Energy-saving early warning management method and internet of things system for intelligent gas flowmeter

Abstract

This specification provides an energy-saving early warning management method and an Internet of Things (IoT) system for smart gas flow meters. The energy-saving early warning management method based on a smart gas flow meter is executed by a smart gas management platform and includes: acquiring the remaining power of the smart gas flow meter; determining the confidence level of the remaining power based at least on environmental data; calibrating the remaining power based on the confidence level to determine the calibrated power; determining whether to issue a power consumption warning to the gas user based on the calibrated power, smart gas flow meter operating data, and at least one reference correspondence, wherein the reference correspondence represents the relationship between the smart gas flow meter's behavior and power consumption; and generating a smart gas flow meter operating plan in response to issuing a power consumption warning to the gas user.

Description

Technical Field

[0001] This specification relates to the field of energy saving in intelligent gas flow meters, and in particular to an energy-saving early warning management method and Internet of Things system for intelligent gas flow meters. Background Technology

[0002] Intelligent gas flow meters are devices used to measure the flow rate of gas in pipelines and are widely used in gas metering management of urban gas pipeline networks. Types of intelligent gas flow meters include ultrasonic gas flow meters, turbine gas flow meters, Roots flow meters, intelligent diaphragm gas meters, and ultrasonic gas meters, with most being battery-powered. However, when the battery power is low, the internal battery voltage of an intelligent gas flow meter decreases, causing the gas valve to automatically close and stop gas output, inconveniencing users. Replacing the batteries promptly will allow the intelligent gas flow meter to maintain gas supply and avoid inconvenience for gas users.

[0003] Regarding energy-saving management of intelligent gas flow meters, CN114526779A discloses a gas meter with lithium battery power monitoring function. This prior art monitors the lithium battery power through a lithium battery power monitoring circuit in the gas meter. However, this prior art only considers energy saving through monitoring the lithium battery power and does not provide energy-saving early warning management for intelligent gas flow meters, including gas meters, from a data processing perspective.

[0004] Therefore, it is hoped that an energy-saving early warning management method and Internet of Things system for smart gas flow meters can be proposed, which can effectively and accurately detect the power of smart gas flow meters, provide timely and reliable early warning of the power of smart gas flow meters, reduce the situation where the battery of smart gas flow meters is abandoned when there is effective power due to inaccurate power judgment of smart gas flow meters, and achieve the purpose of energy saving of smart gas flow meters. Summary of the Invention

[0005] This specification provides one or more embodiments of an energy-saving early warning management method for a smart gas flow meter. The method is executed by a smart gas management platform and includes: acquiring the remaining power of the smart gas flow meter; determining a confidence level of the remaining power based at least on environmental data; calibrating the remaining power based on the confidence level to determine a calibration power level; determining whether to issue a power warning to the gas user based on the calibration power level, the smart gas flow meter's operating data, and at least one reference correspondence, wherein the reference correspondence represents the relationship between the smart gas flow meter's operating behavior and power consumption; and generating an energy-saving management operation plan in response to issuing the power warning to the gas user.

[0006] This specification provides one or more embodiments of an energy-saving early warning management IoT system for smart gas flow meters. The system includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially. The smart gas management platform is configured to: acquire the remaining power of the smart gas flow meter; determine the confidence level of the remaining power based at least on environmental data; calibrate the remaining power based on the confidence level to determine the calibration power; determine whether to issue a power warning to the gas user based on the calibration power, the smart gas flow meter's operating data, and at least one reference correspondence, wherein the reference correspondence represents the relationship between the smart gas flow meter's operating behavior and power consumption; and generate an energy-saving management operation plan in response to issuing a power warning to the gas user.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes an energy-saving early warning management method for an intelligent gas flow meter.

[0008] Some embodiments of this specification achieve at least the following technical effects: by acquiring the remaining power of the intelligent gas flow meter, determining the confidence level of the remaining power based on environmental data, calibrating the remaining power, determining the calibration power, and using the calibration power, the operating data of the intelligent gas flow meter, and at least one reference correspondence, determining whether to issue a power warning to the gas user, and in response to issuing the power warning, generating an energy-saving management operation plan. This can effectively and accurately detect the power of the intelligent gas flow meter, provide timely and reliable power warnings for the intelligent gas flow meter, generate a suitable energy-saving management operation plan, and effectively achieve energy saving by the intelligent gas flow meter. Attached Figure Description

[0009] 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:

[0010] Figure 1 This is an exemplary platform structure diagram of an energy-saving early warning management Internet of Things system for smart gas flow meters, shown according to some embodiments of this specification;

[0011] Figure 2 This is an exemplary flowchart of an energy-saving early warning management method for a smart gas flow meter, according to some embodiments of this specification;

[0012] Figure 3 This is an exemplary schematic diagram illustrating the determination of whether to issue a power consumption warning to a gas user, based on some embodiments of this specification;

[0013] Figure 4 This is an exemplary schematic diagram illustrating the generation of an intelligent gas flow meter operation scheme according to some embodiments of this specification. Detailed Implementation

[0014] 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.

[0015] 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.

[0016] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," 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 expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0017] 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.

[0018] Figure 1 This is an exemplary platform structure diagram of an energy-saving early warning management IoT system for smart gas flow meters, shown according to some embodiments of this specification. The following will provide a detailed description of the energy-saving early warning management IoT system for smart gas flow meters involved in the embodiments of this specification. It should be noted that the following embodiments are only for explaining this specification and do not constitute a limitation thereof.

[0019] In some embodiments, the energy-saving early warning management Internet of Things system 100 for smart gas flow meters (hereinafter referred to as the energy-saving Internet of Things system 100) may include a smart gas user platform 110, a smart gas service platform 120, a smart gas management platform 130, a smart gas sensor network platform 140, and a smart gas object platform 150.

[0020] The smart gas user platform 110 is a platform for interacting with users. In some embodiments, the smart gas user platform 110 can be configured as a terminal device.

[0021] In some embodiments, the smart gas user platform 110 may include a gas user sub-platform 111 and a regulatory user sub-platform 112.

[0022] The Gas User Sub-Platform 111 is a platform that provides gas users with data related to gas usage and solutions to gas-related problems. Gas users include industrial gas users, commercial gas users, and general gas users.

[0023] The monitoring user sub-platform 112 is a platform for monitoring users to supervise the operation of the entire energy-saving IoT system 100. Monitoring users include personnel from the safety management department, etc.

[0024] In some embodiments, the smart gas user platform 110 can send query commands for gas equipment parameter information (e.g., the remaining power of the smart gas flow meter) to the smart gas management platform 130 via the smart gas service platform 120, and receive gas equipment management schemes (e.g., energy-saving management operation schemes) uploaded by the smart gas service platform 120.

[0025] The intelligent gas service platform 120 is used to transmit user needs and control information. The intelligent gas service platform 120 can obtain gas equipment management information from the intelligent gas management platform 130 and upload it to the intelligent gas user platform 110.

[0026] In some embodiments, the smart gas service platform 120 may include a smart gas consumption service sub-platform 121 and a smart supervision service sub-platform 122.

[0027] The Smart Gas Service Sub-Platform 121 is a platform that provides gas services to gas users.

[0028] The Smart Supervision Service Sub-Platform 122 is a platform that provides supervision services to users who need supervision services.

[0029] In some embodiments, the smart gas service platform 120 can send the gas equipment management plan to the regulatory user sub-platform 112 based on the smart regulatory service sub-platform 122.

[0030] The Smart Gas Management Platform 130 is a platform used to monitor and manage the safety of various devices and related information, and it aggregates all the data from the Internet of Things (IoT) to provide safety management and information management functions for the IoT operating system.

[0031] In some embodiments, the smart gas management platform 130 may include a smart gas safety management sub-platform 131 and a smart gas data center 132.

[0032] The intelligent gas safety management sub-platform 131 is a platform for processing safety-related information concerning target platform equipment. In some embodiments, the intelligent gas safety management sub-platform 131 includes four safety monitoring and management modules: intrinsic safety monitoring and management, information security monitoring and management, functional safety monitoring and management, and safety inspection management. The intelligent gas safety management sub-platform 131 can analyze and process safety-related information concerning target platform equipment through the aforementioned modules.

[0033] The smart gas data center 132 can be used to store and manage all operational information of the energy-saving IoT system 100. In some embodiments, the smart gas data center 132 can be configured as a storage device for storing gas equipment-related data, etc.

[0034] In some embodiments, intrinsic safety monitoring and management includes monitoring explosion-proof safety aspects such as mechanical leakage, electrical power consumption (e.g., intelligent control power consumption, communication power consumption, etc.), and valve control. Information security monitoring and management includes monitoring for data anomalies, unauthorized device information, and unauthorized access. Functional monitoring and management includes functional safety monitoring for conditions such as prolonged inactivity, continuous flow timeouts, flow overload, abnormally high flow rates, abnormally low flow rates, low gas pressure, strong magnetic interference, and low voltage. In some embodiments, the smart gas data center can automatically send relevant safety data to the corresponding safety monitoring and management module by identifying safety parameter categories. Each safety monitoring and management module has preset corresponding safety monitoring thresholds. When relevant safety data exceeds the corresponding safety monitoring threshold, the smart gas data center can automatically trigger an alarm on the smart gas management platform and can optionally automatically push the alarm information to gas users and regulatory users.

[0035] In some embodiments, the smart gas management platform 130 can interact with the smart gas service platform 120 and the smart gas sensor network platform 140 through the smart gas data center 132. For example, the smart gas data center 132 can send gas equipment management plans to the smart gas service platform 120. As another example, the smart gas data center can send a query command for gas equipment parameter information to the smart gas sensor network platform 140 to obtain the gas equipment parameter information.

[0036] The intelligent gas sensor network platform 140 can be a functional platform for network management of sensor communication. In some embodiments, the intelligent gas sensor network platform 140 can be configured as a communication network and gateway to realize functions such as network management, protocol management, command management, and data parsing. In some embodiments, the intelligent gas sensor network platform 140 can interact with the intelligent gas management platform 130 and the intelligent gas object platform 150. For example, the intelligent gas sensor network platform 140 can receive gas equipment parameter information uploaded by the intelligent gas object platform 150, and / or send instructions to obtain gas equipment parameter information to the intelligent gas object platform 150.

[0037] The intelligent gas object platform 150 can be a functional platform for generating sensing information and executing control information. In some embodiments, the intelligent gas object platform 150 can be configured as various gas metering and management devices. These devices may include ultrasonic gas flow meters, turbine gas flow meters, Roots flow meters, smart diaphragm gas meters, ultrasonic gas meters, gas valves, etc.

[0038] In some embodiments, the smart gas object platform 150 can interact with the smart gas equipment sensor network platform 140 to receive instructions from the smart gas equipment sensor network platform 140 to obtain gas equipment parameter information, and can also upload gas equipment parameter information to the smart gas equipment sensor network platform 140.

[0039] Some embodiments in this specification, based on the energy-saving Internet of Things system 100, can form an information operation closed loop between the smart gas object platform and the smart gas user platform, and operate in a coordinated and regular manner under the unified management of the smart gas management platform, thereby realizing the informatization and intelligentization of energy-saving management of smart gas flow meters.

[0040] Figure 2 This is an exemplary flowchart illustrating an energy-saving early warning management method for a smart gas flow meter, 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 smart gas management platform.

[0041] Step 210: Obtain the remaining power of the smart gas flow meter.

[0042] In some embodiments, the remaining power of the smart gas flow meter can be directly determined by the power acquisition module of the smart gas flow meter. The power acquisition module is used to acquire the power data of the smart gas flow meter. For example, the power data of the smart gas flow meter may include the power used by the smart gas flow meter, the remaining power of the smart gas flow meter, etc.

[0043] Step 220: Determine the confidence level of the remaining battery power based at least on environmental data.

[0044] Environmental data refers to relevant data about the environment surrounding the smart gas flow meter. For example, environmental data can include temperature data, humidity data, and light intensity data.

[0045] In some embodiments, environmental data can be acquired through sensor detection. Sensor types include, but are not limited to, temperature sensors, humidity sensors, and light intensity sensors.

[0046] The confidence level of the remaining battery power is used to measure how reliable the remaining battery power is. The higher the confidence level, the closer the obtained remaining battery power is to the actual remaining battery power, and the more reliable the obtained remaining battery power is.

[0047] Understandably, environmental data can affect the operational quality of various components in a smart gas flow meter, and may even cause malfunctions, resulting in inaccurate readings of remaining battery power. In some embodiments, the smart gas management platform can determine the confidence level of the remaining battery power using environmental data and a confidence level lookup table. The confidence level lookup table records the confidence levels of the remaining battery power corresponding to different environmental data. This table can be preset based on prior knowledge or historical data. For example, when the temperature, humidity, and light intensity of the environmental data are too high, the probability of battery leakage in the smart gas flow meter increases, potentially leading to inaccurate readings of the remaining battery power. Accordingly, the confidence level for the remaining battery power corresponding to such environmental data can be set to a lower value.

[0048] In some embodiments, the smart gas management platform can process environmental data and historical usage data of smart gas flow meters based on a confidence level determination model to determine the confidence level.

[0049] In some embodiments, the confidence determination model is a machine learning model. In some embodiments, the confidence determination model may include any one or a combination of various feasible models such as a recurrent neural network (RNN) model, a deep neural network (DNN) model, and a convolutional neural network (CNN) model.

[0050] In some embodiments, the input to the confidence determination model may include environmental data and historical usage data of the smart gas flow meter, and the output may be the confidence level of the remaining battery power.

[0051] Historical usage data for smart gas flow meters refers to the data collected during the meter's historical usage. For example, historical usage data may include the meter's lifespan and the number of times it has undergone maintenance.

[0052] The parameters of the confidence determination model can be obtained through training. In some embodiments, the confidence determination model can be trained using multiple first training samples with a first label. For example, multiple first training samples with a first label can be input into the initial confidence determination model, and a loss function can be constructed using the first label and the results of the initial confidence determination model. The parameters of the initial confidence determination model are then iteratively updated based on the loss function. When the loss function of the initial confidence determination model satisfies a preset condition, the model training is complete, and the trained confidence determination model is obtained. The preset condition can be loss function convergence, the number of iterations reaching a threshold, etc.

[0053] In some embodiments, the first training sample may include sample environmental data of the sample smart gas flow meter and historical usage data of the sample smart gas flow meter. The first label may be the confidence level of the sample smart gas flow meter's remaining power. The sample remaining power refers to the acquired remaining power of the sample smart gas flow meter.

[0054] In some embodiments, the first training sample and the first label can be obtained based on historical data. In some embodiments, the smart gas management platform can determine the historical environmental data and historical usage data of normal smart gas flow meters as positive samples in the first training sample, and determine the historical environmental data and historical usage data of abnormal smart gas flow meters as negative samples in the first training sample. For example, the first label of a positive sample is 1, indicating that the obtained remaining power is the same as or close to the actual remaining power. The first label of a negative sample is a non-1 value, indicating that the obtained remaining power is different from or not close to the actual remaining power.

[0055] Among them, a normal smart gas flow meter is a smart gas flow meter that has not experienced any abnormalities, and its actual remaining power is the same as or close to the remaining power obtained by the power acquisition module. An abnormal smart gas flow meter is a smart gas flow meter that has experienced abnormalities, and its actual remaining power is different from or not close to the remaining power obtained by the power acquisition module.

[0056] In some embodiments, the types of abnormal situations include, but are not limited to, battery abnormalities and reading abnormalities (e.g., performance degradation of the power acquisition module causing the acquired remaining power to be different from or not close to the actual remaining power).

[0057] In some embodiments, the smart gas management platform can determine the confidence level of an abnormal smart gas flow meter based on historical anomaly data from multiple abnormal smart gas flow meters. Abnormal smart gas flow meters with the same anomaly type have the same confidence level. The confidence level corresponding to an abnormal smart gas flow meter can represent the degree of closeness between the obtained remaining power of the abnormal smart gas flow meter and its actual remaining power.

[0058] In some embodiments, historical anomaly data includes the anomaly type, the time point of the anomaly, and the remaining power consumption obtained at multiple time points before and after it. Historical anomaly data can be obtained based on an energy-saving IoT system.

[0059] In some embodiments, the smart gas management platform can acquire a large number of historical abnormal datasets of abnormal smart gas flow meters based on an energy-saving Internet of Things system; determine the impact of at least one type of abnormality based on the historical abnormal dataset; and determine the confidence level of at least one type of abnormal smart gas flow meter based on the impact of at least one type of abnormality.

[0060] The historical anomaly dataset includes historical anomaly data from multiple abnormal smart gas flow meters.

[0061] In some embodiments, the smart gas management platform can classify historical anomaly datasets according to anomaly types to obtain at least one subset of historical anomaly data corresponding to anomaly type; based on the subset of historical anomaly data corresponding to each of the at least one anomaly type, the influence degree of each anomaly type is determined. For example, for any subset of historical anomaly data corresponding to an anomaly type, the smart gas management platform can calculate the standard deviation of the historical remaining power obtained by each abnormal smart gas flow meter at multiple time points before and after the abnormal time point, and determine the mean of the standard deviation of the historical remaining power of all abnormal smart gas flow meters in an anomaly type as the influence degree of that anomaly type. In this embodiment, the mean of the standard deviation of the historical remaining power can reflect the fluctuation degree of the historical remaining power obtained under an anomaly type; the larger the mean of the standard deviation of the historical remaining power, the greater the fluctuation degree.

[0062] In some embodiments, the intelligent gas management platform can determine the confidence level of an abnormal intelligent gas flow meter by looking up a confidence level lookup table and the anomaly type of the abnormal intelligent gas flow meter, and then determine the first label when using the relevant data of the abnormal intelligent gas flow meter as the first training sample. The confidence level lookup table includes the correspondence between the impact degree of different anomaly types and the confidence level of abnormal intelligent gas flow meters for different anomaly types. The confidence level lookup table can be pre-prepared based on historical data or prior knowledge.

[0063] In some embodiments of this specification, environmental data and historical usage data of the smart gas flow meter are processed based on a confidence level determination model to determine the confidence level. This can utilize the self-learning capability of the machine learning model to find patterns from a large number of historical abnormal datasets, obtain the correlation between the environmental data, historical usage data and the confidence level of the remaining power of the smart gas flow meter, and improve the accuracy and efficiency of determining the confidence level.

[0064] Step 230: Based on the confidence level, calibrate the remaining power and determine the calibration power.

[0065] Calibration power refers to the remaining power of the smart gas flow meter after calibration.

[0066] In some embodiments, the smart gas management platform can determine the adjustment amount based on confidence level; and determine the calibration amount based on the remaining power of the smart gas flow meter and the adjustment amount. For example, the calibration amount can be equal to the sum of the remaining power and the adjustment amount.

[0067] In some embodiments, the smart gas management platform can pre-set a correspondence between confidence level and adjustment amount, and determine the adjustment amount based on the confidence level of the remaining power of the smart gas flow meter and this correspondence. For example, the correspondence between confidence level and adjustment amount could be that the higher the confidence level, the smaller the adjustment amount.

[0068] In some embodiments, the smart gas management platform can also determine whether to calibrate the remaining power capacity based on a confidence level. For example, when the confidence level is lower than a confidence threshold, it is determined that the remaining power capacity should be calibrated. The confidence threshold can be a system-preset or manually preset value.

[0069] Step 240: Based on the calibration power, the operating data of the smart gas flow meter, and at least one reference correspondence, determine whether to issue a power warning to the gas user.

[0070] The operating data of a smart gas flow meter refers to data related to the operation of the smart gas flow meter. In some embodiments, the operating data of a smart gas flow meter includes at least one operating behavior that has occurred from the time the smart gas flow meter's battery is fully charged until the present, as well as the amount of data and / or the duration of the behavior.

[0071] Operational behavior refers to the actions of a smart gas flow meter during operation. In some embodiments, operational behavior may include sending data, receiving data, and maintaining the operation of the smart gas flow meter. Maintaining the operation of the smart gas flow meter means not receiving or sending data, but only maintaining the basic operation of the smart gas flow meter.

[0072] Because smart gas flow meters need to operate different components due to the fixed amount or type of data they receive or transmit, the operating frequency and duration of these components may also differ, resulting in varying battery consumption. Accordingly, in some embodiments, the operating behavior of different types of smart gas flow meters can be categorized based on the types of data sent and / or received.

[0073] In some embodiments, data types can be preset. In some embodiments, data types can be categorized based on the purpose of the data. For example, data types can be fault data, usage data, maintenance data, environmental monitoring data, etc.

[0074] In some embodiments, behavioral data volume refers to the amount of data transmitted and / or received during the operation of the smart gas flow meter.

[0075] In some embodiments, the smart gas management platform can interact with the smart gas object platform through the smart gas sensor network platform to obtain the operating data of the smart gas flow meter.

[0076] In some embodiments, the reference correspondence represents the relationship between the operating behavior of the smart gas flow meter and its power consumption. The power consumption for different operating behaviors may be the same or different.

[0077] In some embodiments, the reference correspondence represents the relationship between the operating behavior of the smart gas flow meter and its power consumption per unit time. The power consumption per unit time may be the same or different for different operating behaviors.

[0078] In some embodiments, at least one reference correspondence can be obtained based on prior knowledge or historical data.

[0079] In some embodiments, the smart gas management platform may preset at least one reference correspondence based on the historical total operating data of the smart gas flow meter.

[0080] Historical total operating data of smart gas flow meters refers to at least one operating behavior, its data volume, and duration, that occurred from the time the battery of a smart gas flow meter for a large number of gas users was fully charged until a low battery warning was issued.

[0081] In some embodiments, the smart gas management platform can obtain historical total operating data of smart gas flow meters through an energy-saving early warning management IoT system.

[0082] In some embodiments, the intelligent gas management platform can perform statistical analysis on the historical total operating data of the intelligent gas flow meter to obtain the power consumption (or power consumption per unit time) corresponding to at least one operating behavior and obtain at least one reference preset relationship.

[0083] In some embodiments, the smart gas management platform can determine the power consumption per unit time required to maintain the operation of the smart gas flow meter based on the historical total operating data of the smart gas flow meter; and determine the power consumption per unit of remaining operating behavior based on experimental tests.

[0084] In some embodiments, the smart gas management platform can filter historical operating data that meets preset conditions from the total historical operating data. Based on the full charge level, charge threshold, and time taken for the smart gas flow meter battery to drop from full charge to the charge threshold in the historical operating data, the power consumption per unit time for maintaining the operation of the smart gas flow meter can be determined. The preset condition can be: filtering historical operating data that only includes the operation of maintaining the smart gas flow meter. For example, the power consumption per unit time for maintaining the operation of the smart gas flow meter can be calculated using the following formula: (Full charge level - Charge threshold) ÷ (T2 - T1), where T2 is the time when the smart gas flow meter issues a charge warning (or reaches the charge threshold), and T1 is the time when the smart gas flow meter starts operating from a fully charged state. Alternatively, a related formula with the same logic can be used for calculation.

[0085] Residual operating behaviors refer to operating behaviors other than maintaining the operation of the smart gas flow meter. For example, residual operating behaviors may include: sending data, receiving data, etc.

[0086] In some embodiments, the intelligent gas management platform can determine the unit power consumption of remaining operating behavior based on experimental tests.

[0087] In some embodiments, the experimental test includes: causing the smart gas flow meter to perform a preset operating behavior based on a preset frequency until the battery voltage of the smart gas flow meter drops from the voltage corresponding to the fully charged state to the preset voltage, and then determining the unit power consumption of the preset operating behavior.

[0088] In some embodiments, the preset operating behavior can be other operating behaviors besides maintaining the operation of the smart gas flow meter.

[0089] The test time is the time it takes for the battery voltage of the intelligent gas flow meter to drop from the voltage corresponding to a fully charged state to a preset voltage.

[0090] In some embodiments, the intelligent gas management platform can construct a conversion relationship between the working voltage and the power of the intelligent gas flow meter battery; determine the simulated remaining power corresponding to the preset voltage based on the conversion relationship; and determine the unit power consumption of the preset operating behavior based on the simulated remaining power.

[0091] For example, if the operating voltage of the smart gas flow meter battery ranges from the highest to the lowest operating voltage, the conversion relationship can be: the lowest operating voltage represents 0% charge, and the highest operating voltage represents 100% charge. For example, the simulated remaining charge corresponding to the preset voltage = [(preset voltage - lowest operating voltage) ÷ (highest operating voltage - lowest operating voltage)] × the full charge of the smart gas flow meter battery.

[0092] In some embodiments, the smart gas management platform can determine the total power consumption of a preset operating behavior based on the simulated remaining power, test time, and power consumption per unit time required to maintain the operation of the smart gas flow meter; and determine the unit power consumption of the preset operating behavior based on the total power consumption of the preset operating behavior and the amount of behavioral data of the preset operating behavior during the test time. Taking operating behavior A as an example, firstly, the total power consumption of operating behavior A is calculated as: simulated remaining power - test time × power consumption per unit time required to maintain the operation of the smart gas flow meter; then, the unit power consumption of operating behavior A is calculated as: total power consumption of operating behavior A ÷ amount of behavioral data of the preset operating behavior during the test time.

[0093] Preset frequency refers to the operating frequency of the pre-set operating behavior.

[0094] The preset frequency, preset operating behavior, and preset voltage can be set manually or by the system.

[0095] In some embodiments, different preset voltages correspond to different preset operating behaviors. In some embodiments, the preset voltage can be determined based on the unit power consumption of the preset operating behavior; for example, the higher the unit power consumption, the higher the preset voltage corresponding to the preset operating behavior.

[0096] By calculating the power consumption per unit time for maintaining the operation of the smart gas flow meter and the power consumption per unit for the remaining operation using different methods, the power consumption of different operation behaviors can be obtained more accurately. Specifically, when calculating the power consumption per unit, the battery voltage drops faster as the power consumption per unit of operation increases. In this case, the reliability of calculating power consumption by voltage ratio may decrease. Therefore, a preset voltage can be appropriately increased to ensure more accurate data.

[0097] A power consumption warning is a notification sent to gas users when power consumption is abnormal. Abnormal power consumption includes situations such as remaining power falling below a remaining power threshold or daily average power consumption exceeding a power consumption threshold. The remaining power threshold and power consumption threshold can be system default values, empirical values, manually preset values, or any combination thereof, and can be set according to actual needs. This manual does not impose any restrictions on these settings.

[0098] Battery level warnings can take the form of light warnings, sound warnings, text message warnings, etc.

[0099] In some embodiments, battery warnings can be categorized by levels. For example, battery warning levels can correspond to different remaining battery ranges.

[0100] In some embodiments, a low battery warning can be used to remind gas users to replace the battery in their smart gas flow meter. In some embodiments, a low battery warning can be used to remind gas users to perform maintenance on the battery in their smart gas flow meter. The low battery warning is merely illustrative and is not intended to be limiting.

[0101] In some embodiments, the smart gas management platform can determine whether to issue a power consumption warning to gas users based on the calibrated power consumption and a power consumption threshold. A power consumption warning is issued to gas users when the calibrated power consumption falls below the power consumption threshold. The power consumption threshold refers to the minimum power consumption value of the smart gas flow meter. The power consumption threshold can be preset by the system or manually. For more information on power consumption thresholds, please refer to [link to relevant documentation]. Figure 3 .

[0102] In some embodiments, the intelligent gas management platform can determine the reference battery operating time of the intelligent gas flow meter, and based on the reference battery operating time and a time threshold, determine whether to issue a power shortage warning to the gas user. For details regarding this embodiment, please refer to... Figure 3 And its related descriptions.

[0103] Step 250: In response to issuing an energy consumption warning to gas users, an energy-saving management operation plan is generated.

[0104] Energy-saving management operation plan refers to the plan related to the operation of intelligent gas flow meter.

[0105] In some embodiments, an energy-saving management operation plan includes one or more operational behaviors. For example, an energy-saving management operation plan may include sending fault data to a smart gas management platform.

[0106] In some embodiments, an energy-saving management operation scheme includes one or more operational behaviors, the amount of data associated with those behaviors, and / or the duration of those behaviors. For example, an energy-saving management operation scheme could involve a smart gas flow meter sending fault data to a smart gas management platform, with the amount of data sent being A megabytes.

[0107] In some embodiments, the smart gas management platform can generate energy-saving management operation plans based on power warning levels. Different power warning levels correspond to different energy-saving management operation plans, and the correspondence can be preset based on prior knowledge or historical data. For example, a level 1 power warning indicates that the smart gas flow meter has the least remaining power, and the generated energy-saving management operation plan could be to maintain the operation of only the smart gas flow meter system; a level 2 power warning indicates that the smart gas flow meter has a small amount of remaining power, and the generated energy-saving management operation plan could be to send a small amount of data, etc.

[0108] In some embodiments, the intelligent gas management platform can determine the importance score of an operational behavior based on at least one reference correspondence and the urgency of the operational behavior; based on the importance score, an energy-saving management operation plan can be generated. For further details on this embodiment, please refer to [link to relevant documentation]. Figure 4 And its related descriptions.

[0109] Some embodiments of this specification obtain the remaining power of the intelligent gas flow meter, determine the confidence level of the remaining power based on environmental data, calibrate the remaining power, determine the calibration power, and use the calibration power, the operating data of the intelligent gas flow meter, and at least one reference correspondence to determine whether to issue a power warning to the gas user. In response to issuing a power warning, an energy-saving management operation plan is generated. This can effectively and accurately detect the power of the intelligent gas flow meter, provide timely and reliable power warnings, generate a suitable energy-saving management operation plan, and effectively achieve energy saving of the intelligent gas flow meter.

[0110] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 200 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0111] Figure 3 This is an exemplary schematic diagram illustrating, according to some embodiments of this specification, a method for determining whether to issue a power consumption warning to a gas user.

[0112] In some embodiments, the smart gas management platform may, at least based on the historical total operating data 310 of the smart gas flow meter, preset at least one reference correspondence 311; determine the battery reference operating time 340 of the smart gas flow meter based on the calibration power 320, the operating data 330 of the smart gas flow meter, and at least one reference correspondence 311; and issue a power warning 360 to the gas user in response to the battery reference operating time 350 being lower than the time threshold 350.

[0113] For details regarding the preset reference correspondence and the operating data of the smart gas flow meter, please refer to [link / reference]. Figure 2 And related explanations.

[0114] Battery reference operating time refers to the operating time of the smart gas flow meter.

[0115] In some embodiments, the smart gas management platform can determine the battery reference operating time corresponding to the calibration power of the smart gas flow meter by looking up a reference time lookup table. The reference time lookup table includes the correspondence between different power levels and different battery reference operating times, and the table can be determined based on prior knowledge or historical data.

[0116] In some embodiments, the smart gas management platform can calculate the average daily power consumption of gas users based on the operating data of smart gas flow meters; and determine the battery reference operating time based on the average daily power consumption, calibration power consumption, and power consumption threshold. For example, battery reference operating time = (calibration power consumption - power consumption threshold) / average daily power consumption of gas users.

[0117] For details regarding calibration power, please see [link / details]. Figure 2 And related explanations.

[0118] Average daily power consumption refers to the average daily power consumption of gas users.

[0119] In some embodiments, the smart gas management platform can determine the power consumption corresponding to each of the multiple operating behaviors included in the smart gas flow meter's operating data based on the smart gas flow meter's operating data and at least one reference correspondence, thereby obtaining the total power consumption of the gas user; and determine the gas user's average daily power consumption based on the gas user's total power consumption and its corresponding number of usage days. For example, when the operating behavior is "maintaining the smart gas flow meter's operation," its corresponding power consumption is the product of the duration of the "maintaining the smart gas flow meter's operation" behavior and the power consumption per unit time. As another example, when the operating behavior is "sending data or receiving data," its corresponding power consumption is the product of the duration of the "maintaining the smart gas flow meter's operation" behavior and the power consumption per unit time.

[0120] The power threshold refers to the minimum power level of the smart gas flow meter. When the remaining power of the smart gas flow meter falls below the power threshold, the smart gas flow meter cannot maintain normal operation. In some embodiments, a power level warning can be issued to the gas user when the power level of the smart gas flow meter falls below the power threshold.

[0121] In some embodiments, the power threshold of the smart gas flow meter may be different for different gas users.

[0122] In some embodiments, the power threshold can be determined based on the battery performance state of the smart gas flow meter. For example, the worse the battery performance state of the smart gas flow meter, the higher the power threshold. In some embodiments, the smart gas management platform can determine the power threshold of the smart gas flow meter using a power threshold lookup table based on the battery performance state of the smart gas flow meter. The power threshold lookup table records the power thresholds corresponding to different battery performance states. The power threshold lookup table can be preset based on prior knowledge or historical data.

[0123] Understandably, a low battery level in a smart gas flow meter will affect its battery performance. If the battery performance is already poor, a low charge threshold can easily lead to battery failure, potentially causing significant losses for gas users. Therefore, when the battery performance is poor, the charge threshold can be set higher to reduce the likelihood of battery failure.

[0124] The battery status indicator (SSA) of a smart gas flow meter reflects the current condition of the battery relative to its factory condition. A better SSA indicates that the battery's current condition is closer to its factory condition. The SSA can be expressed numerically, as a percentage, or in text; this manual does not impose any restrictions.

[0125] In some embodiments, the smart gas management platform can determine the battery performance status of the smart gas flow meter based on the battery's service life and the number of repairs. For example, the longer the service life and the more repairs, the worse the battery performance.

[0126] In some embodiments, the intelligent gas management platform can determine the battery performance status of the intelligent gas flow meter based on environmental data and battery data, using a performance status lookup table. The battery data may include battery capacity, energy density, charge / discharge rate, self-discharge, and operating temperature range. The performance status lookup table includes the correspondence between different environmental data, different battery data, and different battery performance states. The performance status lookup table can be pre-set based on historical data or prior knowledge.

[0127] In some embodiments, the smart gas management platform can process environmental data and battery data of the smart gas flow meter based on a predictive model to predict the battery performance status of the smart gas flow meter.

[0128] For details regarding environmental data, please see [link / reference]. Figure 2 And related explanations.

[0129] The predictive model can be used to predict the battery performance state of a smart gas flow meter. In some embodiments, the predictive model is a machine learning model. For example, the predictive model can be a DNN or a binary classification model (Support Vector Machines, SVM).

[0130] In some embodiments, the input to the prediction model may include environmental data and battery data of the smart gas flow meter, and the output may be the battery failure probability of the smart gas flow meter.

[0131] In some embodiments, the state of battery performance of a smart gas flow meter can be determined based on the probability of battery failure. For example, the higher the probability of battery failure, the worse the state of battery performance of the smart gas flow meter.

[0132] Battery data for a smart gas flow meter refers to data related to the meter's battery. For example, battery data may include capacity, energy density, charge / discharge rate, self-discharge rate, and operating temperature range.

[0133] The parameters of the prediction model can be obtained through training. In some embodiments, the prediction model can be trained using multiple second training samples with second labels. For example, multiple second training samples with second labels can be input into the initial prediction model, and a loss function can be constructed using the second labels and the results of the initial prediction model. The parameters of the initial prediction model are then iteratively updated based on the loss function. When the loss function of the initial prediction model satisfies a preset condition, the model training is complete, and a trained prediction model is obtained. The preset condition can be, for example, the loss function converging or the number of iterations reaching a threshold.

[0134] In some embodiments, the second training samples may include sample environmental data and battery data of the sample smart gas flow meter. The second label may be historical anomalies of the sample smart gas flow meter's battery. In some embodiments, the second training samples may be obtained based on historical data (e.g., historical environmental data and historical battery data of the smart gas flow meter), and the second label may be obtained through manual annotation. For example, if the smart gas flow meter battery exhibits an anomaly, the second label is recorded as 1; if the smart gas flow meter battery does not exhibit an anomaly, the second label is recorded as 0.

[0135] Some embodiments in this specification process environmental data and battery data of smart gas flow meters based on predictive models to predict the probability of battery failure of smart gas flow meters and further determine the battery performance status of smart gas flow meters. This can utilize the self-learning capability of machine learning models to find patterns in large historical datasets, obtaining the correlation between environmental data, smart gas flow meter battery data, and the probability of battery failure, thus improving the accuracy and efficiency of determining the probability of battery failure. Simultaneously, based on the operating data of smart gas flow meters, the average daily power consumption of gas users is calculated. By using the average daily power consumption, remaining power, and power threshold, the reference battery operating time is determined, making the determined reference battery operating time of smart gas flow meters more accurate.

[0136] In some embodiments, the time threshold may differ for different gas users. In some embodiments, the time threshold may be an empirical value, a default value, a pre-set value, or any combination thereof, and may be determined according to actual needs, without limitation herein.

[0137] In some embodiments, the smart gas management platform can determine time thresholds based on historical behavior data of gas users.

[0138] Historical behavioral data of gas users refers to relevant data that reflects their past behavior. This data can include historical payment information and historical feedback information. For example, historical payment information may include the number of times a payment was overdue and the duration of the overdue payment, while historical feedback information may include the number of complaints filed.

[0139] In some embodiments, the smart gas management platform can obtain historical behavior data of gas users through an energy-saving early warning management IoT system.

[0140] In some embodiments, the smart gas management platform can preset the correspondence between historical behavior data of gas users and time thresholds, and determine the time thresholds corresponding to different historical behavior data by querying the correspondence.

[0141] In some embodiments, the smart gas management platform can determine a gas user's behavior score based on the user's historical behavior data; and determine a time threshold for the gas user based on the behavior score. The lower the gas user's behavior score, the higher the time threshold.

[0142] In some embodiments, the smart gas management platform can determine a gas user's behavior score based on the user's historical behavior data and a first scoring rule. For example, the first scoring rule could be: the more times a gas user defaults on payments, the longer the default period, or the more complaints they file, the lower their corresponding behavior score.

[0143] Some embodiments in this specification determine time thresholds based on historical behavior data of gas users, which can provide early warnings more time in advance based on the historical situation of gas users, reducing gas user complaints caused by insufficient battery power affecting gas supply.

[0144] Some embodiments in this specification issue power consumption warnings to gas users based on the relationship between reference operating time and time threshold, which can provide timely warnings about the power consumption of smart gas flow meters and improve customer satisfaction.

[0145] Figure 4 This is an exemplary schematic diagram illustrating the generation of an energy-saving management operation scheme according to some embodiments of this specification.

[0146] In some embodiments, the smart gas management platform can determine the importance score 430 of the operating behavior based on at least one reference correspondence 410 and the importance of the operating behavior 420; and generate an energy-saving management operation plan 440 based on the importance score 430.

[0147] The urgency level of an operational action is used to measure its urgency and importance. The higher the urgency level, the more urgent and important the action is.

[0148] The urgency of operational behavior can be preset by technical personnel based on experience and requirements.

[0149] In some embodiments, the smart gas management platform can determine the overall performance status 423 of the smart gas flow meter based on the battery performance status 421 and the historical usage data 422 of the smart gas flow meter; and determine the urgency 420 of the operating behavior based on the overall performance status 423 of the smart gas flow meter.

[0150] For details regarding historical usage data of the smart gas flow meter, please refer to the relevant instructions in step 220. For more information on determining the battery performance status of the smart gas flow meter, please refer to... Figure 3 And related explanations.

[0151] The overall performance status of a smart gas flow meter reflects how well it performs relative to its original factory condition. A better overall performance status indicates that the smart gas flow meter is closer to its original factory condition.

[0152] In some embodiments, the overall performance status of a smart gas flow meter can reflect the probability of the smart gas flow meter malfunctioning. For example, the worse the overall performance status of the smart gas flow meter, the higher the probability of the smart gas flow meter malfunctioning.

[0153] In some embodiments, the smart gas management platform can score the smart gas flow meter based on its battery performance status and historical usage data to obtain a first state score corresponding to the battery performance status and a second state score corresponding to the historical usage data; and determine the overall performance status of the smart gas flow meter based on the weighted result of the first state score and the second state score.

[0154] In some embodiments, the smart gas management platform can determine a first state score and a second state score by pre-setting a second scoring rule. An exemplary second scoring rule could be: the worse the battery performance of the smart gas flow meter, the lower the first state score; the longer the smart gas flow meter has been used and the more times it has been repaired, the lower the second state score.

[0155] In some embodiments, the intelligent gas management platform can directly determine the overall performance status of the intelligent gas flow meter by weighting the first state score and the second state score.

[0156] In some embodiments, the importance of operational behavior is negatively correlated with the overall performance status of the smart gas flow meter. The worse the overall performance status of the smart gas flow meter, the higher the importance of its operational behavior.

[0157] By negatively correlating the urgency of operational actions with the overall performance status of the smart gas flow meter, specific operational actions (such as uploading data related to smart gas flow meter faults) can be prioritized when the overall performance status of the smart gas flow meter is poor. This is because the worse the overall performance status of the smart gas flow meter, the more prone it is to failure, and the higher the urgency of the operational action of sending fault data, so as to detect the fault in a timely manner.

[0158] The importance score of operational behavior is a score that reflects the degree of importance of operational behavior. The higher the score, the more important the operational behavior.

[0159] In some embodiments, the intelligent gas management platform can determine the importance score of an operational behavior based on at least one reference correspondence and the urgency of the operational behavior. In some embodiments, the intelligent gas management platform can obtain the unit-time power consumption of the operational behavior based on one or more reference correspondences; determine a first score for the operational behavior by using a first preset correspondence between the unit-time power consumption and a first score; determine a second score for the operational behavior by using a second preset correspondence between the urgency of the operational behavior and a second score; and determine the importance score of the operational behavior by weighting and summing the first score and the second score based on preset weights. The first preset correspondence and the second preset correspondence can be preset by the system or manually.

[0160] In some embodiments, the intelligent gas management platform can generate energy-saving management operation plans based on a scoring threshold and an importance score for operational behaviors. For example, one or more operational behaviors with an importance score higher than the scoring threshold can be identified as energy-saving management operation plans. The scoring threshold can be preset by the system or manually.

[0161] Some embodiments in this specification generate an energy-saving management operation plan based on the urgency of the operating behavior when a power warning is issued. This plan can prioritize the execution of high-urgency operating behaviors when the power is critical, making full use of the remaining power of the smart gas flow meter and improving the safety of using the smart gas flow meter.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 should be 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. An energy-saving early warning management method for intelligent gas flow meters, characterized in that, The method is executed by the intelligent gas management platform and includes: Obtain the remaining power of the smart gas flow meter; Based on environmental data and historical usage data of the smart gas flow meter, a confidence level determination model is used to determine the confidence level of the remaining power. The confidence level determination model is a machine learning model. Based on the confidence level, an adjustment amount is determined, and the remaining power is added to the adjustment amount to determine the calibration power. At least based on the historical total operating data of the intelligent gas flow meter, at least one reference correspondence is preset, wherein the reference correspondence represents the relationship between the operating behavior of the intelligent gas flow meter and the power consumption; Based on the calibration charge, the operating data of the smart gas flow meter, and the at least one reference correspondence, the battery reference operating time of the smart gas flow meter is determined; Based on the battery reference operating time and time threshold, determine whether to issue a power level warning to gas users; In response to the battery reference operating time being lower than the time threshold, the power consumption warning is issued to the gas user, and an energy-saving management operation plan for the smart gas flow meter is generated.

2. The method as described in claim 1, characterized in that, Determining the battery reference operating time of the smart gas flow meter based on the calibration charge, the operating data of the smart gas flow meter, and the at least one reference correspondence includes: Based on the operational data, the average daily electricity consumption of the gas user is calculated; Based on the average daily power consumption, the calibrated power consumption, and the power consumption threshold, the reference battery operating time is determined, and the power consumption threshold is determined based on the battery performance status of the smart gas flow meter.

3. The method as described in claim 2, characterized in that, The method for determining the battery performance state includes: The environmental data and the battery data of the smart gas flow meter are processed based on a prediction model to predict the battery performance status. The prediction model is a machine learning model.

4. The method as described in claim 1, characterized in that, The time threshold is determined based on the gas user's historical behavior data.

5. The method as described in claim 1, characterized in that, The preset reference correspondence is based at least on the historical total operating data of the smart gas flow meter, including: Based on the historical total operating data, determine the power consumption per unit time required to maintain the operation of the intelligent gas flow meter; Based on experimental testing, the unit power consumption of the remaining operating behavior of the intelligent gas flow meter was determined.

6. The method as described in claim 1, characterized in that, The step of issuing the electricity warning to the gas user and generating an energy-saving management operation plan for the smart gas flow meter includes: Based on the at least one reference correspondence and the urgency of the operational behavior, an importance score for the operational behavior is determined; Based on the importance score, the energy-saving management operation plan is generated.

7. The method as described in claim 6, characterized in that, The methods for determining the urgency of the aforementioned operational behavior include: The overall performance status of the smart gas flow meter is determined based on its battery performance status and historical usage data. Based on the overall performance status, the urgency of the operational behavior is determined.

8. An energy-saving early warning management Internet of Things system for intelligent gas flow meters, characterized in that, The energy-saving early warning management IoT system includes a smart gas user platform, a smart gas service platform, a smart gas management platform, a smart gas sensor network platform, and a smart gas object platform that interact sequentially; the smart gas management platform is configured as follows: Obtain the remaining power of the smart gas flow meter; Based on environmental data and historical usage data of the smart gas flow meter, a confidence level determination model is used to determine the confidence level of the remaining power. The confidence level determination model is a machine learning model. Based on the confidence level, an adjustment amount is determined, and the remaining power is added to the adjustment amount to determine the calibration power. At least based on the historical total operating data of the intelligent gas flow meter, at least one reference correspondence is preset, wherein the reference correspondence represents the relationship between the operating behavior of the intelligent gas flow meter and the power consumption; Based on the calibration charge, the operating data of the smart gas flow meter, and the at least one reference correspondence, the battery reference operating time of the smart gas flow meter is determined; Based on the battery reference operating time and time threshold, determine whether to issue a power level warning to gas users; In response to the battery reference operating time being lower than the time threshold, the power consumption warning is issued to the gas user, and an energy-saving management operation plan for the smart gas flow meter is generated.

Images