Gas data abnormity alarm method and system, terminal and readable storage medium

Abstract

The invention discloses a gas data abnormity alarm method and system, a terminal and a readable storage medium, and the method comprises the steps: obtaining gas multi-source data of environment monitoring data, cleaning the gas multi-source data, and obtaining gas cleaning data; acquiring a preset gas anomaly judgment rule, and matching the gas cleaning data with a plurality of anomaly items in the gas anomaly judgment rule to obtain an anomaly type corresponding to the gas multi-source data; and analyzing the abnormal type to obtain an analysis result, and issuing an equipment fault alarm according to the analysis result. According to the invention, by acquiring the gas monitoring data, cleaning and matching the abnormity, analyzing the abnormity type and issuing the equipment fault alarm, the safety of the equipment and the environment is ensured, and the gas data abnormity alarm is carried out in time.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a gas data abnormality alarm method, system, terminal and computer-readable storage medium. Background Art

[0002] With the popularization of urban gas supply, gas meters, as an important measurement tool, are widely used in the daily gas management of residents and industrial users. However, during long-term use, gas meters may become abnormal due to various reasons, resulting in inaccurate gas data and even safety hazards. Therefore, how to effectively detect gas data anomalies and issue alarms has become a technical problem that needs to be solved urgently.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0004] The main purpose of the present invention is to provide a gas data abnormality alarm method, system and computer-readable storage medium, aiming to solve the problem in the prior art that when analyzing gas data, it is impossible to accurately identify the occurrence of abnormalities and thus fail to issue gas data abnormality alarms in a timely manner.

[0005] To achieve the above object, the present invention provides a gas data abnormality alarm method, the gas data abnormality alarm method comprising the following steps:

[0006] Acquire gas multi-source data of environmental monitoring data, clean the gas multi-source data, and obtain gas cleansing data;

[0007] Obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a number of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data;

[0008] The abnormal type is analyzed to obtain an analysis result, and a device failure alarm is issued according to the analysis result.

[0009] Optionally, the acquiring of multi-source gas data of environmental monitoring data and cleaning of the multi-source data to obtain gas cleaning data specifically includes:

[0010] Collect environmental monitoring data, and determine whether the environmental monitoring data is abnormal according to preset data cleaning rules;

[0011] If it is determined according to the data cleaning rule that the environmental monitoring data is abnormal, the gas multi-source data of the environmental monitoring data is identified and the gas multi-source data is marked to obtain gas cleaning data.

[0012] Optionally, the gas data abnormality alarm method, wherein the step of acquiring gas multi-source data of environmental monitoring data, cleaning the multi-source data to obtain gas cleaning data, further comprises:

[0013] Extracting features from the gas cleaning data to obtain feature data;

[0014] The characteristic data is analyzed to determine abnormal data.

[0015] Optionally, in the gas data abnormality alarm method, the step of performing data analysis on the characteristic data to determine abnormal data specifically includes:

[0016] Using regression analysis, neural network and time series to generate a curve for the characteristic data to obtain a fitted gas curve;

[0017] Real-time gas data is acquired, and the fitted gas curve is compared with the real-time gas data. When the real-time gas data exceeds a preset threshold of the fitted gas curve, the data exceeding the preset threshold of the gas curve is determined to be abnormal data.

[0018] Optionally, the gas data abnormality alarm method, wherein the obtaining of a preset gas abnormality determination rule, matching the gas cleaning data with a plurality of abnormal items in the gas abnormality determination rule, and obtaining the abnormality type corresponding to the gas multi-source data, specifically includes:

[0019] Obtaining a preset gas anomaly determination rule, and matching the cleaning data with a rule of each anomaly item in the gas anomaly determination rule;

[0020] If the match is successful, the abnormal item that successfully matches the gas abnormality determination rule is defined as a target abnormal item, and the abnormal type corresponding to the gas multi-source data is determined according to the target abnormal item.

[0021] Optionally, the gas data abnormality alarm method, wherein the abnormality type is analyzed to obtain an analysis result, and an equipment fault alarm is issued according to the analysis result, specifically comprising:

[0022] According to a preset abnormal cause determination table, the abnormal type is matched and analyzed to obtain an analysis result; if the analysis result is that the gas data state is abnormal, an equipment fault alarm is issued according to the abnormal gas data state;

[0023] The equipment failure alarm includes: alarm time, alarm status, corresponding alarm strategy, alarm object and alarm content.

[0024] Optionally, the gas data abnormality alarm method further comprises:

[0025] Data monitoring is performed on the gas multi-source data of the environmental monitoring data, and when abnormal data is detected in the gas multi-source data, monitoring alarm information is sent.

[0026] In addition, to achieve the above-mentioned purpose, the present invention also provides a gas data abnormality alarm system, wherein the gas data abnormality alarm system:

[0027] A data cleaning module, used for acquiring gas multi-source data of environmental monitoring data, cleaning the gas multi-source data, and obtaining gas cleaning data;

[0028] An abnormality type determination module is used to obtain a preset gas abnormality determination rule, match the gas cleaning data with a number of abnormal items in the gas abnormality determination rule, and obtain the abnormality type corresponding to the gas multi-source data;

[0029] The analysis report generation module is used to analyze the abnormality type, obtain analysis results, and issue equipment failure alarms according to the analysis results.

[0030] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a gas data abnormality alarm program, and when the gas data abnormality alarm program is executed by a processor, the steps of the gas data abnormality alarm method as described above are implemented.

[0031] The present invention obtains gas multi-source data of environmental monitoring data, cleans the gas multi-source data, and obtains gas cleansing data; obtains preset gas anomaly determination rules, matches the gas cleansing data with several anomaly items in the gas anomaly determination rules, and obtains the anomaly type corresponding to the gas multi-source data; analyzes the anomaly type to obtain an analysis result, and issues an equipment fault alarm based on the analysis result. The present invention obtains gas monitoring data, cleans and matches anomalies, analyzes anomaly types, and issues equipment fault alarms to ensure equipment and environmental safety and to issue gas data anomaly alarms in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flow chart of a preferred embodiment of the gas data abnormal alarm method of the present invention;

[0033] Figure 2 It is a structural diagram of a preferred embodiment of the gas data abnormality alarm system of the present invention;

[0034] Figure 3 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] In the prior art, gas meters may become abnormal due to various reasons during long-term use, resulting in inaccurate gas data and even potential safety hazards, which leads to the problem of being unable to effectively detect gas data abnormalities and issue alarms. Therefore, a gas data abnormality alarm method is needed, which can accurately identify the occurrence of abnormalities and issue gas data abnormality alarms in a timely manner.

[0037] The gas data abnormality alarm method described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the gas data abnormal alarm method includes the following steps:

[0038] Step S10 acquires gas multi-source data of environmental monitoring data, cleans the gas multi-source data, and obtains gas cleansing data.

[0039] The step S10 comprises:

[0040] Step S11, collecting environmental monitoring data, and determining whether the environmental monitoring data is abnormal according to preset data cleaning rules;

[0041] Step S12: If it is determined according to the data cleaning rule that the environmental monitoring data is abnormal, the gas multi-source data of the environmental monitoring data is identified and the gas multi-source data is marked to obtain gas cleaning data.

[0042] Specifically, environmental monitoring data is collected (the method for obtaining environmental monitoring data of a specified area may be to read environmental monitoring data from an instrument end, or to obtain environmental monitoring data from a third-party platform, or to obtain environmental monitoring data in other ways, and the method for obtaining environmental monitoring data is not specifically limited herein), and it is determined whether the environmental monitoring data is abnormal according to a preset data cleaning rule; if it is determined according to the data cleaning rule that the environmental monitoring data is abnormal, the gas multi-source data of the environmental monitoring data is identified, and the gas multi-source data is marked to obtain gas cleaning data (according to the preset data cleaning rule, the gas multi-source data is cleaned to obtain gas cleaning data, and the gas cleaning data is uploaded to the server end; the data cleaning rule automatically reconfigured by the server end can take into account the differences in geographical factors and meteorological conditions between different regions, so that the gas multi-source data is cleaned according to the data cleaning rule, and the data cleaning result finally obtained is more accurate).

[0043] As an example, the collection of environmental monitoring data can expand the scope of data collection through the data middle platform project, and can support access to various gas business system data, including CIS, GIS, PS, pipeline integrity, and platform data. The data collection scope includes: data source management (supports the creation and configuration of data sources, file sources, and API sources through the interface, and saves the successfully configured data sources, file sources, and API sources for easy use next time), shared exchange (supports the creation of library and table exchange tasks through the interface, including full tasks, scheduled incremental tasks, real-time incremental tasks, file exchange tasks, and API exchange tasks), data exchange monitoring (supports monitoring of real-time incremental tasks and non-real-time incremental tasks running in the system, and monitors whether the task execution status is normal. If there is any abnormality, timely alarm) and business system docking (based on customer service, production, and business management application needs, sort out the specified business system data in batches, formulate scenario application data access plans, and complete data collection).

[0044] Furthermore, feature extraction is performed on the gas cleaning data to obtain feature data; data analysis is performed on the feature data to determine abnormal data, and a curve is generated for the feature data using regression analysis, a neural network, and a time series to obtain a fitted gas curve; real-time gas data is acquired, and the fitted gas curve is compared with the real-time gas data; when the real-time gas data exceeds a preset threshold of the fitted gas curve, the data exceeding the preset threshold of the gas curve is determined to be abnormal data (using a gas usage curve including but not limited to time series, regression analysis, neural network, and / or wavelet analysis to fit the gas usage curve, and comparing it with the actual gas data; when the actual user gas usage data exceeds the preset threshold of the fitted gas curve, the user exceeding the preset threshold of the fitted gas curve is determined to be abnormal data).

[0045] Step S20: obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a plurality of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data.

[0046] The step S20 comprises:

[0047] Step S21, using regression analysis, neural network and time series to generate a curve for the characteristic data to obtain a fitted gas curve;

[0048] Step S21, acquiring real-time gas data, comparing the fitted gas curve with the real-time gas data, and when the real-time gas data exceeds a preset threshold of the fitted gas curve, determining that the data exceeding the preset threshold of the gas curve is abnormal data.

[0049] Specifically, a preset gas anomaly determination rule is obtained (the anomaly determination rule includes: definition and management of basic standards, indicator standards, business terms, dimension standards, public codes and other standards; the anomaly determination rule also includes naming specifications, and table-level and field-level naming rules are defined and managed), and the cleaned data is matched with the rule of each anomaly item in the gas anomaly determination rule; if the match is successful, the anomaly item successfully matched with the gas anomaly determination rule is defined as a target anomaly item, and the anomaly type corresponding to the gas multi-source data is determined according to the target anomaly item, and the gas multi-source data includes: gas unstructured data, gas structured data, gas relational data and gas non-relational data (expanding the data collection scope, supporting gas multi-source data collection including unstructured and structured data, relational and non-relational databases, and real-time data).

[0050] Step S30: Analyze the abnormality type to obtain an analysis result, and issue a device failure alarm according to the analysis result.

[0051] The step S30 comprises:

[0052] Step S31, performing matching analysis on the abnormality type according to a preset abnormality cause determination table to obtain an analysis result;

[0053] Step S32: If the analysis result is that the gas data status is abnormal, an equipment fault alarm is issued according to the abnormal gas data status.

[0054] Specifically, the abnormality type is matched and analyzed according to the preset abnormality cause determination table to obtain the analysis result; if the analysis result is that the gas data status is abnormal, the equipment failure alarm is issued according to the abnormal gas data status; the equipment failure alarm includes: alarm time, alarm status, corresponding alarm strategy, alarm object and alarm content (supports monitoring overview display, supports customized configuration of alarm strategy and alarm history viewing, supports viewing the alarm history generated by the alarm strategy in the past 30 days, and the equipment failure alarm includes alarm time, alarm status, corresponding alarm strategy, alarm object, detailed alarm content, etc.).

[0055] As an example, the analysis results after analysis are stored in the target database, and the functions of the target database include: distributed storage (designed with a distributed system infrastructure to meet the massive data access of structured and unstructured data, including office documents, texts, pictures, GIS, audio and video, etc. For data with retention value, it can support the storage of massive historical data) to store analysis results, distributed computing, supporting the system to automatically divide the big data to be processed by a job (Job) into many data blocks, each data block corresponds to a computing task (Task), and automatically schedule computing nodes to process the corresponding data blocks, large-scale parallel computing (including large-scale parallel computing technology and row-column hybrid storage: including shared nothing / MPP architecture, support for row / column storage database, database compression, MapReduce, online expansion, multi-level fault tolerance, etc.) and memory computing engine (supports memory computing, uses Spark as a real-time analysis engine, and is compatible with Hadoop data sources) for computational analysis.

[0056] Furthermore, data monitoring is performed on the gas multi-source data of the environmental monitoring data, and when abnormal data is detected in the gas multi-source data, a monitoring alarm message is sent (monitoring means that one piece of environmental monitoring data is generated at every preset time interval. For example, the preset time interval can be configured to be 5 minutes, and the newly generated environmental monitoring data is processed. The above monitoring process emphasizes that the generated environmental monitoring data is processed one by one).

[0057] Furthermore, if Figure 2 As shown, based on the above-mentioned gas data abnormal alarm method, the present invention also provides a gas data abnormal alarm system accordingly, wherein the gas data abnormal alarm system includes:

[0058] A data cleaning module 51 is used to obtain gas multi-source data of environmental monitoring data, clean the gas multi-source data, and obtain gas cleaning data;

[0059] The abnormality type determination module 52 is used to obtain a preset gas abnormality determination rule, match the gas cleaning data with a number of abnormal items in the gas abnormality determination rule, and obtain the abnormality type corresponding to the gas multi-source data;

[0060] The analysis report generating module 53 is used to analyze the abnormality type, obtain analysis results, and issue a device fault alarm according to the analysis results.

[0061] Furthermore, if Figure 3 As shown, based on the above-mentioned gas data abnormality alarm method and system, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 3 Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0062] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed in the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a gas data abnormality alarm program 40 is stored on the memory 20, and the gas data abnormality alarm program 40 can be executed by the processor 10, thereby realizing the gas data abnormality alarm method in the present application.

[0063] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the gas data abnormality alarm method.

[0064] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The terminals communicate with each other via a system bus.

[0065] In one embodiment, when the processor 10 executes the gas data abnormality alarm program 40 in the memory 20, the following steps are implemented:

[0066] Acquire gas multi-source data of environmental monitoring data, clean the gas multi-source data, and obtain gas cleansing data;

[0067] Obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a number of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data;

[0068] The abnormal type is analyzed to obtain an analysis result, and a device failure alarm is issued according to the analysis result.

[0069] The step of acquiring gas multi-source data of environmental monitoring data and cleaning the multi-source data to obtain gas cleaning data specifically includes:

[0070] Collect environmental monitoring data, and determine whether the environmental monitoring data is abnormal according to preset data cleaning rules;

[0071] If it is determined according to the data cleaning rule that the environmental monitoring data is abnormal, the gas multi-source data of the environmental monitoring data is identified and the gas multi-source data is marked to obtain gas cleaning data.

[0072] The step of acquiring gas multi-source data of environmental monitoring data and cleaning the multi-source data to obtain gas cleaning data further includes:

[0073] Extracting features from the gas cleaning data to obtain feature data;

[0074] The characteristic data is analyzed to determine abnormal data.

[0075] The performing of data analysis on the characteristic data to determine abnormal data specifically includes:

[0076] Using regression analysis, neural network and time series to generate a curve for the characteristic data to obtain a fitted gas curve;

[0077] Real-time gas data is acquired, and the fitted gas curve is compared with the real-time gas data. When the real-time gas data exceeds a preset threshold of the fitted gas curve, the data exceeding the preset threshold of the gas curve is determined to be abnormal data.

[0078] The step of obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a number of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data specifically includes:

[0079] Obtaining a preset gas anomaly determination rule, and matching the cleaning data with a rule of each anomaly item in the gas anomaly determination rule;

[0080] If the match is successful, the abnormal item that successfully matches the gas abnormality determination rule is defined as a target abnormal item, and the abnormal type corresponding to the gas multi-source data is determined according to the target abnormal item.

[0081] The analyzing the abnormality type to obtain an analysis result, and issuing a device fault alarm according to the analysis result, specifically includes:

[0082] According to a preset abnormal cause determination table, the abnormal type is matched and analyzed to obtain an analysis result; if the analysis result is that the gas data state is abnormal, an equipment fault alarm is issued according to the abnormal gas data state;

[0083] The equipment failure alarm includes: alarm time, alarm status, corresponding alarm strategy, alarm object and alarm content;

[0084] The gas data abnormality alarm method further includes:

[0085] Data monitoring is performed on the gas multi-source data of the environmental monitoring data, and when abnormal data is detected in the gas multi-source data, monitoring alarm information is sent.

[0086] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a gas data abnormality alarm program, and when the gas data abnormality alarm program is executed by a processor, the steps of the gas data abnormality alarm method described above are implemented.

[0087] In summary, the present invention provides a method, system, terminal and readable storage medium for abnormal alarm of gas data, the method comprising: obtaining gas multi-source data of environmental monitoring data, cleaning the gas multi-source data to obtain gas cleaning data; obtaining preset gas abnormality determination rules, matching the gas cleaning data with several abnormal items in the gas abnormality determination rules to obtain the abnormality type corresponding to the gas multi-source data; analyzing the abnormality type to obtain the analysis result, and issuing the equipment fault alarm according to the analysis result. The present invention obtains gas monitoring data, cleans and matches the abnormality, analyzes the abnormality type, and issues the equipment fault alarm to ensure the safety of equipment and environment, and to issue the gas data abnormality alarm in time.

[0088] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or terminal system including the element.

[0089] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0090] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A gas data abnormal alarm method, characterized in that: The gas data abnormality alarm method comprises: Acquire gas multi-source data of environmental monitoring data, clean the gas multi-source data, and obtain gas cleansing data; Obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a number of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data; The abnormal type is analyzed to obtain an analysis result, and a device failure alarm is issued according to the analysis result.

2. The gas data abnormal alarm method according to claim 1, characterized in that: The step of acquiring gas multi-source data of environmental monitoring data and cleaning the multi-source data to obtain gas cleaning data specifically includes: Collect environmental monitoring data, and determine whether the environmental monitoring data is abnormal according to preset data cleaning rules; If it is determined according to the data cleaning rule that the environmental monitoring data is abnormal, the gas multi-source data of the environmental monitoring data is identified and the gas multi-source data is marked to obtain gas cleaning data.

3. The gas data abnormal alarm method according to claim 1, characterized in that: The step of obtaining gas multi-source data of environmental monitoring data and cleaning the multi-source data to obtain gas cleaning data further includes: Extracting features from the gas cleaning data to obtain feature data; The characteristic data is analyzed to determine abnormal data.

4. The gas data abnormality alarm method according to claim 3, characterized in that: The performing data analysis on the characteristic data to determine abnormal data specifically includes: Using regression analysis, neural network and time series to generate a curve for the characteristic data to obtain a fitted gas curve; Real-time gas data is acquired, and the fitted gas curve is compared with the real-time gas data. When the real-time gas data exceeds a preset threshold of the fitted gas curve, the data exceeding the preset threshold of the gas curve is determined to be abnormal data.

5. The gas data abnormal alarm method according to claim 1, characterized in that: The step of obtaining a preset gas anomaly determination rule, matching the gas cleaning data with a plurality of anomaly items in the gas anomaly determination rule, and obtaining an anomaly type corresponding to the gas multi-source data specifically includes: Obtaining a preset gas anomaly determination rule, and matching the cleaning data with a rule of each anomaly item in the gas anomaly determination rule; If the match is successful, the abnormal item that successfully matches the gas abnormality determination rule is defined as a target abnormal item, and the abnormal type corresponding to the gas multi-source data is determined according to the target abnormal item.

6. The gas data abnormal alarm method according to claim 1, characterized in that: The analyzing the abnormality type to obtain an analysis result, and issuing a device fault alarm according to the analysis result specifically includes: Perform matching analysis on the abnormality type according to a preset abnormality cause determination table to obtain an analysis result; If the analysis result is that the gas data status is abnormal, an equipment fault alarm is issued according to the abnormal gas data status; The equipment failure alarm includes: alarm time, alarm status, corresponding alarm strategy, alarm object and alarm content.

7. The gas data abnormality alarm method according to claim 1, characterized in that: The gas data abnormality alarm method further includes: Data monitoring is performed on the gas multi-source data of the environmental monitoring data, and when abnormal data is detected in the gas multi-source data, monitoring alarm information is sent.

8. A gas data abnormality alarm system, characterized in that: The gas data abnormality alarm system comprises: A data cleaning module, used for acquiring gas multi-source data of environmental monitoring data, cleaning the gas multi-source data, and obtaining gas cleaning data; An abnormality type determination module is used to obtain a preset gas abnormality determination rule, match the gas cleaning data with a number of abnormal items in the gas abnormality determination rule, and obtain the abnormality type corresponding to the gas multi-source data; The analysis report generation module is used to analyze the abnormality type, obtain analysis results, and issue equipment failure alarms according to the analysis results.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and a gas data abnormality alarm program stored in the memory and executable on the processor. When the gas data abnormality alarm program is executed by the processor, the steps of the gas data abnormality alarm method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a gas data abnormality alarm program, and when the gas data abnormality alarm program is executed by a processor, the steps of the gas data abnormality alarm method according to any one of claims 1 to 7 are implemented.

Images