, including gas transportation pressure, gas composition data, gas impurity content and corresponding time tags. Obtain the gas volume flow rate collected by gas metering devices deployed on gas transmission pipelines at multiple time points within the current period; Based on the gas equipment object platform, the upstream and downstream difference information of the gas transmission pipeline in the current time period is obtained; the upstream and downstream difference information is information reflecting the difference between the upstream gas pipeline and the adjacent downstream gas pipeline, including the difference in the thickness of the pipe attachment and the difference in the inner diameter of the pipe between the upstream gas pipeline and the adjacent downstream gas pipeline. Based on the gas delivery information from multiple sampling time points within the current time period, the gas characteristic changes of the gas delivery pipeline during the current time period are generated; Based on the upstream and downstream difference information and the gas characteristic changes in the current time period, as well as the gas volume flow rate at the multiple sampling time points, the standard gas flow rate at the multiple sampling time points is generated; The standard flow rate of the gas is the volumetric flow rate of the gas passing through the cross-section of the pipeline per unit time under standard conditions; Based on the standard flow rate of the gas at the multiple sampling time points, the sampling time interval of the gas transmission pipeline is adjusted. Based on the standard gas flow rate, the cumulative gas flow rate of the gas transmission pipeline during the current time period is generated; Based on the cumulative gas flow rate, the opening and closing status and / or opening range of the regulating valve of the gas delivery pipeline are controlled to determine the gas flow rate entering the gas generating device.

6. The system according to claim 5, characterized in that, The gas company management platform is further configured as follows: Based on historical metering data from different gas transmission pipelines, a transformation database is generated and set up on the company's server. The transformation database includes multiple upstream and downstream difference information, gas transmission information, gas characteristic changes, and corresponding first transformation algorithms. The first transformation algorithm is a related algorithm for gas metering. The independent variables in the first transformation algorithm are the gas volume flow rate and gas standard flow rate corresponding to the upstream and downstream difference information, the gas transmission information, and the gas characteristic changes. The dependent variable is a combination of weighting coefficients corresponding to the upstream and downstream difference information, the gas transmission information, and the gas characteristic changes, respectively. Based on the upstream and downstream difference information, the gas characteristic changes, and the gas transmission information during the current time period, the target first transformation algorithm is determined by searching the transformation database; Based on the gas volume flow rate, the gas standard flow rate is generated through the target first transformation algorithm.

7. The system according to claim 5, characterized in that, The gas company management platform is further configured as follows: Based on historical metering data from different gas transmission pipelines, a transformation model is generated and set in the company's server; the transformation model is a machine learning model. Based on the upstream and downstream difference information of the current time period, the gas delivery information of the multiple sampling time points, and the gas volume flow rate, the standard gas flow rate of the multiple sampling time points is generated through the transformation model; A sampling instruction is generated and sent to a sampling device deployed around the gas metering device to obtain gas sampling data around the gas metering device.

8. The system according to claim 5, characterized in that, The gas company management platform is further configured as follows: Based on the gas reference flow rate and the gas standard flow rate corresponding to the gas metering device, a fault judgment result is generated for the gas transmission pipeline where the gas metering device is located. In response to the fault determination result indicating the presence of equipment fault, a troubleshooting command is issued to control the auxiliary equipment of the gas transmission pipeline to perform equipment self-checks in order to confirm the faulty equipment.

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