Intelligent gas-based self-closing valve reliability monitoring method and internet of things system

Abstract

The embodiment of the specification provides a smart gas-based self-closing valve reliability monitoring method and an Internet of Things system. The method comprises the following steps: in response to the closing of a gas self-closing valve, obtaining running environment data within a first preset time and gas use information of a gas user within the first preset time; determining the closing type of the gas self-closing valve based on the gas use information and the set position of the gas self-closing valve; in response to the closing type being the first type, issuing an adjustment prompt; and in response to the closing type being the second type, determining the reliability of the current closing state of the gas self-closing valve, and judging whether to issue a warning prompt based on the reliability.

Description

Technical Field

[0001] This manual relates to the field of gas safety monitoring, and in particular to a method for monitoring the reliability of self-closing valves based on smart gas and an Internet of Things system. Background Technology

[0002] A gas shut-off valve is a valve used to control and cut off the flow of gas. In the event of dangerous situations such as gas leaks, fires, or explosions, the gas shut-off valve can quickly cut off the gas supply under manual or automatic control. Cutting off the gas supply effectively prevents gas backflow in the pipeline, that is, prevents unignited gas from flowing back into the gas supply pipeline, ensuring the safety and reliability of the gas system.

[0003] However, gas automatic shut-off valves inevitably encounter problems during use. Monitoring their operation typically involves regular on-site inspections and maintenance. This method is labor-intensive and cannot promptly detect abnormal operating conditions.

[0004] Therefore, it is desirable to provide a method and Internet of Things system for monitoring the reliability of gas self-closing valves based on smart gas, so as to monitor the operating status of gas self-closing valves in a timely and effective manner and ensure the stable operation of gas pipeline networks. Summary of the Invention

[0005] To address the problem of how to effectively and accurately monitor the operating status of a gas self-closing valve, this invention provides a smart gas self-closing valve reliability monitoring method, an Internet of Things system, and a storage medium.

[0006] This invention includes a method for monitoring the reliability of a self-closing valve based on smart gas systems. The method includes: in response to the closure of a gas self-closing valve, acquiring operating environment data and gas user usage information within a first preset time period; determining the closure type of the gas self-closing valve based on the gas usage information and the valve's location, wherein the closure type includes a first type and a second type, the first type being related to the gas usage and the second type being related to the gas supply; issuing an adjustment prompt in response to the closure type being the first type; and determining the reliability of the current closure state of the gas self-closing valve based at least on the operating environment data in response to the closure type being the second type, and determining whether to issue a warning based on the reliability.

[0007] This invention provides an IoT system for monitoring the reliability of self-closing valves based on smart gas technology. The IoT system includes a smart gas user platform, a smart gas service platform, a smart gas equipment management platform, a smart gas sensor network platform, and a smart gas object platform. The smart gas equipment management platform is configured to: in response to the closure of a gas self-closing valve, acquire operating environment data and gas user gas usage information within a first preset time period; based on the gas usage information and the setting position of the gas self-closing valve, determine the closure type of the gas self-closing valve, the closure type including a first type and a second type, where the first type is related to the gas usage and the second type is related to the gas supply; in response to the closure type being the first type, issue an adjustment prompt; in response to the closure type being the second type, determine the reliability of the current closure state of the gas self-closing valve based at least on the operating environment data, and determine whether to issue a warning prompt based on the reliability.

[0008] The present invention includes a computer-readable storage medium storing computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the self-closing valve reliability monitoring method based on smart gas.

[0009] The beneficial effects of the above invention include, but are not limited to: (1) the specific closing type of the gas self-closing valve can be accurately determined by the gas usage information and the setting position of the gas self-closing valve; (2) when the closing type of the gas self-closing valve is the first type, adjustment information can be sent to the gas user in a timely manner to prevent the gas user from being unable to use gas or gas leakage due to misoperation or improper use; when the closing type is the second type, a warning can be sent to the management personnel in a timely manner to effectively improve the management efficiency and service quality of the management personnel. Attached Figure Description

[0010] Figure 1 This is an exemplary platform structure diagram of an IoT system for monitoring the reliability of intelligent gas self-closing valves, as shown in some embodiments of this specification.

[0011] Figure 2 This is an exemplary flowchart of a smart gas self-closing valve reliability monitoring method according to some embodiments of this specification;

[0012] Figure 3 This is an exemplary schematic diagram illustrating the determination of whether to issue a first warning prompt based on some embodiments of this specification;

[0013] Figure 4 This is an exemplary schematic diagram illustrating the determination of whether to issue a second warning prompt, based on 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 IoT system for monitoring the reliability of intelligent gas self-closing valves, as shown in some embodiments of this specification.

[0019] like Figure 1 As shown, the smart gas self-closing valve reliability monitoring IoT system 100 includes a smart gas user platform 110, a smart gas service platform 120, a smart gas equipment management platform 130, a smart gas sensor network platform 140, and a smart gas object platform 150 that interact sequentially.

[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. In some embodiments, the smart gas user platform may include a gas user sub-platform, a government user sub-platform, and a regulatory user sub-platform. The gas user sub-platform provides gas users with gas-related data and solutions to gas-related problems. Gas users can be industrial gas users, commercial gas users, or general gas users, etc. The government user sub-platform provides government users with gas operation-related data. Government users can be managers of gas operating entities (such as administrative department managers), etc. The regulatory user sub-platform can be a platform for regulatory users to monitor the operation of the entire Internet of Things system. Regulatory users can be personnel from safety management departments.

[0021] In some embodiments, the smart gas user platform 110 can send a query command for gas equipment usage data to the smart gas equipment management platform 130 via the smart gas service platform 120, and receive gas equipment management plans (e.g., adjustment prompts, early warning prompts, etc.) uploaded by the smart gas service platform 120.

[0022] The smart gas service platform 120 is a platform for receiving and transmitting data and / or information. In some embodiments, the smart gas service platform 120 can receive query instructions issued by the smart gas user platform 110 and forward them to the smart gas equipment management platform 130. In some embodiments, the smart gas service platform 120 can send gas equipment management solutions to the smart gas user platform 110.

[0023] In some embodiments, the smart gas service platform may include a smart gas consumption service sub-platform, a smart operation service sub-platform, and a smart regulatory service sub-platform. The smart gas consumption service sub-platform provides gas consumption services to gas users. The smart operation service sub-platform provides gas operation-related information (e.g., gas equipment management information) to government users. The smart regulatory service sub-platform provides regulatory information to users who require regulatory services.

[0024] The intelligent gas equipment management platform 130 can coordinate and integrate the connections and collaborations between various functional platforms, and gather all the information of the Internet of Things, providing a platform for sensing, management and control management functions for the Internet of Things operation system.

[0025] In some embodiments, the smart gas equipment management platform 130 may include a smart gas indoor equipment parameter management sub-platform, a smart gas pipeline equipment parameter management sub-platform, and a smart gas data center.

[0026] The smart gas data center can aggregate and store at least a portion of the operational data from the Internet of Things (IoT) system. In some embodiments, the smart gas equipment 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. In some embodiments, the smart gas data center can send query commands for gas equipment usage data to the smart gas sensor network platform 140 and receive relevant data about gas equipment uploaded by the smart gas sensor network platform 140.

[0027] In some embodiments, the smart gas indoor equipment parameter management sub-platform and the smart gas pipeline equipment parameter management sub-platform can interact bidirectionally with the smart gas data center.

[0028] In some embodiments, the smart gas indoor equipment parameter management sub-platform and the smart gas pipeline equipment parameter management sub-platform may respectively include an equipment operation parameter detection and early warning module and an equipment parameter remote management module.

[0029] The equipment operation parameter detection and early warning module can be used to view historical and real-time equipment operation parameters and provide monitoring and early warning based on preset thresholds. When equipment operation parameters are abnormal (e.g., exceeding the corresponding preset threshold), managers can directly switch from the equipment operation parameter detection and early warning module to the equipment parameter remote management module to remotely process the equipment parameters. If necessary, adjustment prompts and / or early warnings can be sent to users through the smart gas service platform 120.

[0030] The remote equipment parameter management module can remotely set and adjust the equipment parameters of the smart gas object platform 150, and remotely authorize the adjustment of equipment parameters initiated on-site by the smart gas object platform 150.

[0031] The intelligent gas sensor network platform 140 is a functional platform for managing sensor communication. In some embodiments, the intelligent gas sensor network platform 140 can be configured as a communication network and gateway to perform functions such as network management, protocol management, command management, and data parsing.

[0032] In some embodiments, the smart gas sensor network platform 140 may include a smart gas indoor equipment sensor network sub-platform and a smart gas pipeline equipment sensor network sub-platform.

[0033] The intelligent gas object platform 150 can be a functional platform for generating sensing information and executing control information. For example, the intelligent gas object platform 150 can monitor and acquire the operating information of gas equipment (e.g., gas self-closing valve).

[0034] In some embodiments, the smart gas target platform 150 may include a smart gas indoor equipment target sub-platform and a smart gas pipeline equipment target sub-platform. The smart gas indoor equipment target sub-platform can be configured to include various types of gas indoor equipment used by gas users. The smart gas pipeline equipment target sub-platform can be configured to include various types of gas pipeline equipment and monitoring equipment.

[0035] In some embodiments, the smart gas object platform 150 can send the operating information of gas equipment to the smart gas equipment management platform 130 through the smart gas sensor network platform 140.

[0036] Some embodiments in this specification demonstrate that the IoT system 100 for monitoring the reliability of smart gas self-closing valves 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 equipment management platform, thereby realizing the informatization and intelligentization of gas equipment management.

[0037] Figure 2 This is an exemplary flowchart of a smart gas self-closing valve reliability monitoring method according to some embodiments of this specification. In some embodiments, process 200 can be executed by a smart gas equipment management platform 130. Figure 2 As shown, process 200 includes the following steps.

[0038] Step 210: In response to the gas self-closing valve closing, acquire the operating environment data and the gas user's gas usage information within the first preset time period.

[0039] The first preset time refers to the period of time before the gas self-closing valve closes. The first preset time can be determined by the system or by manual preset.

[0040] Operating environment data refers to data information related to the environment in which the gas self-closing valve is located. In some embodiments, operating environment data includes gas pressure, flow rate, temperature, humidity, etc. In some embodiments, the intelligent gas equipment management platform 130 can acquire the operating environment data of the gas self-closing valve in real time through sensing elements and store it in the storage device configured in the intelligent gas data center. The intelligent gas equipment management platform 130 can acquire operating environment data within a first preset time period by interacting with the storage device.

[0041] Sensing elements are components used to sense, measure, or detect specific parameters or signals in the environment. Examples of sensing elements include pressure sensors, flow rate sensors, temperature sensors, and humidity sensors.

[0042] Gas usage information refers to data related to gas users' gas consumption. In some embodiments, gas usage information includes gas meter battery level, remaining gas balance, and the power status of critical gas appliances. The gas meter is a device connected to the gas shut-off valve and used to monitor gas consumption and meter usage. Critical gas appliances are those requiring gas usage monitoring. Critical gas appliances can be preset. Examples include water heaters and gas stoves. Insufficient power in the gas meter battery or critical gas appliances may cause the gas shut-off valve to close. Accordingly, the power status of critical gas appliances can include whether the water heater or gas stove has insufficient power. Insufficient power can mean that the power level is below a preset power threshold.

[0043] It should be noted that when the key gas appliance is a gas stove that does not require battery power for ignition, the power status of the key gas appliance does not include the power status of the gas stove.

[0044] The intelligent gas equipment management platform 130 can obtain gas usage information within a first preset time period by interacting with the storage device.

[0045] Step 220: Based on the gas usage information and the setting location of the gas self-closing valve, determine the closing type of the gas self-closing valve.

[0046] In some embodiments, the location of the gas self-closing valve may include a user location and a non-user location.

[0047] A non-user location refers to a gas shut-off valve installed on a public gas pipeline. Public gas pipelines include those supplying gas to an entire high-rise building or a residential community. Gas shut-off valves installed in non-user locations can be used to control the gas supply to downstream gas pipelines.

[0048] User location refers to any installation location other than the user's location. For example, non-public gas pipelines include gas pipelines entering a single gas user's home, gas pipelines supplying gas to a user's critical gas appliances, and locations near critical gas appliances. Gas shut-off valves installed at user locations can be used to control the gas supply to a single gas user's gas pipeline.

[0049] Closure type can be used to classify the reasons or circumstances under which a gas self-closing valve closes. In some embodiments, the closure type of a gas self-closing valve includes a first type and a second type.

[0050] The first type refers to the type of gas valve closure caused by the gas user. In some embodiments, the first type relates to gas usage. For example, the first type could be the type of gas valve closure caused by insufficient gas meter current, unpaid gas bills, or insufficient gas stove battery power. Specifically, when the gas meter battery is low, the gas meter cannot function properly and provide real-time data, which may trigger the gas valve's closure protection mechanism to prevent the inability to monitor and measure gas consumption. If gas bills are long overdue, the gas supplier may take measures to interrupt the gas supply. In this case, the gas supplier can cut off the gas supply by operating the gas valve to force the gas user to fulfill their payment obligations. When the gas stove battery is low, the ignition device may not function properly, resulting in the inability to ignite the gas. In this case, the gas valve's closure protection mechanism may be triggered to prevent gas leakage.

[0051] The second type refers to the type of gas valve closure caused by the gas supplier. In some embodiments, the second type relates to the gas supply situation. For example, the second type could be the type of gas valve closure caused by a gas leak.

[0052] In some embodiments, the intelligent gas equipment management platform 130 can determine the closing type of the gas automatic shut-off valve based on gas usage information and the setting position of the gas automatic shut-off valve through preset judgment rules. For example, the preset judgment rule could be: when the gas usage information shows that the power of the key gas equipment is insufficient and the setting position of the gas automatic shut-off valve is the user's position, the closing type is determined to be the first type. For example, the preset judgment rule could be: when the gas usage information shows that the user's remaining gas cost is insufficient (i.e., the remaining cost is 0 or negative) and the setting position of the gas automatic shut-off valve is the user's position, the closing type is determined to be the first type. For another example, the preset judgment rule could be: when the gas usage information shows that the gas meter battery is insufficient and the setting position of the gas automatic shut-off valve is the user's position, the closing type is determined to be the first type. For yet another example, the preset judgment rule could be: when the gas usage information shows that the gas meter battery has sufficient power, the power of the key gas equipment is sufficient, the user's remaining gas cost is sufficient (i.e., the remaining cost is greater than 0), and the setting position of the gas automatic shut-off valve is not the user's position, the closing type is determined to be the second type.

[0053] In some embodiments, the intelligent gas equipment management platform 130 can determine the closing type of the gas self-closing valve based on its setting location.

[0054] In some embodiments, in response to the setting location being a non-user location, the smart gas equipment management platform 130 can determine that the shutdown type is a second type.

[0055] In some embodiments, in response to setting the location as the user's location, the smart gas equipment management platform 130 can determine whether the gas usage information meets preset conditions; in response to meeting the preset conditions, it determines the shutdown type as the first type; in response to not meeting the preset conditions, it determines the shutdown type as the second type.

[0056] Preset conditions refer to the conditions used to determine whether the closing type is the first type or the second type.

[0057] In some embodiments, the preset condition may be: the gas meter battery level is lower than a preset power threshold. In some embodiments, the preset condition may be: the user's remaining gas bill is lower than a preset bill threshold. In some embodiments, the preset condition may be: the power level of critical gas appliances is lower than a preset power threshold. The preset power thresholds may differ for different types of gas appliances and can be set according to actual circumstances.

[0058] In some embodiments, when the gas usage information meets any one or more of the preset conditions mentioned above, the intelligent gas equipment management platform 130 can determine that the gas usage information meets the preset conditions, thereby determining that the closing type of the gas self-closing valve is the first type; conversely, if the gas usage information does not meet the preset conditions, it can determine that the closing type of the gas self-closing valve is the second type. The preset electricity threshold, preset cost threshold, and preset power threshold can be preset by management personnel based on historical experience.

[0059] In some embodiments of this specification, the closing type of the gas self-closing valve can be quickly and accurately determined based on the setting location of the gas self-closing valve and gas usage information, effectively improving the management efficiency and service quality of managers.

[0060] Step 230: In response to the closing type being Type 1, an adjustment prompt is issued.

[0061] In some embodiments, the adjustment prompt can be used to remind gas users to adjust the closed state of the gas self-closing valve. For example, it can remind gas users to change the gas self-closing valve from a closed state to an open state. In some embodiments, the adjustment prompt can be used to remind gas users to adjust related gas appliances.

[0062] For example, the adjustment prompts may include reminding gas users to replace the batteries in their gas meters, reminding them to pay their gas bills, and reminding them to charge critical gas equipment.

[0063] In some embodiments, the intelligent gas equipment management platform 130 can issue adjustment prompts in various ways. For example, it can push adjustment prompts to gas users via SMS or mobile application, or provide adjustment prompts via gas self-closing valves and LED indicator lights on gas equipment. The content of the above adjustment prompts is for illustrative purposes only and is not intended to limit the scope of this specification.

[0064] Step 240: In response to the closing type being the second type, determine the reliability of the current closing state of the gas self-closing valve based at least on the operating environment data, and determine whether to issue a warning based on the reliability of the current closing state.

[0065] The reliability of the current closed state refers to the accuracy with which the gas automatic shut-off valve is currently closed. For example, if the gas automatic shut-off valve is in the closed state when a gas abnormality occurs, the reliability of the closed state is considered high. Conversely, if the gas automatic shut-off valve is in the closed state when no gas abnormality occurs, the reliability of the closed state is considered low. Gas abnormalities include, but are not limited to, one or more of the following: insufficient gas meter battery power, unpaid gas bills, insufficient power in critical gas equipment, and gas leaks.

[0066] In some embodiments, in response to the shutdown type being the second type, the intelligent gas equipment management platform 130 can determine the reliability of the current shutdown state by querying a preset lookup table based on operating environment data. In some embodiments, the preset lookup table may include the correspondence between different operating environment data and different levels of reliability. The preset lookup table may be determined based on historical data or prior knowledge.

[0067] In some embodiments, the reliability of the current closed state may include perceived reliability. Perceived reliability refers to the accuracy of the operating environment data detected by the sensing element of the gas self-closing valve. A higher perceived reliability indicates a higher accuracy of the operating environment data detected by the sensing element.

[0068] In some embodiments, in response to the shutdown type being the second type, the intelligent gas equipment management platform 130 can also determine the gas supply characteristics based on operating environment data, and further determine the perceived reliability of the current shutdown state. For more details on this embodiment, see [link to relevant documentation]. Figure 3 And its related descriptions.

[0069] In some embodiments, the reliability of the closed state may include operational reliability. Operational reliability refers to the degree to which the non-sensing elements of the gas self-closing valve can function properly. Non-sensing elements refer to components on the gas self-closing valve other than the sensing elements, which are used to perform mechanical operations. For example, non-sensing elements may include springs, bearings, relays, etc.

[0070] In some embodiments, in response to the shutdown type being the second type, the intelligent gas equipment management platform 130 can also determine the downstream gas supply interruption characteristics based on downstream gas data, and further determine the execution reliability of the current shutdown state. For more details on this embodiment, see [link to relevant documentation]. Figure 4 And its related descriptions.

[0071] In some embodiments, in response to the reliability of the current shutdown state being lower than a preset reliability threshold, the smart gas equipment management platform 130 can issue a warning to the management personnel. For example, the warning can be issued via SMS or a mobile application, or displayed on a dashboard in the management personnel's workplace.

[0072] In some embodiments, the warning notification may include a first warning notification. The first warning notification is a warning notification issued based on perceived reliability. In some embodiments, in response to the perceived reliability of the current off state being lower than a first reliability threshold, the smart gas equipment management platform 130 may issue a first warning notification. The first reliability threshold is a preset reliability threshold used to determine whether to issue the first warning notification. The first reliability threshold may be preset by management personnel based on historical experience, etc.

[0073] In some embodiments, the warning notification may include a second warning notification. The second warning notification is a warning notification issued based on execution reliability. In some embodiments, in response to the execution reliability of the current shutdown state being lower than a second reliability threshold, the intelligent gas equipment management platform 130 may issue a second warning notification. The second reliability threshold is a preset reliability threshold used to determine whether to issue a second warning notification. The second reliability threshold may be preset by administrators based on historical experience, etc.

[0074] In some embodiments, the smart gas equipment management platform 130 may issue a first warning and / or a second warning to the management personnel. In some embodiments, the smart gas equipment management platform 130 may also issue a first warning and / or a second warning to other components of the smart gas self-closing valve reliability monitoring IoT system 100 (e.g., the smart gas object platform, etc.) to verify relevant data of each gas equipment (e.g., detection data of sensing elements, operation records of gas self-closing valves, etc.).

[0075] In some embodiments, the methods used to issue the first and second warning alerts may be the same or different. In some embodiments, the content of the first and second warning alerts may be the same or different. In some embodiments, the first warning alert may be related to a malfunction of a sensing element. For example, the first warning alert may include the type of sensing element that has malfunctioned. In some embodiments, the second warning alert may be related to a malfunction of a gas self-closing valve. For example, the second warning alert may include a delay in the closing time of the gas self-closing valve.

[0076] In some embodiments of this specification, the specific closing type of the gas self-closing valve can be accurately determined by the gas usage information and the setting position of the gas self-closing valve. When the closing type of the gas self-closing valve is the first type, adjustment information can be promptly sent to the gas user to prevent the gas user from being unable to use the gas or from experiencing gas leakage due to misoperation or improper use; when the closing type is the second type, a warning can be promptly sent to the management personnel, effectively improving the management efficiency and service quality of the management personnel.

[0077] Figure 3 This is an exemplary schematic diagram illustrating the determination of whether to issue a first warning prompt based on some embodiments of this specification.

[0078] In some embodiments, in response to the shutdown type being the second type, the intelligent gas equipment management platform 130 can determine the perceived reliability based on operating environment data and issue a first warning based on the actual situation of the perceived reliability.

[0079] See Figure 3 In some embodiments, the intelligent gas equipment management platform 130 can determine the gas supply characteristics 311 based on the operating environment data 310; determine the sensing reliability 330 of the current closed state based on the gas supply characteristics 311 and the sensing element data 320 of the gas self-closing valve; and issue a first warning to the management personnel in response to the sensing reliability being lower than a first reliability threshold.

[0080] For more information on perceived reliability, first warning alert, and first reliability threshold, please refer to [link / reference]. Figure 2 And its related descriptions.

[0081] Gas supply characteristics refer to the features related to the supply of gas over a period of time. For example, gas supply characteristics include the rate of change of the pressure, flow rate, temperature, humidity, etc. of the gas supplied over a period of time.

[0082] In some embodiments, the intelligent gas equipment management platform 130 can determine gas supply characteristics through data analysis based on operating environment data at a first preset time. For example, the first preset time is a time interval [t, t+t0]. At this time, the difference between the operating environment data at time t+t0 and the operating environment data at time t can be determined, and the ratio of this difference to t0 is determined as the gas supply characteristics.

[0083] Sensing element data refers to data information related to a sensing element. In some embodiments, sensing element data includes usage data and performance data. Usage data may include usage time (from manufacturing date to present), number of maintenance visits, etc. Performance data may include sensitivity, measurement accuracy, response time, and stability. Sensitivity refers to the degree to which the sensing element responds to an input signal. Higher sensitivity means the sensing element can more accurately sense and measure subtle changes in the input signal. Measurement accuracy refers to the degree of closeness between the sensing element's measurement result and the true value. Sensing elements with high measurement accuracy provide more accurate and reliable measurement results. Response time is the time interval between receiving an input signal and generating a response. Sensing elements with fast response times can more quickly sense and respond to changes in the environment. Stability refers to the accuracy and consistency of the sensing element in repeated sensing.

[0084] Sensing element data can be determined in various ways. In some embodiments, the smart gas equipment management platform 130 can determine the sensing element data based on feedback from gas users. For example, if a gas user reports that the actual gas usage does not match the gas usage displayed on the gas meter, it indicates that the sensing element has low sensitivity. In some embodiments, the smart gas equipment management platform 130 can determine the sensing element's performance data using detection devices. For example, managers can use a signal generator to detect the sensing element's response time and an oscilloscope to determine the sensing element's sensitivity, measurement accuracy, and stability. In some embodiments, the sensing element data is related to the number of times the sensing element has been repaired and its usage time. For example, the more repairs and the longer the usage time, the worse the sensing element's performance data. In some embodiments, the smart gas equipment management platform 130 can retrieve the sensing element's usage data from a storage device. The above descriptions of the methods for determining sensing element data are for illustrative purposes only and are not intended to limit the scope of this specification.

[0085] In some embodiments, the intelligent gas equipment management platform 130 can determine the sensing reliability based on gas supply characteristics and sensing element data through vector retrieval.

[0086] In some embodiments, the intelligent gas equipment management platform 130 can construct a matching vector based on gas supply characteristics and sensing element data; perform vector matching in the vector database based on the matching vector to determine the associated feature vector; and determine the sensing reliability based on the associated feature vector.

[0087] In some embodiments, the vector database may include multiple reference feature vectors and their corresponding reference sensing reliability. In some embodiments, the reference feature vectors may be constructed based on historical data. For example, multiple reference feature vectors may be obtained by constructing vectors from multiple historical gas supply characteristics and historical sensing element data. The reference sensing reliability corresponding to the reference feature vectors may be manually labeled based on prior knowledge and the fault status of the sensing elements.

[0088] In some embodiments, the intelligent gas equipment management platform 130 can determine reference feature vectors that meet preset matching conditions from a vector database based on the vector to be matched, and identify the reference feature vectors that meet the preset matching conditions as associated feature vectors. The preset matching conditions can refer to the judgment conditions used to determine the associated feature vectors. In some embodiments, the preset matching conditions may include vector distance less than a distance threshold, minimum vector distance, etc.

[0089] In some embodiments, the processor may determine the reference perceived reliability corresponding to the associated feature vector as the current perceived reliability.

[0090] In some embodiments, the intelligent gas equipment management platform 130 can determine a first sensing reliability and a second sensing reliability based on gas supply characteristics and sensing element data; and determine the sensing reliability based on the first sensing reliability and / or the second sensing reliability.

[0091] The first and second perceived reliability are parameters used to determine perceived reliability.

[0092] In some embodiments, the intelligent gas equipment management platform 130 can determine the first sensing reliability and the second sensing reliability through vector retrieval based on gas supply characteristics and sensing element data. Accordingly, the vector database mentioned above may include a reference to the first sensing reliability and a reference to the second sensing reliability; further details are provided in the relevant descriptions above and will not be repeated here.

[0093] In some embodiments, the methods for determining the first perceived reliability and the second perceived reliability may differ.

[0094] In some embodiments, the intelligent gas equipment management platform 130 may determine a first degree of anomaly based on gas supply characteristics; determine a second degree of anomaly based on sensing element data; determine a comprehensive degree of anomaly based on the first degree of anomaly and the second degree of anomaly; and determine a first degree of sensing reliability based on the comprehensive degree of anomaly.

[0095] The first and second degrees of anomaly are both parameters used to measure the degree of anomaly in a sensing element; the difference between them lies in how they are determined.

[0096] In some embodiments, the intelligent gas equipment management platform 130 can calculate a first similarity between the gas supply characteristics and historical gas supply characteristics at the same time and location, and determine a first degree of anomaly based on the first similarity. For example, the difference between the numerical value 1 and the first similarity can be determined as the first degree of anomaly. The first similarity can be determined based on the vector distance between the gas supply characteristics and historical gas supply characteristics. For example, the vector distance can be Euclidean distance, Manhattan distance, etc.

[0097] In some embodiments, the intelligent gas equipment management platform 130 can determine the second degree of anomaly of a sensing element based on sensing element data by querying an anomaly degree lookup table. In some embodiments, the anomaly degree lookup table may include a correspondence between different sensing element data and different second degrees of anomaly. For example, the more times a sensing element has been repaired, the longer it has been used, and the worse its performance parameters are, the higher the second degree of anomaly of that sensing element. The anomaly degree lookup table can be preset based on historical data or prior knowledge.

[0098] The overall anomaly level refers to a parameter that comprehensively measures the degree of anomaly of the sensing element. In some embodiments, the intelligent gas equipment management platform 130 determines the overall anomaly level by a weighted sum of the first anomaly level and the second anomaly level.

[0099] In some embodiments, the intelligent gas equipment management platform 130 can allocate weighted weights according to the ratio of a first degree of anomaly to a second degree of anomaly. For example, a total weight value (hereinafter referred to as the first total weight value) corresponding to the first degree of anomaly and the second degree of anomaly can be preset, and the weighted weights corresponding to the first degree of anomaly and the second degree of anomaly can be determined according to the first total weight value and the ratio of the first degree of anomaly to the second degree of anomaly, respectively. The first total weight value can be predetermined by the system or manually.

[0100] In some embodiments, the intelligent gas equipment management platform 130 can determine the first sensing reliability of the sensing element by querying a sensing reliability lookup table based on the overall anomaly level. In some embodiments, the sensing reliability lookup table may include a correspondence between different overall anomaly levels and different first sensing reliability levels. For example, the higher the overall anomaly level, the lower the first sensing reliability. The sensing reliability lookup table may be pre-set based on historical data or prior knowledge.

[0101] In some embodiments of this specification, by determining the first sensing reliability, the accuracy of the operating environment data detected by the sensing element can be analyzed from the perspectives of gas supply characteristics and sensing element data. Since the comprehensive anomaly degree takes into account the influence of gas supply characteristics and sensing element data, the accuracy of the first sensing reliability determined by the comprehensive anomaly degree can be effectively improved.

[0102] In some embodiments, the intelligent gas equipment management platform 130 can determine the estimated failure rate of the sensing element of the gas self-closing valve through a fault prediction model based on gas supply characteristics, sensing element data and operating environment data; and determine the second sensing reliability based on the estimated failure rate.

[0103] A fault prediction model is a model used to estimate the failure rate of sensing elements. In some embodiments, fault prediction is a machine learning model. For example, any one or a combination of deep neural network (DNN), support vector machine (SVM), or other custom model structures.

[0104] In some embodiments, the inputs to the fault prediction model include gas supply characteristics, sensing element data, and operating environment data; the output includes the estimated failure rate of the sensing elements. For further details regarding gas supply characteristics and operating environment data, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0105] The estimated failure rate refers to the estimated probability that a sensing element will fail.

[0106] In some embodiments, the input to the fault prediction model may also include downstream gas supply interruption characteristics. Further details regarding downstream gas supply interruption characteristics can be found in [link to relevant documentation]. Figure 4 And its related descriptions.

[0107] In some embodiments of this specification, the downstream gas supply interruption characteristics are further input into the fault prediction model, which fully and comprehensively considers the impact of gas in the downstream gas pipeline of the gas self-closing valve on the sensing element, making the estimated failure rate of the sensing element output by the model more accurate and reasonable.

[0108] In some embodiments, the fault prediction model can be trained using a large number of first training samples with first labels through various methods. For example, it can be trained using gradient descent. As an example only, multiple first training samples with first labels can be input into the initial fault prediction model. A loss function is constructed using the first labels and the results of the initial fault prediction model. The parameters of the initial monitoring model are then iteratively updated based on the loss function. The model training is complete when the loss function of the initial fault prediction model satisfies a preset condition, resulting in a trained fault prediction model. The preset condition could be loss function convergence, the number of iterations reaching a threshold, etc.

[0109] In some embodiments, the first training sample may include sample gas supply characteristics of the sample gas self-closing valve, sample sensing element data, and sample operating environment data. The first label may be the actual failure probability of the sensing element in the sample gas self-closing valve. When the sensing element in the sample gas self-closing valve malfunctions, its failure probability is recorded as 1; when the sensing element in the sample gas self-closing valve does not malfunction, its failure probability is recorded as 0.

[0110] In some embodiments, the first training sample can be obtained based on historical operating data of the sample gas self-closing valve. The first label can be determined based on whether the sensing element is abnormal and the type of abnormality. For example, if the sensing element corresponding to the first training sample is not abnormal, the first label is 0. If the sensing element corresponding to the first training sample is abnormal, the value of the first label is determined according to the degree of influence of the abnormality type on the measurement accuracy of the sensing element. The greater the influence of the abnormality type on the measurement accuracy of the sensing element, the closer the value of the first label is to 1. The degree of influence of the abnormality type of the sensing element on the measurement accuracy of the sensing element can be determined by the system or by human preset.

[0111] In some embodiments, the smart gas equipment management platform 130 can determine the second sensing reliability based on the estimated failure rate of the sensing element in various ways. For example, the smart gas equipment management platform 130 can determine the second sensing reliability as the difference between 1 and the estimated failure rate. As another example, the smart gas equipment management platform 130 can determine the second sensing reliability as the reciprocal of the estimated failure rate.

[0112] In some embodiments of this specification, by determining the second sensing reliability, the impact of sensing element failure on the accuracy of the operating environment data detected by the sensing element can be taken into account. Furthermore, by determining the estimated failure rate through a fault prediction model, the self-learning capability of machine learning models can be utilized to find patterns in large amounts of data, obtaining the correlation between gas supply characteristics, sensing element data, operating environment data, and the failure rate, thereby improving the accuracy and efficiency of determining the failure rate, and effectively improving the accuracy of the second sensing reliability.

[0113] In some embodiments, the intelligent gas equipment management platform 130 can determine the perceived reliability in various ways based on a first perceived reliability and / or a second perceived reliability. In some embodiments, the intelligent gas equipment management platform 130 can determine the first perceived reliability as the perceived reliability. In some embodiments, the intelligent gas equipment management platform 130 can determine the second perceived reliability as the perceived reliability. In some embodiments, the intelligent gas equipment management platform 130 can determine the perceived reliability as a weighted result of the first and second perceived reliability. In some embodiments, the weights of the first and second perceived reliability can be allocated according to the ratio between them. In some embodiments, the intelligent gas equipment management platform 130 can determine the weight corresponding to the second perceived reliability based on the execution reliability of the current closed state of the gas self-closing valve. For example, the weight corresponding to the second perceived reliability can be negatively correlated with the execution reliability; the higher the execution reliability, the larger the weight corresponding to the second perceived reliability. Furthermore, the intelligent gas equipment management platform 130 can determine the weighted weight corresponding to the first perceived reliability based on the weighted weight corresponding to the second perceived reliability and the total weight of the weighted weights corresponding to the first perceived reliability and the second perceived reliability (hereinafter referred to as the second weight total value). The second weight total value can be predetermined by the system or by a human.

[0114] In some embodiments of this specification, the sensing reliability is determined by a first sensing reliability and / or a second sensing reliability. This allows for a comprehensive analysis of the accuracy of the operating environment data detected by the sensing element, based on actual gas supply characteristics, sensing element data, and / or predicted sensing element failure rates, thereby improving the accuracy of the sensing reliability. By determining the sensing reliability and deciding whether to issue a first warning based on it, management personnel can determine whether the sensing element is in normal working condition and issue timely warnings. This facilitates timely maintenance of the gas self-closing valve, thereby ensuring the safety and reliability of the gas supply.

[0115] Figure 4 This is an exemplary schematic diagram illustrating the determination of whether to issue a second warning prompt, based on some embodiments of this specification.

[0116] In some embodiments, in response to the shutdown type being the second type, the intelligent gas equipment management platform 130 can determine the execution reliability and issue a second early warning based on the actual execution reliability.

[0117] See Figure 4In some embodiments, in response to the shutdown type being the second type, the smart gas equipment management platform 130 can acquire downstream gas data 410 within a second preset time period; determine downstream gas supply interruption characteristics 411 based at least on the downstream gas data 410; determine execution reliability 420 based on the downstream gas supply interruption characteristics 411; and issue a second early warning to the management personnel in response to the execution reliability being lower than the second reliability threshold.

[0118] For more information on execution reliability, the second warning alert, and the second reliability threshold, please refer to [link / reference]. Figure 2 And its related descriptions.

[0119] The second preset time refers to a period of time after the gas self-closing valve closes. The second preset time can be determined by the system or by manual preset.

[0120] Downstream gas data refers to data related to the gas in the downstream gas pipeline. The downstream gas pipeline is located downstream of the gas self-closing valve.

[0121] In some embodiments, the downstream gas data includes at least a gas supply sequence. The gas supply sequence may include gas supply data detected over multiple time periods within a second preset time period. Gas supply refers to data related to the amount of gas supplied by the downstream gas pipeline. Gas supply may refer to the gas supply per unit time.

[0122] In some embodiments, the intelligent gas equipment management platform 130 can detect the gas supply through gas metering devices installed at downstream gas pipelines. In some embodiments, the intelligent gas equipment management platform 130 can determine the gas supply per unit time of downstream gas pipelines through the following steps S11 and S12.

[0123] Step S11: Divide the second preset time into multiple time periods according to preset unit time intervals. For example, if the second preset time is 20 minutes, divide the second preset time into 20 time periods according to preset unit time intervals of 1 minute.

[0124] Step S12: Based on the gas metering device closest to the gas self-closing valve and installed on the downstream gas pipeline, determine the gas supply per unit time in the downstream gas pipeline after the gas self-closing valve closes. For example, if the start time of a certain time period is A and the end time is B, and the readings of the gas metering device at times A and B are a and b respectively, then the gas supply per unit time is...

[0125] Downstream gas supply interruption characteristics refer to features related to the gas supply status of downstream gas pipelines. In some embodiments, downstream gas supply interruption characteristics may include the interruption time, interruption completion rate, and interruption speed of gas supply in the downstream gas pipeline.

[0126] The gas supply interruption time refers to the time from the closure of the gas automatic shut-off valve to the restoration of stable gas supply in the downstream gas pipeline. For example, if the gas automatic shut-off valve closes at time T1, and the gas supply in the downstream gas pipeline restores to stable at time T2, then the interruption time is T2-T1. The time when the gas supply restores to stable refers to the moment when the gas supply per unit time tends to stabilize. For example, if multiple consecutive time periods (the number can be preset by the system or manually) have the same or similar gas supply per unit time, then the end time of the last time period is the moment when the gas supply per unit time tends to stabilize.

[0127] The completion rate of gas supply interruption refers to the degree to which downstream gas pipelines have stopped supplying gas. In some embodiments, the intelligent gas equipment management platform 130 may determine the completion rate of gas supply interruption based on the following steps S21-S23.

[0128] Step S21: Divide the first preset time into multiple time periods according to a preset unit time interval. For an explanation of the first preset time, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0129] Step S22: Based on the gas metering device that is closest to the gas self-closing valve and is installed on the downstream gas pipeline, determine the unit time gas supply in the last time period of the first preset time.

[0130] Step S23: Calculate the percentage of the stable gas supply per unit time after the gas self-closing valve closes compared to the gas supply per unit time in the last time period of the first preset time. Subtract this percentage from 1 to determine the completion rate of the gas supply interruption. The closer the gas supply per unit time after the gas self-closing valve closes to the gas supply per unit time in the last time period of the first preset time, the smaller the completion rate of the gas supply interruption, indicating a higher degree of completion in stopping gas supply from the downstream gas pipeline.

[0131] The rate of gas supply interruption refers to the rate at which the downstream gas pipeline stops supplying gas during the interruption period. In some embodiments, the intelligent gas equipment management platform 130 can determine the rate of gas supply interruption using the following formula (1).

[0132]

[0133] Where V represents the rate of gas supply interruption; Q1 represents the gas supply per unit time that tends to stabilize after the gas self-closing valve closes; Q2 represents the gas supply per unit time in the last time period of the first preset time before the gas self-closing valve closes; T1 represents the closing time of the gas self-closing valve; and T2 represents the time when the gas supply in the downstream gas pipeline of the gas self-closing valve returns to stability.

[0134] In some embodiments, the intelligent gas equipment management platform 130 can determine the execution reliability by querying an execution reliability lookup table based on the comprehensive differences between standard downstream gas supply interruption characteristics and downstream gas supply interruption characteristics. In some embodiments, the execution reliability lookup table may include a correspondence between different comprehensive differences and different execution reliability levels. For example, the greater the comprehensive difference, the lower the execution reliability. The execution reliability lookup table can be pre-set based on historical data or prior knowledge.

[0135] The standard downstream gas supply interruption characteristic refers to the downstream gas supply interruption characteristic when the non-sensing element is functioning normally. The standard downstream gas supply interruption characteristic may include the standard interruption time, standard interruption completion rate, and standard interruption speed of gas in the downstream gas pipeline. In some embodiments, the standard downstream gas supply interruption characteristic can be preset based on historical experience and historical data.

[0136] In some embodiments, the intelligent gas equipment management platform 130 can determine the differences in interruption time, interruption completion rate, and interruption speed between the standard downstream gas interruption characteristics and the downstream gas interruption characteristics, and determine the weighted result of the differences in interruption time, interruption completion rate, and interruption speed as the comprehensive difference. The weighting values ​​can be system preset values ​​or user preset values. The interruption time difference is the difference between the standard interruption time and the interruption completion rate; the interruption completion rate difference is the difference between the standard interruption completion rate and the interruption speed; the interruption speed difference is the difference between the standard interruption speed and the interruption speed. A difference refers to a value in which a certain indicator in the downstream gas interruption characteristic exceeds the value of the corresponding indicator in the standard downstream gas interruption characteristic. If a certain indicator in the downstream gas interruption characteristic does not exceed the corresponding indicator in the standard downstream gas interruption characteristic, the difference is set to 0. Taking the interruption time difference as an example, the interruption time difference refers to the interruption time exceeding the value of the standard interruption time; if the interruption time does not exceed the standard interruption time, the interruption time difference is 0.

[0137] In some embodiments, in response to an execution reliability falling below a second reliability threshold, the smart gas equipment management platform 130 may issue a second early warning to the management personnel.

[0138] In some embodiments, the second reliability threshold is related to the criticality of the gas shut-off valve's setting position. For an explanation of the setting position, please refer to [link to relevant documentation]. Figure 2 And its related descriptions.

[0139] Criticality is a parameter used to characterize the importance of the installation location. The higher the criticality, the more important the installation location of the gas self-closing valve.

[0140] In some embodiments, the criticality of the location of the gas self-closing valve is related to the number of downstream gas users and surrounding equipment data. The number of downstream gas users refers to the number of gas users corresponding to the gas pipeline downstream of the gas self-closing valve. Surrounding equipment data refers to data related to the equipment surrounding the gas self-closing valve. For example, surrounding equipment data may include the type of surrounding equipment (e.g., including gas meters, gas pressure gauges, etc.), the number of surrounding equipment, and the importance level of the surrounding equipment (which can be preset by the system or manually based on the purpose of the equipment). For example, the more downstream gas users, the more surrounding equipment, and the higher the importance level of the surrounding equipment, the higher the criticality of the location. In some embodiments, the intelligent gas equipment management platform 130 can obtain surrounding equipment data through gas pipeline design drawings, user input, etc.

[0141] In some embodiments, the higher the criticality of the location of the gas self-closing valve, the greater the second reliability threshold.

[0142] In some embodiments of this specification, for gas self-closing valves with high criticality in their installation location, the second reliability threshold is increased. This is equivalent to strengthening the monitoring of the gas self-closing valve at that installation location so as to provide timely warnings. This helps managers to reasonably arrange the inspection sequence, promptly detect gas problems, and reduce the impact on gas users.

[0143] In some embodiments, the second reliability threshold is related to the overall performance score of the non-sensing elements of the gas self-closing valve.

[0144] The overall performance score is a parameter used to evaluate the overall performance of multiple non-sensing components. A higher overall performance score indicates better overall performance of the multiple non-sensing components.

[0145] In some embodiments, the intelligent gas equipment management platform 130 can determine the overall performance score of multiple non-sensing components based on their individual performance parameters. Individual performance parameters are parameters used to evaluate the performance of a single non-sensing component, including its accuracy, strength, stiffness, and durability.

[0146] In some embodiments, the intelligent gas equipment management platform 130 can determine the individual performance score of a single non-sensing element based on its individual performance parameters using a score lookup table; and determine the overall performance score of the non-sensing elements by weighting the individual performance scores of the multiple non-sensing elements included in the gas self-closing valve. In some embodiments, the score lookup table may include a correspondence between different individual performance parameters and different individual performance scores. The score lookup table may be pre-set based on historical data or prior knowledge.

[0147] In some embodiments, the higher the overall performance score of the non-sensing components of the gas self-closing valve, the larger the second reliability threshold. A higher overall performance score of the non-sensing components indicates better overall performance, meaning a better gas supply interruption effect after the gas self-closing valve closes, and a smaller tolerance for a decrease in operational reliability. In this case, increasing the second reliability threshold can strengthen monitoring, promptly detect gas problems, and reduce the impact on gas users.

[0148] In some embodiments of this specification, downstream gas supply interruption characteristics are determined by downstream gas data, and the operational reliability of non-sensing components is determined based on this. This enables accurate and efficient determination of whether to issue a second warning to management personnel, allowing management personnel to maintain non-sensing components in a timely manner, effectively improving the safety and reliability of gas supply.

[0149] Some embodiments of this specification provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions from the storage medium, the computer executes the above-described gas self-closing valve reliability monitoring method.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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. A method for monitoring the reliability of a self-closing valve based on smart gas, characterized in that, The method includes: In response to the closure of the gas self-closing valve, the system acquires operating environment data and gas user gas usage information within a first preset time period. Based on the gas usage information and the location of the gas self-closing valve, the closing type of the gas self-closing valve is determined. The closing type includes a first type and a second type. The first type is related to the gas usage situation, and the second type is related to the gas supply situation. In response to the shutdown type being the first type, an adjustment prompt is issued; In response to the closing type being the second type, the reliability of the current closing state of the gas self-closing valve is determined at least based on the operating environment data, and a warning prompt is issued based on the reliability.

2. The method as described in claim 1, characterized in that, The determination of the closing type of the gas self-closing valve based on gas usage information and the setting location of the gas self-closing valve includes: In response to the setting location being a non-user location, the closing type is determined to be the second type; In response to the setting of the location as the user's location, Determine whether the gas usage information meets preset conditions; In response to the satisfaction of the preset condition, the shutdown type is determined to be the first type; In response to the failure to meet the preset conditions, the shutdown type is determined to be the second type.

3. The method as described in claim 1, characterized in that, The reliability includes perceived reliability, the warning prompt includes a first warning prompt, the response to the shutdown type being the second type, determining the reliability of the current shutdown state of the gas self-closing valve based at least on the operating environment data, and determining whether to issue a warning prompt based on the reliability includes: In response to the closing type being the second type, The gas supply characteristics are determined based on the aforementioned operating environment data; Based on the gas supply characteristics and the sensing element data of the gas self-closing valve, the sensing reliability of the current closed state is determined; In response to the perceived reliability being lower than a first reliability threshold, the first early warning prompt is issued to the management personnel.

4. The method as described in claim 3, characterized in that, The determination of the sensing reliability of the current closed state based on the gas supply characteristics and the sensing element data of the gas self-closing valve includes: Based on the gas supply characteristics and the sensing element data, determine the first sensing reliability and / or the second sensing reliability; The perceived reliability is determined based on the first perceived reliability and / or the second perceived reliability.

5. The method as described in claim 4, characterized in that, The determination of the first sensing reliability based on the gas supply characteristics and the sensing element data includes: Based on the gas supply characteristics, a first degree of anomaly is determined; Based on the data from the sensing element, a second degree of anomaly is determined; Based on the first degree of abnormality and the second degree of abnormality, a comprehensive degree of abnormality is determined; The reliability of the first perception is determined based on the overall anomaly level.

6. The method as described in claim 4, characterized in that, The determination of the second sensing reliability based on the gas supply characteristics and the sensing element data includes: Based on the gas supply characteristics, the sensing element data, and the operating environment data, the estimated failure rate of the sensing element of the gas self-closing valve is determined by a fault prediction model, which is a machine learning model. The second perceived reliability is determined based on the estimated failure rate.

7. The method as described in claim 6, characterized in that, The input to the fault prediction model also includes downstream gas supply interruption characteristics.

8. The method as described in claim 1, characterized in that, The reliability includes execution reliability, the warning prompt includes a second warning prompt, the response to the shutdown type being the second type, determining the reliability of the current shutdown state of the gas self-closing valve based at least on the operating environment data, and determining whether to issue a warning prompt based on the reliability includes: In response to the closing type being the second type, Acquire downstream gas data within a second preset time period; Based at least on the downstream gas data, the characteristics of the downstream gas supply interruption can be determined; The execution reliability is determined based on the downstream gas supply interruption characteristics. In response to the execution reliability falling below the second reliability threshold, a second warning message is issued to the management personnel.

9. The method as described in claim 8, characterized in that, The second reliability threshold is related to the criticality of the setting position of the gas self-closing valve.

10. An IoT system for monitoring the reliability of a smart gas self-closing valve, characterized in that, The IoT system includes a smart gas user platform, a smart gas service platform, a smart gas equipment management platform, a smart gas sensor network platform, and a smart gas object platform; the smart gas equipment management platform is configured as follows: In response to the closure of the gas self-closing valve, the system acquires operating environment data and gas user gas usage information within a first preset time period. Based on the gas usage information and the location of the gas self-closing valve, the closing type of the gas self-closing valve is determined. The closing type includes a first type and a second type. The first type is related to the gas usage situation, and the second type is related to the gas supply situation. In response to the shutdown type being the first type, an adjustment prompt is issued; In response to the closing type being the second type, the reliability of the current closing state of the gas self-closing valve is determined at least based on the operating environment data, and a warning prompt is issued based on the reliability.

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