Gas anomaly judgment method and smart gas internet of things system for realizing safe gas use

Abstract

The embodiment of the present specification provides a gas anomaly judgment method and a smart gas Internet of Things system for realizing safe gas use. The method is realized by the smart gas Internet of Things system, the system comprises a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor equipment sensing network platform and a smart gas indoor equipment object platform, the method is executed by the smart gas safety management platform, and comprises the following steps: receiving request information of a target user, the request information comprising a request of the target user on gas anomaly cause analysis; based on the request information, extracting user data, based on the user data, extracting gas data; based on the user data and the gas data, determining analysis information of the gas anomaly cause.

Description

Technical Field

[0001] This manual relates to the field of the Internet of Things (IoT), and in particular to a method for detecting gas anomalies and a smart gas IoT system for achieving safe gas usage. Background Technology

[0002] Natural gas is an essential component of a modern urban infrastructure system. Developing urban natural gas can significantly improve heat energy utilization efficiency, which is not only a necessity for urban modernization but also an important measure to conserve energy, protect the urban environment, and improve people's living standards.

[0003] With the continuous development of my country's gas industry, gas has become a common energy source for every household. However, users may encounter various problems when using gas, such as gas stoves failing to ignite or gas water heaters failing to heat water. Because there are many reasons for gas malfunctions, users sometimes cannot quickly and accurately determine the cause of the malfunction, making it easy to miss or even apply the wrong solution, thus creating potential gas safety hazards.

[0004] Therefore, a more efficient method is urgently needed to help users accurately and quickly determine the cause of gas abnormalities and generate effective solutions. Summary of the Invention

[0005] This specification provides one or more embodiments of a method for determining gas anomalies to ensure safe gas usage. This method is implemented by a smart gas IoT system, which includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensor network platform, and a smart gas indoor device object platform. The method is executed by the smart gas safety management platform and includes: receiving a request from a target user, the request including the target user's request for analysis of the cause of the gas anomaly; extracting user data based on the request; extracting gas data based on the user data; and determining analytical information for the cause of the gas anomaly based on the user data and the gas data.

[0006] One embodiment of this specification provides a smart gas IoT system. The system comprises a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor device sensor network platform, and a smart gas indoor device object platform. The smart gas safety management platform includes a smart gas indoor safety management sub-platform and a smart gas data center. The smart gas indoor safety management sub-platform and the smart gas data center interact bidirectionally. The smart gas safety management platform is configured to perform the following operations: the smart gas data center receives target user data from the smart gas user platform based on the smart gas service platform. The system receives user request information, including the target user's request for analysis of the cause of gas anomalies. The smart gas in-home safety management sub-platform is used to: extract user data based on the request information; obtain gas data extracted by the smart gas in-home device object platform through the smart gas in-home device sensor network platform based on the user data; determine analysis information for the cause of gas anomalies based on the user data and the gas data; and send the analysis information for the cause of gas anomalies to the smart gas data center. The smart gas data center then sends the analysis information for the cause of gas anomalies to the smart gas user platform via the smart gas service platform.

[0007] This specification provides one or more embodiments of a gas anomaly detection device for safe gas use, including a processor, the processor being used to execute a gas anomaly detection method for safe gas use.

[0008] 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 a gas anomaly detection method to ensure safe gas usage. 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 a schematic diagram of the platform structure of a smart gas IoT system according to some embodiments of this specification;

[0011] Figure 2 This is an exemplary flowchart of a method for determining gas abnormalities according to some embodiments of this specification;

[0012] Figure 3 This is an exemplary flowchart illustrating the analysis information for determining the cause of gas anomalies according to some embodiments of this specification;

[0013] Figure 4 This is an exemplary flowchart illustrating the analysis information for determining the cause of gas anomalies using a preset algorithm, as shown in some embodiments of this specification.

[0014] Figure 5 This is an exemplary structural diagram of a multi-classification model shown in some embodiments of this specification. Detailed Implementation

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

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

[0017] 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.

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

[0019] An Internet of Things (IoT) system is an information processing system that includes some or all of the following platforms: a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The user platform is the functional platform for acquiring user-sensed information and generating control information. The service platform connects the management platform and the user platform, providing communication services for both sensing and control information. The management platform coordinates and manages the connections and collaboration between the various functional platforms (such as the user platform and the service platform). The management platform aggregates information from the IoT operating system and provides sensing and control management functions. The service platform connects the management platform and the object platform, providing communication services for both sensing and control information. The user platform is the functional platform for acquiring user-sensed information and generating control information.

[0020] Information processing in an IoT system can be divided into user-perceived information processing and control information processing. Control information can be generated based on user-perceived information. In some embodiments, control information may include user-demand control information, and user-perceived information may include user query information. The processing of perception information involves the object platform acquiring the perception information and transmitting it to the management platform via the sensor network platform. User-demand control information is transmitted from the management platform to the user platform via the service platform, thereby controlling the sending of prompt information.

[0021] Figure 1 This is a schematic diagram of the platform structure of a smart gas IoT system according to some embodiments of this specification. For example... Figure 1 As shown, the smart gas IoT system 100 includes a smart gas user platform 110, a smart gas service platform 120, a smart gas safety management platform 130, a smart gas indoor equipment sensor network platform 140, and a smart gas indoor equipment object platform 150.

[0022] In some embodiments, the smart gas IoT system 100 can receive user request information when the gas equipment used by the user malfunctions, process the user request information and the gas data of the gas equipment used by the user, determine the cause of the gas malfunction, and feed back the analysis information of the cause of the gas malfunction to the user, thereby helping the user to accurately and quickly determine the cause of the gas malfunction and find a solution even when lacking relevant professional knowledge.

[0023] The smart gas user platform 110 can refer to a platform used to acquire user request information and provide feedback on the analysis information of gas abnormality causes to the user. In some embodiments, the smart gas user platform 110 can be configured as a terminal device, such as a mobile phone, tablet, or computer. In some embodiments, the smart gas user platform 110 can interact with the smart gas service platform 120, acquiring and sending user request information to the smart gas service platform 120. For example, the smart gas user platform 110 can acquire the user's request information, "The gas stove is not igniting, please check the cause," through the terminal device and send it to the smart gas service platform 120 for querying. In some embodiments, the smart gas user platform 110 can receive the analysis information of gas abnormality causes uploaded by the smart gas service platform 120 and provide feedback to the user.

[0024] In some embodiments, the smart gas user platform 110 may include a gas user sub-platform 111 and a regulatory user sub-platform 112. The gas user sub-platform 111 may refer to a platform that provides gas users with gas data and analytical information on the causes of gas anomalies. In some embodiments, the gas user sub-platform 111 may correspond to and interact with the smart gas service sub-platform 121 to obtain safe gas usage services. The regulatory user sub-platform 112 may refer to a platform that monitors the operation of the smart gas IoT system 100 for regulatory users. In some embodiments, the regulatory user sub-platform 112 may correspond to and interact with the smart regulatory service sub-platform 122 to obtain services for safety regulatory needs.

[0025] For more information on user requests, analysis of gas anomaly causes, and gas data, please refer to [link / reference]. Figure 2 And its detailed description.

[0026] The smart gas service platform 120 can refer to a platform used for receiving and transmitting data and / or information. In some embodiments, the smart gas service platform 120 can interact with the smart gas user platform 110, receiving user request information issued by the smart gas user platform 110, and uploading analysis information on the causes of gas anomalies to the smart gas user platform 110. In some embodiments, the smart gas service platform 120 can interact with the smart gas safety management platform 130, sending user request information to the smart gas safety management platform 130, and receiving analysis information on the causes of gas anomalies uploaded by the smart gas safety management platform 130.

[0027] In some embodiments, the smart gas service platform 120 may include a smart gas consumption service sub-platform 121 and a smart regulatory service sub-platform 122. In some embodiments, the smart gas consumption service sub-platform 121 may correspond to the gas user sub-platform 111, providing gas users with safe gas consumption services. In some embodiments, the smart regulatory service sub-platform 122 may correspond to the regulatory user sub-platform 112, providing regulatory users with services to meet their safety regulatory needs.

[0028] The intelligent gas safety management platform 130 can refer to a platform that coordinates and integrates the connections and collaborations between various functional platforms, gathers all the information from the Internet of Things (IoT), and provides sensing, management, and control functions for the IoT operating system. In some embodiments, the intelligent gas safety management platform 130 can be configured to receive request information from a target user, including a request from the target user regarding the analysis of the causes of gas anomalies; extract user data based on the request information; extract gas data based on the user data; and determine the analytical information for the causes of gas anomalies based on the user data and the gas data.

[0029] In some embodiments, the smart gas safety management platform 130 may include a smart gas indoor safety management sub-platform 131 and a smart gas data center 132. In some embodiments, the smart gas indoor safety management sub-platform 131 can interact bidirectionally with the smart gas data center 132, and the smart gas indoor safety management sub-platform 131 can obtain and feed back safety management data (such as user data, gas data, analysis information on the causes of gas anomalies, etc.) from the smart gas data center 132.

[0030] In some embodiments, the intelligent gas indoor safety management sub-platform 131 may include an intrinsically safe monitoring and management module 1311. In some embodiments, the intrinsically safe monitoring and management module 1311 can be used to monitor gas safety-related information. For example, the intrinsically safe monitoring and management module 1311 can monitor gas explosion-proof safety-related information such as mechanical leaks in gas terminals, electrical power consumption (e.g., intelligent control power consumption, communication power consumption), and valve control. In some embodiments, the intrinsically safe monitoring and management module 1311 can preset a safety monitoring threshold. If the gas safety-related data (e.g., gas data) received by the intrinsically safe monitoring and management module 1311 from the intelligent gas data center 132 exceeds the threshold, the intrinsically safe monitoring and management module 1311 will automatically trigger an alarm and may optionally automatically push the alarm information to the gas user sub-platform 111 and the regulatory user sub-platform 112. In some embodiments, the intelligent gas indoor safety management sub-platform 131 may also include other safety monitoring and management modules (e.g., information security monitoring and management module, functional safety monitoring and management module). Different safety monitoring and management modules can perform different functions, and no limitation is made here.

[0031] In some embodiments, the information interaction between the smart gas safety management platform 130 and the upper-level smart gas service platform 120 and the lower-level smart gas indoor device sensor network platform 140 is all through the smart gas data center 132. The smart gas data center 132 can aggregate and store all operational data of the IoT operating system. In some embodiments, the smart gas data center 132 can receive user request information issued by the smart gas service platform 120, and send the user data and gas data extracted based on the user request information to the smart gas indoor safety management sub-platform 131 for analysis and processing. The smart gas indoor safety management sub-platform 131 sends the processed data to the smart gas data center 132, and the smart gas data center 132 then sends the aggregated and processed data (e.g., analysis information on the causes of gas anomalies) to the smart gas service platform 120. In some embodiments, the smart gas data center 132 can issue instructions to obtain gas anomaly-related information (e.g., whether there is a gas leak) to the smart gas indoor device sensor network platform 140, and receive gas anomaly-related information uploaded by the smart gas indoor device sensor network platform 140.

[0032] For more information on target users and user data, see [link to relevant information]. Figure 2 For a detailed description and more information on analytical methods for determining the causes of gas anomalies, please refer to [link / reference]. Figure 2-5 And its detailed description.

[0033] The smart gas indoor equipment sensor network platform 140 can refer to a platform that uniformly manages sensor communication. In some embodiments, the smart gas indoor equipment sensor network platform 140 can be configured as a communication network and gateway. The smart gas indoor equipment sensor network platform 140 can employ multiple gateway servers or multiple smart routers, without further limitation.

[0034] In some embodiments, the smart gas indoor device sensor network platform 140 can connect to the smart gas safety management platform 130 and the smart gas indoor device object platform 150 to realize the functions of sensing and communication of perception information and control information. In some embodiments, the smart gas indoor device sensor network platform 140 can interact with the smart gas indoor device object platform 150, receive gas anomaly-related information uploaded by the smart gas indoor device object platform 150, and send instructions to obtain gas anomaly-related information to the smart gas indoor device object platform 150. In some embodiments, the smart gas indoor device sensor network platform 140 can interact with the smart gas safety management platform 130, receive instructions from the smart gas safety management platform 130 to obtain gas anomaly-related information, and upload gas anomaly-related information to the smart gas safety management platform 130.

[0035] The smart gas indoor device object platform 150 can refer to a platform used to obtain information related to gas anomalies. In some embodiments, the smart gas indoor device object platform 150 can be configured as various gas-related devices, such as indoor gas appliances, gas safety detection devices, etc. In some embodiments, the smart gas indoor device object platform 150 can interact with the smart gas indoor device sensor network platform 140, receive instructions from the smart gas indoor device sensor network platform 140 to obtain information related to gas anomalies, and upload information related to gas anomalies to the smart gas indoor device sensor network platform 140.

[0036] In some embodiments, the smart gas indoor equipment object platform 150 may include a fair metering equipment object sub-platform 151, a safety monitoring equipment object sub-platform 152, and a safety valve control equipment object sub-platform 153. In some embodiments, the smart gas indoor equipment object platform 150 can obtain gas anomaly-related information through the aforementioned object sub-platforms. For example, the safety monitoring equipment object sub-platform 152 (e.g., a gas concentration detection device) can detect whether a gas leak exists.

[0037] Some embodiments in this specification build a smart gas IoT system 100 through a five-platform IoT functional architecture, and adopt a combination of a main platform and sub-platforms. This not only reduces the data processing pressure of the main platform, but also ensures the independence of each data, ensuring data classification and transmission, traceability, and the classification, issuance, and processing of instructions. This makes the IoT structure and data processing clear and controllable, and facilitates IoT management and data processing.

[0038] Figure 2 This is an exemplary flowchart illustrating a method for determining gas anomalies according to some embodiments of this specification. In some embodiments, process 200 may be executed by a smart gas safety management platform 130. Figure 2 As shown, process 200 may include the following steps:

[0039] Step 210: The intelligent gas safety management platform receives the request information from the target user, which includes the target user's request for analysis of the causes of gas anomalies.

[0040] The target user can refer to the user who has experienced a gas abnormality.

[0041] In some embodiments, the request information may also include user-uploaded image data. This user image data may refer to image materials related to gas abnormalities, such as pictures, videos, and / or audio.

[0042] A request for analysis of the cause of a gas malfunction can refer to an instruction issued by a target user requesting analysis and determination of the cause of a gas malfunction. For example, if a target user experiences a gas malfunction but is unable to determine the cause in a timely and accurate manner, they can issue a request for analysis of the cause of the gas malfunction.

[0043] Step 220: The intelligent gas safety management platform extracts user data based on the request information, and extracts gas data based on the user data.

[0044] User data can refer to the data of the target user themselves. For example, user data may include, but is not limited to, the target user's location information, the target user's gas usage type, and / or the target user's gas meter number information.

[0045] Gas data refers to data related to a target user's gas usage. For example, gas data may include, but is not limited to, gas balance, data on gas pipelines at all levels involved with the target user, the target user's gas usage data, and / or gas anomaly data. Gas usage data may refer to data related to gas consumption, frequency of use, and / or duration of use, while gas anomaly data may include the number, frequency, and / or duration of gas anomalies.

[0046] Gas data can be determined by the intelligent gas safety management platform based on user data.

[0047] Step 230: The intelligent gas safety management platform analyzes the information to determine the cause of gas anomalies based on user data and gas data.

[0048] The analysis information for the causes of gas anomalies can refer to data related to the analysis of gas anomalies for the target user. For example, the analysis information may include at least one type of anomaly cause, such as primary and secondary causes. It may also include gas anomaly causes with high certainty and / or those with low certainty. Furthermore, the analysis information may include the location of the gas anomaly and its probability of occurrence, where the location may include the user terminal and / or pipeline terminal. For instance, when a gas stove fails to start normally, the analysis information could indicate a malfunction in the gas stove ignition device and / or a clogged gas nozzle, with corresponding probabilities of 75% and 25%, respectively.

[0049] For further explanation regarding the determination of the cause of gas abnormalities, please refer to other parts of this manual (e.g., Figure 3 , Figure 5 (and related descriptions).

[0050] The probability of occurrence refers to the likelihood of a gas malfunction. It's understandable that there can be multiple causes for a gas malfunction, and the probability of occurrence will vary depending on the cause.

[0051] In some embodiments, the intelligent gas safety management platform can use various methods such as statistical analysis, rule bases, preset algorithms, modeling, and / or mathematical calculations to determine analytical information for the causes of gas anomalies. For example, the intelligent gas safety management platform can establish a preset rule base to determine the type and certainty of the causes of gas anomalies through preset rules. As another example, the intelligent gas safety management platform can use preset algorithms to determine the type and probability of occurrence of the causes of gas anomalies. For further explanation on how to determine analytical information for the causes of gas anomalies, please refer to other parts of this specification (e.g., Figure 4 (and related content).

[0052] In some embodiments of this specification, the intelligent gas safety management platform analyzes user-uploaded request information and other relevant data, and combines this with the platform's own data to quickly and accurately determine the causes of gas anomalies and their probability of occurrence. This allows the platform to provide timely and effective solutions to users who lack gas-related professional knowledge, thus meeting their needs.

[0053] 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.

[0054] Figure 3 This is an exemplary flowchart illustrating the analysis information for determining the cause of gas anomalies according to some embodiments of this specification. In some embodiments, process 300 may be executed by the intelligent gas safety management platform 130. Figure 3 As shown, process 300 may include the following steps:

[0055] Step 310: The intelligent gas safety management platform extracts preset rules based on the rule base.

[0056] A rule base can refer to a knowledge base composed of various rules. Preset rules can refer to rules that are manually set in advance to determine whether user data and gas data meet certain conditions. For more information on user data and gas data, please see [link to relevant documentation]. Figure 2And related descriptions. In some embodiments, the preset rules may include rules related to the level of certainty in determining the cause of the gas anomaly. For example, different preset rules may be used for different user data and gas data extraction, thereby determining the cause of the gas anomaly and its level of certainty for different situations. For another example, for the user's gas balance in the user data, and the preset rule is "the user's gas balance is less than 0", if the user's gas balance in the user data meets this rule, then the cause of the gas anomaly can be determined as the user's arrears, with a level of certainty of 100%.

[0057] Step 320: The intelligent gas safety management platform uses a rule-based judgment engine to determine the cause of the candidate gas anomaly and to determine its certainty level.

[0058] A rule-based judgment engine can refer to an engine that, based on specified filtering conditions contained in preset rules, determines whether the data matches the real-time conditions at the time of operation and executes the actions specified in the rules. In some embodiments, the rule-based judgment engine is used to determine whether user data and gas data meet preset rules.

[0059] Candidate gas anomaly causes refer to the reasons why the gas being processed by the engine is abnormal, which will be determined by the rules. For more information on gas anomaly causes, see [link to relevant documentation]. Figure 2 And its related descriptions.

[0060] The level of certainty can refer to the degree of certainty regarding the cause of the gas anomaly. In some embodiments, the level of certainty can be expressed as a percentage or a grade (e.g., grades I-V). For example, if the cause of the gas anomaly is insufficient user balance, which is directly certain, then the level of certainty for this gas anomaly could be 100% or grade V. As another example, if the cause of the gas anomaly is a pipeline leak, which is a conclusion drawn from data analysis but requires further investigation, then the level of certainty for this gas anomaly might be 60% or grade III.

[0061] In some embodiments, the intelligent gas safety management platform can use a rule-based judgment engine to determine the causes of candidate gas anomalies and assess their level of certainty. For example, the intelligent gas safety management platform may use a rule-based judgment engine to determine that the cause of the gas anomaly is pipeline maintenance and assess its level of certainty as 100%. As another example, the intelligent gas safety management platform may use a rule-based judgment engine to determine that the cause of the gas anomaly is a pipeline terminal malfunction and assess its level of certainty as 50% based on the probability of the anomaly occurring.

[0062] Step 330: The intelligent gas safety management platform determines whether the certainty level meets the first preset condition.

[0063] The first preset condition can refer to a pre-set condition used to determine whether further data needs to be obtained and the cause of the gas anomaly needs to be analyzed. For example, the first preset condition could be a certainty level greater than or equal to 90%. Another example is that the first preset condition could be a certainty level greater than or equal to Level IV.

[0064] Step 340: In response to the non-compliance, the intelligent gas safety management platform uses pipeline information and user terminal information to determine the analysis information of the cause of gas abnormality through a preset algorithm.

[0065] Pipeline information can refer to information related to gas pipelines. In some embodiments, pipeline information may include pipeline gas information and pipeline terminal information. Pipeline gas information may refer to information related to the gas in the pipeline, such as gas density, pressure, and flow direction. Pipeline terminal information may refer to information related to pipeline terminal equipment, such as whether the gas valve is open.

[0066] User terminal information refers to information related to a user's gas terminal equipment. For example, whether the gas meter is operating normally, or whether the gas stove is damaged.

[0067] In some embodiments, pipeline information and user terminal information can be determined based on user data and gas data. For more information on user data and gas data, see [link to relevant documentation]. Figure 2 And its related descriptions.

[0068] In some embodiments, the intelligent gas safety management platform can determine the analytical information for the causes of gas anomalies through preset algorithms. For more information on preset algorithms, please refer to [link to relevant documentation]. Figure 4 And its related descriptions.

[0069] Step 350, the response then directly determines the analysis information for the cause of the gas anomaly. In some embodiments, when the first preset condition is met, the rule-based judgment engine determines candidate causes of the gas anomaly and judges their certainty level, further determining the analysis information for the cause of the gas anomaly. For details regarding the analysis information for the cause of the gas anomaly, please refer to... Figure 2 The corresponding description.

[0070] In some embodiments of this specification, the determination of the certainty level determines whether the smart gas safety management platform should directly report the cause of the gas anomaly to the user or obtain other data to continue analyzing the cause of the gas anomaly. This ensures that the user receives accurate and effective information on the cause of the gas anomaly, while also reducing the operational load on the smart gas safety management platform.

[0071] Figure 4This is an exemplary flowchart illustrating the analysis of gas anomaly causes determined by a preset algorithm according to some embodiments of this specification. In some embodiments, process 400 may be executed by the intelligent gas safety management platform 130. Figure 4 As shown, process 400 may include the following steps:

[0072] Step 410: Construct a graph based on pipeline information and user terminal information.

[0073] In some embodiments, the nodes of the graph may include pipeline terminal nodes and user terminal nodes, and the edges of the graph may be gas pipelines between nodes.

[0074] For more information on pipeline information and user terminal information, please refer to [link / reference]. Figure 3 And its related descriptions.

[0075] A pipeline terminal node can refer to a node established based on the connection points of pipelines at various levels. For example, a pipeline terminal node can be a node established based on the connection point between the main pipeline and a branch pipeline. Another example is a node established based on the connection point between a branch pipeline and the inlet pipeline. The attributes of a pipeline terminal node can include the node's anomaly score, gas usage data, and gas anomaly data.

[0076] A node's anomaly score can be considered a score related to the likelihood of the node becoming abnormal. Understandably, the higher the node's anomaly score, the greater the likelihood of it becoming abnormal.

[0077] For more information on gas usage data and gas anomaly data, please refer to other sections of this manual (e.g., Figure 2 (and related descriptions).

[0078] A user terminal node can refer to a node established based on user terminal information. For example, a user terminal node can include gas-using terminal devices, such as gas stoves, gas water heaters, and / or gas furnaces. The attributes of a user terminal node can include the node's anomaly score, gas usage data, user image data, and gas anomaly data.

[0079] For more information on user image data, see [link to relevant documentation]. Figure 2 And its related descriptions.

[0080] Edges reflect the connection relationships between different adjacent nodes. When two nodes are connected by a gas pipeline, they can be connected by an edge, where the direction of the edge can be the gas delivery direction. In some embodiments, the attributes of the edge may include a weight value and gas flow information.

[0081] The weight value can reflect the importance of an edge and the frequency of gas anomalies occurring at its two endpoints. Understandably, the higher the importance of an edge, the more times its two endpoints experience anomalies within a given timeframe, and the larger the edge's weight value.

[0082] In some embodiments, the importance of an edge can be determined based on factors such as the pipeline's grade and / or transport capacity. For example, if the main pipeline has a higher grade than a branch pipeline, then the main pipeline is more important, and the edge corresponding to the main pipeline has a larger weight value than the edge corresponding to the branch pipeline. Similarly, if branch pipeline 1 has a stronger transport capacity than branch pipeline 2, then branch pipeline 1 is more important, and the edge corresponding to branch pipeline 1 has a larger weight value.

[0083] Step 420: Analyze the graph based on a preset algorithm to determine the anomaly score of the nodes.

[0084] A preset algorithm can refer to a pre-defined algorithm used to analyze and process graph structures. Preset algorithms can include, but are not limited to, one or more methods such as statistical analysis, induction, logical transformation, and / or mathematical calculation.

[0085] Step 430: The abnormal score of the node is updated through continuous iteration until it meets the second preset condition, at which point the iteration stops.

[0086] In some embodiments, the intelligent gas safety management platform can iterate the graph at least once using a preset algorithm, continuously updating the abnormal scores of nodes until the iteration meets the preset conditions, at which point the iteration ends, and the abnormal score of the last updated node is taken as the final score.

[0087] In some embodiments, the process of iteratively calculating the anomaly score for each node is as follows: In each iteration, for each node, based on the node's anomaly score to be updated, the anomaly scores to be updated for other nodes directly connected to the node, and the weights of the edges between the node and other connected nodes, the updated anomaly score for the node is determined. This updated anomaly score is then used as the node's anomaly score to be updated in the next iteration. Specifically, in the first iteration, the node's anomaly score to be updated is the node's initial anomaly score, which can be determined based on the node's gas usage data and gas anomaly data.

[0088] For example, the algorithm for updating the anomaly score of the i-th node in the j-th round could be:

[0089]

[0090] Among them, V i ′ represents the updated anomaly score of the node, i.e., the anomaly score to be updated in the next iteration; V i and Vk Let represent the anomaly scores to be updated for the i-th and k-th nodes in this round; p and q are weight coefficients, which can be determined by the intelligent gas safety management platform based on the attributes of the nodes and edges in the graph; k represents the number of nodes connected to the i-th node by an edge, and K represents the number of nodes connected to the i-th node by an edge; R ki This represents the weight value of the edge between the i-th node and the k-th node.

[0091] The iteration ends when the second preset condition is met. The second preset condition may include function convergence, the abnormal score of a certain node reaching a threshold, and / or the number of iterations reaching a threshold.

[0092] In some embodiments, gas usage data and gas anomaly data can also be updated based on feedback information from the target user. This feedback information can refer to relevant behavioral information of the target user based on nodes and edges. For example, if a target user restarts the gas stove but fails to start, the gas anomaly data for the user's terminal node corresponding to this gas stove is updated, and the anomaly score for that node is appropriately increased. As another example, if a user restarts the gas stove but fails to start, and then starts the gas water heater and finds it works normally, the anomaly score for the user's terminal node corresponding to the gas water heater remains unchanged, but the weight of the edge corresponding to the gas water heater is reduced to minimize the impact of normal data, focusing on the data of edges corresponding to nodes that may have anomalies, thus improving the algorithm's iteration efficiency.

[0093] Understandably, the weight of an edge reflects the frequency of gas malfunctions at both ends of the edge. When the frequency of gas malfunctions at the gas stove increases while the frequency of gas malfunctions at the gas water heater remains constant, the weight of the edge corresponding to the gas stove increases. Conversely, with the total weight remaining constant, the weight of the edge corresponding to the gas water heater decreases, thus increasing attention to the edges corresponding to the gas stove, which has a high frequency of gas malfunctions.

[0094] In some embodiments, when gas anomaly data in the attributes of multiple peer nodes is updated, the anomaly score of their common parent node increases. Here, peer nodes are nodes with the same or similar characteristics, and the common parent node is the next higher-level node directly connected to each of the peer nodes by an edge. For example, a gas stove and a gas water heater are the same node, and the inlet pipe connecting the gas stove and the gas water heater is its common parent node. As another example, when both the gas stove and the gas water heater fail to start, the gas anomaly data of their respective user terminal nodes is updated, and consequently, the anomaly score of the common parent node of these two user terminal nodes, i.e., the node corresponding to the inlet pipe, increases. As yet another example, when five target users report gas anomalies, the anomaly score of the common parent node corresponding to these five target users, i.e., the node corresponding to the branch pipe connecting to the community, increases.

[0095] It is understandable that when multiple peer nodes experience gas anomalies, it is very likely that the anomaly is caused by a gas anomaly in their corresponding common superior node. By appropriately increasing the anomaly score of the common superior node, the result determined by the preset algorithm can be more accurate and more in line with the actual situation.

[0096] In some embodiments of this specification, the intelligent gas safety management platform uses a preset algorithm to analyze and process a graph based on gas data, pipeline information, and user terminal information. This allows for the rapid determination of the anomaly scores of multiple nodes, thereby determining the likelihood of an anomaly occurring. This meets user needs and lays the foundation for further analysis to determine the type of gas anomaly.

[0097] In some embodiments, the intelligent gas safety management platform can also utilize a variety of feasible methods to predict the type of abnormal cause and the probability of occurrence of various abnormalities based on the attributes of the current node, neighboring nodes, and neighboring edges.

[0098] The attributes of the current node and its neighboring nodes include the updated anomaly scores of the nodes. For example, a smart gas safety management platform can use methods such as statistical analysis, cluster analysis, and / or modeling to predict the types of anomalies and the probability of occurrence for each type of anomaly.

[0099] In some embodiments, the anomaly score of the updated node can be determined based on a preset algorithm. For further explanation of the preset algorithm, see [link to relevant documentation]. Figure 4 And its related descriptions.

[0100] In some embodiments, the intelligent gas safety management platform can utilize predictive models to predict the classification of abnormal causes and the probability of occurrence of each type of abnormal cause.

[0101] Figure 5 This is an exemplary structural diagram of a multi-classification model shown in some embodiments of this specification.

[0102] In some embodiments, the prediction model may include a multi-classification model, a convolutional neural network, or a deep neural network, or a combination thereof.

[0103] like Figure 5As shown, the input to the prediction model 530 may include multiple node attributes 510 and multiple edge attributes 520, and its output may include anomaly cause classification and corresponding probability 540. The anomaly classification and corresponding probability 540 may include anomaly cause classifications and the probability of anomaly occurrence for multiple different nodes, for example, anomaly cause classification and corresponding probability 1, anomaly cause classification and corresponding probability 2, ... anomaly cause classification and corresponding probability n. In some embodiments, node attributes 510 may include updated node anomaly scores, and edge attributes 520 may include updated edge weight values. For further explanation of the updated node anomaly scores and updated edge weight values, please refer to this specification. Figure 4 And its related descriptions.

[0104] In some embodiments, the prediction model 530 can be obtained by training it separately.

[0105] In some embodiments, a prediction model 530 can be obtained by acquiring multiple training samples and training them based on the multiple training samples and their corresponding labels. The training samples may include multiple sample node attributes and multiple sample edge attributes, and the labels may include the anomaly cause classification and corresponding probability corresponding to the aforementioned samples. In some embodiments, training samples and labels can be obtained based on historical data, for example, based on historical node attributes, edge attributes, and their corresponding anomaly causes.

[0106] The training samples are input into the initial prediction model. A loss function is constructed based on the output and label of the initial prediction model. The parameters of the initial prediction model are updated through the loss function until the trained initial prediction model meets the preset conditions, and the trained prediction model 530 is obtained. The preset conditions can be that the loss function is less than a threshold, convergence, or the training period reaches a threshold, etc.

[0107] In some embodiments of this specification, by using a trained prediction model to analyze and process the node and edge attributes of the updated graph, the type of anomaly cause and the probability of each anomaly cause occurring can be determined relatively quickly and accurately, thus meeting user needs in a timely manner.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 judging gas anomalies to achieve safe gas use, characterized in that, The method is implemented using a smart gas IoT system, which includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor equipment sensor network platform, and a smart gas indoor equipment object platform. The method is executed by the smart gas safety management platform and includes: Receive request information from the target user, the request information including the target user's request for analysis of the cause of gas abnormality; Based on the request information, extract user data, and based on the user data, extract gas data; Based on the user data and the gas data, analytical information is used to determine the cause of the gas anomaly, including the location of the gas anomaly and its probability of occurrence. The analytical information used to determine the cause of the gas anomaly includes: Based on the rule base, preset rules are extracted, and a rule judgment engine is used to determine the candidate causes of gas anomalies and to determine their certainty level; wherein, the rule judgment engine is used to determine whether the user data and the gas data meet the preset rules, and the certainty level refers to the level of certainty of the cause of the gas anomaly; Determine whether the certainty level meets the first preset condition. If it does not meet the condition, determine the analysis information of the cause of the gas anomaly based on pipeline information and user terminal information through a preset algorithm. The pipeline information and user terminal information are determined based on the user data and the gas data. The pipeline information includes pipeline gas information and pipeline terminal information. The analytical information used to determine the cause of the gas anomaly through the preset algorithm includes: A graph is constructed based on the pipeline information and the user terminal information. The nodes of the graph include pipeline terminal nodes and user terminal nodes. The edges of the graph represent the gas pipelines between the nodes, and the direction of the edges represents the gas delivery direction. The attributes of the pipeline terminal nodes include the node's anomaly score, gas usage data, and gas anomaly data. The attributes of the user terminal nodes include the node's anomaly score, gas usage data, user image data, and gas anomaly data. The attributes of the edges include weight values ​​and gas flow information. The graph is analyzed based on the preset algorithm to determine the anomaly score of each node. When the gas anomaly data in the attributes of multiple peer nodes is updated, the anomaly score of the common parent node is increased. The peer nodes are nodes with the same or similar characteristics, and the common parent node is the next-level node that is directly connected to the multiple peer nodes by an edge.

2. The method as described in claim 1, characterized in that, Also includes: Based on the attributes of the current node, neighboring nodes, and adjacent edges, the classification of anomaly causes and the probability of occurrence of each type of anomaly are predicted; wherein, the attributes of the current node and the neighboring nodes include the updated anomaly score of the node.

3. A smart gas IoT system, characterized in that, The system includes a smart gas user platform, a smart gas service platform, a smart gas safety management platform, a smart gas indoor equipment sensor network platform, and a smart gas indoor equipment object platform. The smart gas safety management platform includes a smart gas indoor safety management sub-platform and a smart gas data center. The smart gas indoor safety management sub-platform and the smart gas data center interact bidirectionally. The smart gas safety management platform is configured to perform the following operations: The smart gas data center receives request information from the smart gas user platform based on the smart gas service platform. The request information includes the target user's request for analysis of the causes of gas anomalies. The intelligent gas indoor safety management sub-platform is used for: Based on the request information, user data is extracted, and based on the user data, gas data extracted by the smart gas indoor equipment object platform is obtained through the smart gas indoor equipment sensor network platform. Based on the user data and the gas data, the analysis information for the cause of the gas anomaly is determined and sent to the smart gas data center; the smart gas data center sends the analysis information for the cause of the gas anomaly to the smart gas user platform via the smart gas service platform, and the analysis information includes the location of the gas anomaly and its probability of occurrence; The analytical information used to determine the cause of the gas anomaly includes: Based on the rule base, preset rules are extracted, and a rule judgment engine is used to determine the candidate causes of gas anomalies and to determine their certainty level; wherein, the rule judgment engine is used to determine whether the user data and the gas data meet the preset rules, and the certainty level refers to the level of certainty of the cause of the gas anomaly; Determine whether the certainty level meets the first preset condition. If it does not meet the condition, determine the analysis information of the cause of the gas anomaly based on pipeline information and user terminal information through a preset algorithm. The pipeline information and user terminal information are determined based on the user data and the gas data. The pipeline information includes pipeline gas information and pipeline terminal information. The analytical information used to determine the cause of the gas anomaly through the preset algorithm includes: A graph is constructed based on the pipeline information and the user terminal information. The nodes of the graph include pipeline terminal nodes and user terminal nodes. The edges of the graph represent the gas pipelines between the nodes, and the direction of the edges represents the gas delivery direction. The attributes of the pipeline terminal nodes include the node's anomaly score, gas usage data, and gas anomaly data. The attributes of the user terminal nodes include the node's anomaly score, gas usage data, user image data, and gas anomaly data. The attributes of the edges include weight values ​​and gas flow information. The graph is analyzed based on the preset algorithm to determine the anomaly score of each node. When the gas anomaly data in the attributes of multiple peer nodes is updated, the anomaly score of the common parent node is increased. The peer nodes are nodes with the same or similar characteristics, and the common parent node is the next-level node that is directly connected to the multiple peer nodes by an edge.

4. The system as described in claim 3, characterized in that, The intelligent gas safety management platform is configured to further perform the following operations: Based on the attributes of the current node, neighboring nodes, and adjacent edges, the classification of anomaly causes and the probability of occurrence of each type of anomaly are predicted; wherein, the attributes of the current node and the neighboring nodes include the updated anomaly score of the node.

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