Method and device for real-time storage and intelligent alarm of abnormal data of Internet of Things gas meters

Abstract

The present application discloses a method and device for real-time storage and intelligent warning of abnormal data of an Internet of Things gas meter, relating to the technical field of gas safety monitoring and intelligent alarm. The main technical solution is as follows: collect the instant status data corresponding to the target gas meter, where the instant status data includes an instant flow value, an instant temperature value, and an instant pressure value; determine whether the target gas meter meets the abnormal warning condition based on the instant status data, and the abnormal warning condition is used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, and the target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter; if it is satisfied, store the instant status data as abnormal data in a non-volatile memory in real time and upload it to the cloud, and perform corresponding intelligent warning operations according to the abnormal warning level. The purpose is to improve the accuracy of abnormal warning to ensure instant storage and upload of relevant abnormal data to the cloud.

Description

Technical Field

[0001] This application relates to the technical field of gas safety monitoring and intelligent alarm, and particularly relates to a method and device for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter. Background Art

[0002] As an important product in the field of intelligent gas, the Internet of Things gas meter realizes efficient data transmission and precise control relying on the Internet of Things technology, greatly promoting the optimization and upgrading of gas services and the modern management of smart cities.

[0003] Currently, the Internet of Things gas meters in the prior art can automatically upload the local data in the non-volatile memory to the cloud through the remote transmission module according to the set frequency on the premise that the non-volatile memory is intact, so as to realize data interaction with the cloud. However, this mechanism has significant limitations: one is that the judgment of sudden abnormal situations (such as fires) is not comprehensive and accurate enough, and the other is that when encountering sudden abnormal situations (such as fires), since the non-volatile memory in the Internet of Things gas meter may be damaged and unable to store relevant abnormal data, it is difficult to report alarms in time or perform operations such as cutting off the gas supply, seriously affecting the emergency response efficiency and the investigation ability after the accident. Summary of the Invention

[0004] In view of the above problems, this application provides a method and device for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter, and the main purpose is to improve the accuracy of abnormal alarms to ensure that relevant abnormal data is immediately stored and uploaded to the cloud.

[0005] To solve the above technical problems, this application proposes the following solutions:

[0006] In a first aspect, this application provides a method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter, which is applied to an Internet of Things gas meter with a locally deployed non-volatile memory. The method includes:

[0007] Collect the instant status data corresponding to the target gas meter, where the instant status data includes an instant flow value, an instant temperature value, and an instant pressure value, and the target gas meter is an Internet of Things gas meter selected as the monitoring target;

[0008] Based on the instant status data, determine whether the target gas meter meets the abnormal alarm condition, where the abnormal alarm condition is used to represent that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, and the target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter;

[0009] If the condition is met, the instant status data is stored in the non-volatile memory in real time as abnormal data and uploaded to the cloud, and corresponding intelligent alarm operations are performed according to the abnormal alarm level, where the abnormal alarm level is determined according to the over-limit parameter of the instant status data exceeding the target threshold.

[0010] In a second aspect, the present application provides an apparatus for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter, which is applied to an Internet of Things gas meter with a locally deployed non-volatile memory. The apparatus includes:

[0011] An acquisition unit for acquiring instant status data corresponding to a target gas meter, where the instant status data includes an instant flow value, an instant temperature value, and an instant pressure value, and the target gas meter is an Internet of Things gas meter selected as a monitoring target;

[0012] A judgment unit for judging whether the target gas meter meets the abnormal alarm condition based on the instant status data, where the abnormal alarm condition is used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, and the target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter;

[0013] An alarm unit for, if the condition is met, storing the instant status data in the non-volatile memory in real time as abnormal data and uploading it to the cloud, and performing corresponding intelligent alarm operations according to the abnormal alarm level, where the abnormal alarm level is determined according to the over-limit parameter of the instant status data exceeding the target threshold.

[0014] To achieve the above object, according to a third aspect of the present application, a storage medium is provided. The storage medium includes a stored program, where when the program runs, it controls the device where the storage medium is located to execute the method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter in the first aspect above.

[0015] To achieve the above object, according to a fourth aspect of the present application, a processor is provided. The processor is used to run a program, where when the program runs, it executes the method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter in the first aspect above.

[0016] With the above technical solution, a method and device for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by this application first collect the instant status data corresponding to the target gas meter. The instant status data includes an instant flow value, an instant temperature value, and an instant pressure value. The target gas meter is an Internet of Things gas meter selected as the monitoring target. Then, based on the instant status data, it is determined whether the target gas meter meets the abnormal alarm condition. The abnormal alarm condition is used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold. The target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter. Finally, if it is satisfied, the instant status data is stored in the non-volatile memory in real time as abnormal data and uploaded to the cloud, and corresponding intelligent alarm operations are performed according to the abnormal alarm level. The abnormal alarm level is determined according to the over-limit parameter of the instant status data exceeding the target threshold. Through the technical solution provided by this application, after collecting instant status data such as the instant flow, instant temperature, and instant pressure corresponding to the gas meter, the target threshold required for the instant status data can be dynamically compensated by the geographical environment and season, so that the target threshold can more accurately reflect the influence of different geographical locations and seasonal changes on the operation of the gas meter, better adapt to the complex and changeable actual environment, reduce the possibility of false alarms and missed alarms, thereby improving the accuracy of abnormal alarms. After determining the abnormal alarm, the instant status data can be stored in the non-volatile memory in real time as abnormal data and uploaded to the cloud, ensuring the security and integrity of the abnormal data, and an abnormal alarm level can be determined according to the over-limit parameter of the instant status data exceeding the target threshold to perform corresponding alarm operations, such as adjusting the frequency of uploading data to the cloud, sending an alarm work order request, a gas source cut-off request, activating a local audible and visual alarm, etc. This not only enhances the monitoring and response capabilities for abnormal situations but also provides strong data support for post-event investigations.

[0017] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the specific implementation manners of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the detailed description of the preferred embodiments below, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of this application. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0019] Figure 1 Shows a flowchart of a method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by an embodiment of this application;

[0020] Figure 2 It shows a flowchart of another method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by an embodiment of the present application;

[0021] Figure 3 It shows a block diagram of the composition of an apparatus for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by an embodiment of the present application;

[0022] Figure 4 It shows a block diagram of the composition of another apparatus for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by an embodiment of the present application. Detailed implementation manners

[0023] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0024] Currently, an Internet of Things gas meter in the prior art can automatically upload local data in a non-volatile memory to the cloud through a remote transmission module at a set frequency on the premise that the non-volatile memory is intact, so as to realize data interaction with the cloud. However, this mechanism has significant limitations: one is that the determination of sudden abnormal situations (such as fires) is not comprehensive and accurate enough, and the other is that when encountering sudden abnormal situations (such as fires), since the non-volatile memory in the Internet of Things gas meter may be damaged, relevant abnormal data cannot be stored, resulting in difficulty in reporting alarms in a timely manner or performing operations such as cutting off the gas supply, seriously affecting the emergency response efficiency and the investigation ability after an accident.

[0025] For this reason, an embodiment of the present application provides a method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter, which is applied to an Internet of Things gas meter locally deployed with a non-volatile memory. Through this method, the accuracy of abnormal alarms can be improved to ensure that relevant abnormal data is immediately stored and uploaded to the cloud. The specific implementation steps are as Figure 1 shown and include:

[0026] 101. Collect the instant status data corresponding to the target gas meter.

[0027] In this step, the target gas meter refers to the IoT gas meter selected as the monitoring target. The instant status data includes instant flow value, instant temperature value, and instant pressure value. In practical applications, temperature sensors and pressure sensors can be pre-implanted at the hardware end of the target gas meter, and the instant temperature value and instant pressure value of the target gas meter can be monitored in real time through the temperature sensors and pressure sensors. And through the flow sensor of the target gas meter itself, the instant flow value of the target gas meter can be monitored. Specifically, the data of the sensors can be automatically obtained at preset time intervals (for example, once a minute), that is, the instant flow value, instant temperature value, and instant pressure value are obtained as the instant status data corresponding to the target gas meter, so as to perform the abnormality judgment in step 102.

[0028] 102. Judge whether the target gas meter meets the abnormal alarm condition based on the instant status data.

[0029] Among them, the abnormal alarm condition is used to represent that at least one of the instant flow value, instant temperature value, and instant pressure value exceeds its respective target threshold, and the target threshold is a dynamic adaptive threshold for geographical environment-season coupling compensation of the target gas meter. In this step, for the instant flow value, instant temperature value, and instant pressure value in the instant status data, basic thresholds can be preset according to historical data and industry standards, including basic flow threshold, basic temperature threshold, and basic pressure threshold. The target threshold is obtained by dynamically adjusting the above basic thresholds considering the influence of the geographical environment and seasonal changes of the location where the target gas meter is located on temperature, pressure, and flow. Specifically, it includes flow determination threshold, temperature determination threshold, and pressure determination threshold. Specifically, geographical environment factors such as altitude and surrounding building density index can be considered to pre-calculate a terrain complexity coefficient of the location where the target gas meter is located. At the same time, adjustment coefficients corresponding to different seasons are preset according to seasonal changes, and the two are introduced into the dynamic adaptive adjustment of the basic flow threshold, basic temperature threshold, and basic pressure threshold. At this time, since the pressure is also affected by the pipeline distance from the pressure regulating station and the pipeline material, and the flow is also affected by holidays, for the dynamic adaptive adjustment of the basic pressure threshold, the pipeline distance and pipeline material can also be introduced to further reflect the influence of the geographical environment. For the dynamic adaptive adjustment of the basic flow threshold, a holiday correction coefficient can be set and introduced to accurately predict and respond to the flow changes in different time periods, avoiding abnormal alarms caused by special periods such as holidays, so as to obtain the flow determination threshold, temperature determination threshold, and pressure determination threshold that can reflect the actual environmental conditions of the location where the target gas meter is located.

[0030] In addition, machine learning algorithms can be used to analyze a large amount of historical data and real-time data, predict the environmental change trend in a future period of time, and dynamically adjust the above-mentioned basic thresholds according to the environmental change trend. Specifically, a large amount of historical data is collected from Internet of Things gas meters, weather stations and other relevant sensors, including but not limited to temperature, pressure, flow rate, geographical location information, timestamp, etc. Based on the historical data, features that are helpful for predicting the environmental change trend are extracted. For example: time series features: average temperature, maximum temperature, minimum temperature, etc. in the past few days, geographical location features: altitude, building density index, etc., seasonal features: current month, whether it is a holiday, etc. Statistical methods or automated feature selection algorithms (such as recursive feature elimination method, LASSO regression, etc.) are used to determine which features are most helpful for the model. An appropriate algorithm is selected according to the nature of the problem. For time series prediction problems, long short-term memory network (LSTM), recurrent neural network (RNN), or gradient boosting decision tree (GBDT) can be considered. The cleaned data set is divided into a training set and a validation set, and the selected model is trained using the training set and its performance is evaluated on the validation set. The hyperparameters of the model are optimized through methods such as grid search or random search to obtain the best performance. The trained model is used to predict the environmental change trend in a future period of time. For example, predict the temperature, pressure and flow rate changes in the next few days. Based on the prediction results, the target thresholds of each parameter are dynamically adjusted. For example: if it is predicted that the temperature will drop significantly in the next few days, the temperature determination threshold is appropriately lowered to prevent false alarms; if it is expected that there will be a large increase in gas consumption demand, the flow rate determination threshold is correspondingly increased to avoid unnecessary alarms.

[0031] After obtaining the flow rate determination threshold, temperature determination threshold and pressure determination threshold, the instant flow rate value can be compared with the flow rate determination threshold, the instant temperature value with the temperature determination threshold, and the instant pressure value with the pressure determination threshold respectively to judge the over-limit situation among them. If there is no over-limit situation in these three parameters of flow rate, temperature and pressure, that is, there is no over-limit parameter, it means that the target gas meter does not meet the abnormal alarm condition. At this time, it is only necessary to wait for the next automatic acquisition of the instant status data of the sensor at a preset time interval (for example, once a minute). If there is an over-limit situation in these three parameters of flow rate, temperature and pressure, that is, it means that there is at least one over-limit parameter, it means that the target gas meter meets the abnormal alarm condition. At this time, step 103 is executed.

[0032] 103. Store the instant status data as abnormal data in the non-volatile memory in real time and upload it to the cloud, and perform corresponding intelligent alarm operations according to the abnormal alarm level.

[0033] Among them, the abnormal alarm level is determined according to the over-limit parameters of the real-time status data exceeding the target threshold. In this step, when it is confirmed that the target gas meter meets the abnormal alarm conditions, it indicates that there are abnormal situations in the current flow rate, temperature, and pressure of the target gas meter. At this time, the data protection mechanism can be immediately activated to store the latest real-time status data as abnormal data in the non-volatile memory in real time and upload it to the cloud, so as to ensure that even in extreme situations (such as fires), data loss can be avoided and the security and integrity of the data can be guaranteed. In order to ensure that the cloud can receive the above abnormal data, gas usage data and other local data in the non-volatile memory more timely, the frequency of uploading local data from the non-volatile memory to the cloud can be increased, that is, switched from the first basic frequency to the second emergency frequency, and the second emergency frequency is higher than the first basic frequency. Exemplarily, assuming that the basic frequency is once per hour and the emergency frequency is once every ten minutes, to ensure that the cloud can receive the latest local data in the non-volatile memory in time. In order to notify the operation and maintenance personnel to conduct on-site verification and processing and prevent the accident from expanding, a remote alarm work order request and a gas source cut-off request can be sent to the cloud. And in order to remind users to pay attention to potential dangers and take corresponding safety measures, the corresponding audible and visual alarm warning of the target gas meter can also be activated, that is, activate the audible and visual alarm device installed on the target gas meter to make it continuously emit warnings.

[0034] It should be noted that in order to improve the accuracy of abnormal alarms, the intelligent alarm operation in step 103 is further refined. A multi-level alarm mechanism can also be introduced. Specifically, during the comparison process of the above three parameters of flow rate, temperature, and pressure, the parameter exceeding the target threshold is regarded as the over-limit parameter, and the number of over-limit parameters (the number of over-limit items) and the difference from the target threshold are counted as the over-limit intensity. Based on the number of over-limit items and the over-limit intensity, the corresponding abnormal alarm level is determined, such as the warning level, the emergency level, and the disaster level, etc. Among them, the "warning level" indicates that there are potential problems with the target gas meter, but the current situation does not pose a direct threat. The "emergency level" indicates that the target gas meter faces relatively serious risks and immediate attention and corresponding measures need to be taken to avoid accidents. The "disaster level" indicates that the target gas meter is in an extremely high-risk state and a serious accident may occur soon, and immediate emergency measures must be taken. Specifically, when the abnormal alarm level is the "warning level", the frequency of uploading the non-volatile memory to the cloud can be switched from the first basic frequency (for example, once per hour) to the second emergency frequency (for example, once every 10 minutes) to increase the frequency, so that the cloud can collect and analyze local data including abnormal data and gas usage data faster and more completely. When the abnormal alarm level is the "emergency level", on the basis of performing the "warning level" actions, the target gas meter can be controlled to send a remote alarm work order request or a gas source cut-off request to the cloud, etc., in order to notify the operation and maintenance personnel to verify and handle on-site and prevent the expansion of the accident. When the abnormal alarm level is the "disaster level", on the basis of performing the "warning level" actions, the audible and visual alarm device installed on the target gas meter can be activated to remind the user to pay attention to potential dangers and take corresponding safety measures, further protecting the user's life and property safety.

[0035] Based on the above Figure 1From the implementation method, it can be seen that through the above technical solution, a method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter provided by this application first collects the instant status data corresponding to the target gas meter. The instant status data includes instant flow value, instant temperature value, and instant pressure value. The target gas meter is an Internet of Things gas meter selected as the monitoring target. Then, it is determined whether the target gas meter meets the abnormal alarm condition based on the instant status data. The abnormal alarm condition is used to characterize that at least one of the instant flow value, instant temperature value, and instant pressure value exceeds its respective target threshold. The target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter. Finally, if it is satisfied, the instant status data is stored in the non-volatile memory in real time as abnormal data and uploaded to the cloud, and corresponding intelligent alarm operations are performed according to the abnormal alarm level. The abnormal alarm level is determined according to the over-limit parameter of the instant status data exceeding the target threshold. Through the technical solution provided by this application, after collecting instant status data such as instant flow, instant temperature, and instant pressure corresponding to the gas meter, the target threshold required for the instant status data is dynamically compensated by the geographical environment and season, so that the target threshold can more accurately reflect the impact of different geographical locations and seasonal changes on the operation of the gas meter, better adapt to the complex and changeable actual environment, reduce the possibility of false alarms and missed alarms, thereby improving the accuracy of abnormal alarms. After determining the abnormal alarm, the instant status data can be stored in the non-volatile memory in real time as abnormal data and uploaded to the cloud, ensuring the security and integrity of the abnormal data. And an abnormal alarm level can be determined according to the over-limit parameter of the instant status data exceeding the target threshold to perform corresponding alarm operations, such as adjusting the frequency of uploading data to the cloud, sending alarm work order requests, gas source cut-off requests, activating local sound and light alarms, etc. This not only enhances the monitoring and response capabilities for abnormal situations but also provides strong data support for post-event investigations.

[0036] Further, the preferred embodiment of this application is a detailed description of the process of real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter on the basis of the above Figure 1 The specific steps are as follows Figure 2 shown and include:

[0037] 201. Collect the instant status data corresponding to the target gas meter.

[0038] This step combines the description of step 101 in the above method, and the same content will not be repeated here.

[0039] 202. Obtain the target threshold corresponding to the target gas meter.

[0040] Among them, the target threshold includes a flow determination threshold, a temperature determination threshold, and a pressure determination threshold.

[0041] It should be noted that the specific execution process for obtaining the target threshold corresponding to the target gas meter is as follows: Obtain the basic threshold corresponding to the target gas meter, where the basic threshold includes the basic flow threshold, the basic temperature threshold, and the basic pressure threshold; calculate the terrain complexity coefficient corresponding to the target gas meter based on the altitude and the surrounding building density index of the location where the target gas meter is located; obtain the seasonal adjustment coefficient corresponding to the current month of the location where the target gas meter is located; obtain the monthly average temperature and the standard deviation of temperature fluctuations of the location where the target gas meter is located, and adjust the basic temperature threshold based on the terrain complexity coefficient, the monthly average temperature, and the standard deviation of temperature fluctuations to obtain the temperature determination threshold; obtain the pipeline distance between the target gas meter and the nearest pressure regulating station and the pipeline material identification, and adjust the basic pressure threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, the pipeline distance, and the pipeline material identification to obtain the pressure determination threshold; obtain the holiday correction coefficient corresponding to the current month of the location where the target gas meter is located, and adjust the basic flow threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, and the holiday correction coefficient to obtain the flow determination threshold.

[0042] In this step, the basic flow threshold can be set according to the gas type and user requirements, and the basic temperature threshold can be set based on the equipment specifications and safety standards, and the basic pressure threshold can be set according to the pipeline material, design specifications, etc.

[0043] Obtain the altitude H of the location where the target gas meter is located from the Geographic Information System (GIS), and calculate the building density index within a certain range around it through satellite images or urban planning data , and calculate the corresponding terrain complexity coefficient based on the two . The specific expression is:

[0044] ;

[0045] Among them, represents the terrain complexity coefficient. H represents the altitude. represents the building density index. and represent the weights of the altitude and the building density index respectively.

[0046] Determine the seasonal adjustment coefficient according to the current month. The specific expression is:

[0047] ;

[0048] Adaptive adjustment for the basic temperature threshold:

[0049] Obtain the meteorological data of the same period in the past 5 years at the location of the target gas meter from the meteorological database, and calculate the monthly average temperature and the standard deviation of temperature fluctuations for the current month based on this. Introduce the terrain complexity coefficient, monthly average temperature, and standard deviation of temperature fluctuations into the adaptive adjustment of the basic temperature threshold. The specific expression is:

[0050] ;

[0051] Among them, is the basic temperature threshold. Considers the difference between the current month and the monthly average temperature. Then reflects the standard deviation of temperature fluctuations and its relationship with the terrain complexity coefficient relationship.

[0052] Adaptive adjustment for the basic pressure threshold:

[0053] Obtain the actual pipeline distance from the target gas meter to the nearest pressure regulating station from the geographic information system , and set the identifier M according to the actual pipeline material (M = 1 for metal pipelines, M = 0 for polyethylene pipelines). Introduce the terrain complexity coefficient , seasonal adjustment coefficient , pipeline distance and pipeline material identifier M into the adaptive adjustment of the basic pressure threshold. The specific expression is:

[0054] ;

[0055] Among them, is the basic pressure threshold. is the actual pipeline distance from the target gas meter to the nearest pressure regulating station. is used to consider the influence of the pipeline distance on the pressure. As the pipeline distance increases, the pressure loss increases. is used to consider the influence of the terrain complexity coefficient on the pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of the pipeline distance on the pressure. As the pipeline distance increases, the pressure decays exponentially. is used to consider the influence of the terrain complexity coefficient on the pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of the seasonal adjustment coefficient on the pressure.

[0056] Adaptive adjustment for the basic pressure threshold:

[0057] Calculate the holiday correction coefficient based on the number of legal holidays in the current month . The specific expression is:

[0058] ;

[0059] The terrain complexity coefficient, seasonal adjustment coefficient, and holiday correction coefficient are introduced into the adaptive adjustment of the basic threshold, and the specific expression is:

[0060] ;

[0061] Among them, is the basic flow threshold. is used to reflect the influence of the seasonal adjustment coefficient on the flow rate, is used to reflect the influence of holidays on the flow rate, is used to consider the influence of the terrain complexity coefficient. The more complex the terrain, the higher the flow threshold.

[0062] Through the above expressions, comprehensively considering the direct or indirect influences of factors such as seasonality, holidays, and terrain complexity on temperature, pressure, and flow rate, the flow determination threshold, temperature determination threshold, and pressure determination threshold can be set more accurately, better adapting to various situations, reducing false alarms and missed alarms. Moreover, the more accurate flow determination threshold, temperature determination threshold, and pressure determination threshold are helpful for timely detecting abnormal situations, reducing unnecessary maintenance work, thereby reducing the overall operation cost. By real-time monitoring and dynamically adjusting the threshold, potential safety hazards of the target gas meter can be detected earlier, thereby improving the accuracy of abnormal alarms to ensure immediate storage and upload of relevant abnormal data to the cloud.

[0063] It should be noted that during the long-term use of the gas meter, it will face situations such as aging and maintenance, which usually have an adverse impact on the actual state of the gas meter. In order to more accurately reflect the actual state of the equipment and improve the accuracy of subsequent abnormal detection. Before obtaining the basic threshold corresponding to the target gas meter, it also includes: determining the aging correction coefficient of the target gas meter according to the usage duration and startup times corresponding to the target gas meter; determining the maintenance correction coefficient of the target gas meter according to the maintenance times and maintenance intervals corresponding to the target gas meter; calculating the comprehensive correction coefficient of the target gas meter based on the aging correction coefficient and maintenance correction coefficient, and correcting the basic flow threshold, basic temperature threshold, and basic pressure threshold respectively based on the comprehensive correction coefficient to obtain the basic threshold.

[0064] In this step, the usage duration (T) and startup times (S) of the target gas meter are collected in advance. According to the preset standard intervals, the corresponding correction coefficients are determined. For example, the usage duration can be divided into several intervals: [0 - 5000 hours], [5000 - 10000 hours], [> 10000 hours], and each interval corresponds to different coefficient values ; Similarly, the startup times can also be divided into different intervals, and each interval corresponds to a different coefficient Calculate the aging correction coefficient based on the usage duration and the number of startups, specifically as follows: , and specifically, a simple linear combination can be assumed: .

[0065] Similarly, pre-collect the number of repairs (R) and the average repair interval (I) of the target gas meter. The number of repairs and the repair interval can also be divided into different intervals according to the actual situation, and specific adjustment coefficients are specified for each interval and Calculate the repair correction coefficient based on the number of repairs and the average repair interval, specifically as follows: , and specifically, a simple linear combination can be assumed: .

[0066] Calculate the comprehensive correction coefficient based on the above aging correction coefficient and repair correction parameters: . By adopting the square root method to balance the weights of the two and avoid the influence of extreme values. For a given base flow threshold , base temperature threshold and base pressure threshold perform corrections respectively to obtain the base thresholds for subsequent use.

[0067] By introducing correction coefficients based on factors such as usage duration, number of startups, number of repairs, and repair interval, the actual health status of the gas meter can be more accurately reflected, which helps to identify potential problems at an early stage and take preventive measures, thereby reducing the failure rate. The changes in environmental conditions in different seasons will affect the normal operation of the gas meter. On this basis, further combining the dynamic compensation of environment-season can ensure that the abnormal judgment is more in line with the actual situation and improve the accuracy and adaptability of abnormal detection.

[0068] 203. Compare the flow value with the flow judgment threshold, the temperature value with the temperature judgment threshold, and the pressure value with the pressure judgment threshold respectively to obtain the comparison results.

[0069] Among them, the comparison results include the number of overlimit items corresponding to the overlimit parameters.

[0070] In this step, obtain the instant flow value, instant temperature value, and instant pressure value of the target gas meter, as well as the dynamically adaptive thresholds calculated in step 202, namely the corresponding flow determination threshold, temperature determination threshold, and pressure determination threshold. For each parameter among flow, temperature, and pressure, compare its instant value with the corresponding determination threshold. If the instant value of a certain parameter exceeds its determination threshold, record this parameter as an over-limit parameter. Count the number of all over-limit parameters to obtain the number of over-limit items. If the number of over-limit items is zero, that is, none of the three parameters of flow, temperature, and pressure exceeds its corresponding determination threshold, then execute step 204. On the contrary, if the number of over-limit items is not zero, that is, one or more of the three parameters of flow, temperature, and pressure exceed their respective determination thresholds, then execute step 205.

[0071] 204. If the number of over-limit items is zero, it is determined that the target gas meter does not meet the abnormal alarm condition.

[0072] In this step, since there are no over-limit parameters among the three parameters of flow, temperature, and pressure, that is, there are no over-limit parameters, at this time, it can be considered that the target gas meter does not meet the abnormal alarm condition.

[0073] 205. If the number of over-limit items is not zero, it is determined that the target gas meter meets the abnormal alarm condition.

[0074] In this step, since there are over-limit parameters among the three parameters of flow, temperature, and pressure, that is, there is one or more over-limit parameters, at this time, it can be considered that the target gas meter meets the abnormal alarm condition. 206. Calculate the comprehensive risk value of the target gas meter based on the number of over-limit items and the over-limit intensity.

[0075] The comparison result in the above step 203 also includes the over-limit intensity corresponding to the over-limit parameter. This over-limit intensity represents the degree to which the over-limit parameter exceeds its determination threshold and is used to evaluate the risk level subsequently. Specifically, it can be calculated by subtracting the determination threshold from the instant value, dividing the result by the determination threshold, and then multiplying by 100% to obtain an over-limit intensity in the form of a percentage.

[0076] It should be noted that the specific implementation process of calculating the comprehensive risk value of the target gas meter based on the number of over-limit items and the over-limit intensity is as follows: Obtain the importance weight corresponding to the number of over-limit items; when the number of over-limit items is one, calculate the single-item collaborative risk coefficient according to the over-limit intensity corresponding to the single over-limit parameter, and calculate the comprehensive risk value based on the importance weight corresponding to the single over-limit item and the single-item collaborative risk coefficient; when the number of over-limit items is multiple, calculate the multiple-item collaborative risk coefficient by multiplying the number of over-limit items and the over-limit intensity corresponding to the multiple over-limit parameters, and determine the comprehensive risk value based on the importance weights corresponding to the multiple over-limit parameters respectively, the multiple-item collaborative risk coefficient, and the abnormal superposition risk coefficient of the multiple over-limit parameters.

[0077] In this step, the number of over-limit items X represents the quantity of over-limit parameters, and the over-limit intensity represents the degree to which the over-limit parameter exceeds its judgment threshold. Since the over-limit parameter may be one item or multiple items, when there are multiple over-limit parameters, considering that the types (flow rate, temperature or pressure) of multiple over-limit parameters are different, their abnormal changes have different impacts on the risk assessment of the gas meter. Therefore, different importance weights can be assigned to them in advance. For example, the temperature / pressure parameter has sudden danger and is assigned a higher weight , and the flow rate parameter is relatively safer and is assigned a lower weight .

[0078] When only one parameter is over-limit ( ), the single-item collaborative risk coefficient can be calculated according to the over-limit intensity , and combined with the importance weight to obtain the comprehensive risk value of the target gas meter. The specific expression is:

[0079] ;

[0080] ;

[0081] Among them, represents the single-item collaborative risk coefficient. represents the comprehensive risk value of the single-item collaborative risk coefficient. 50 is a preset constant. By dividing by 50, the over-limit intensity is converted into a relatively small proportional value, which helps to control the influence range of different over-limit intensities on the collaborative effect. When two or more over-limit parameters are over-limit simultaneously ( ), considering the amplification effect of the interaction between these over-limit parameters on the risk, the multiple-item collaborative risk coefficient can be calculated specifically by the way of multiplication. The specific expression is:

[0082] ;

[0083] Among them, represents the over-limit intensity of the i-th over-limit parameter is converted to obtain a standardized adjustment factor. 50 is a preset constant. By dividing by 50, the over-limit intensity is converted into a relatively small proportional value, which helps to control the influence range of different over-limit intensities on the collaborative effect. is the multiple-item collaborative risk coefficient, and by multiplying the is obtained by subtracting 1, which is used to reflect the risk amplification factor brought by multiple over-limit parameters due to simultaneous over-limit. Add the weighted over-limit intensities of each over-limit parameter, and superimpose them with the multiple collaborative risk coefficients and then add a fixed coefficient multiplied by the square of the number of over-limit items (such as 0.3) to strengthen the risk of abnormal superposition of multiple parameters, and obtain the comprehensive risk value of the target gas meter , and the specific expression is:

[0084] .

[0085] Among them, represents the weighted average over-limit intensity of all over-limit parameters. represents the risk amplification factor of the basic risk plus the synergistic effect. If , then this part is equal to 1, indicating that there is no additional synergistic effect; if , it means that there is a synergistic effect and the risk will be further amplified. represents the product of the square of the number of over-limit items and the fixed coefficient, emphasizing the risk of abnormal superposition of multiple over-limit parameters. When multiple over-limit parameters exceed the limit simultaneously, it will significantly increase the comprehensive risk value. is a fixed coefficient used to strengthen the risk of abnormal superposition of multiple over-limit parameters.

[0086] 207. If the comprehensive risk value does not exceed the first risk threshold, the abnormal alarm level is determined to be the early warning level.

[0087] In this step, if the calculated comprehensive risk value is lower than the set first risk threshold, the abnormal alarm level is determined to be the "early warning level". The "early warning level" indicates that there are potential problems with the target gas meter, but the current situation does not pose a direct threat. It is recommended to strengthen monitoring and take preventive measures.

[0088] 208. If the comprehensive risk value exceeds the first risk threshold but does not exceed the second risk threshold, the abnormal alarm level is determined to be the emergency level.

[0089] In this step, if the calculated comprehensive risk value exceeds the first risk threshold but does not exceed the second risk threshold, the abnormal alarm level is determined to be the "emergency level". The "emergency level" indicates that the target gas meter faces relatively serious risks and immediate attention and corresponding measures are required to avoid accidents.

[0090] 209. If the comprehensive risk value exceeds the second risk threshold, the abnormal alarm level is determined to be the disaster level.

[0091] In this step, if the calculated comprehensive risk value If it exceeds the second risk threshold, the abnormal alarm level is set to "disaster level". "Disaster level" indicates that the target gas meter is in an extremely high-risk state, and a serious accident may occur soon. Immediate emergency measures must be taken.

[0092] Regarding the above steps 207-209, it should be noted that the first risk threshold and the second risk threshold can be set based on a comprehensive consideration of the safety of the target gas meter. The following several methods can be specifically adopted:

[0093] A. Analyze the abnormal events that have occurred in the gas meter in the past and their resulting consequences, find out at which risk levels minor problems (warning level), serious problems (emergency level), and situations that may lead to catastrophic consequences (disaster level) begin to appear, and set the first risk threshold and the second risk threshold accordingly.

[0094] B. Refer to the safety standards and operating specifications of the gas meter and related industries, and use them as the basis for the first risk threshold and the second risk threshold.

[0095] C. Evaluate the response of the gas meter under different conditions through tests under laboratory conditions or computer simulations, understand the possible impacts when specific parameters exceed the normal range, and set reasonable first risk threshold and second risk threshold accordingly.

[0096] Through the above detailed implementation methods, the collaborative risk coefficient is introduced to accurately calculate the comprehensive risk value based on the number of exceeded items and the intensity of exceeding, and divide the abnormal alarm level accordingly, so as to effectively monitor and timely respond to the operating status of the target gas meter and ensure the use safety.

[0097] 210. Store the real-time status data as abnormal data in the non-volatile memory in real time and upload it to the cloud, and perform corresponding intelligent alarm operations according to the abnormal alarm level.

[0098] This step combines the description of step 103 in the above method, and the same content will not be repeated here.

[0099] It should be noted that the intelligent alarm operation includes three specific types of actions, namely, switching the frequency of uploading local data from the non-volatile memory to the cloud from the first basic frequency to the second emergency frequency, controlling the target gas meter to send remote alarm work order requests and gas source cut-off requests to the cloud, and activating the corresponding sound and light alarm warning of the target gas meter. However, since the abnormal alarm level is clearly defined in steps 207-209, the corresponding intelligent alarm operations can be performed in combination with this abnormal alarm level to achieve multi-level alarms. Specifically, it includes three situations:

[0100] Situation 1:

[0101] If the abnormal alarm level is the warning level, the frequency of uploading local data from the non-volatile memory to the cloud is switched from the first basic frequency to the second emergency frequency. The local data includes at least the abnormal data and gas usage data stored in the non-volatile memory.

[0102] Since the "warning level" indicates that there are potential problems with the target gas meter, but the current situation does not pose an immediate threat. Therefore, to ensure the security and integrity of the data, the frequency of uploading the non-volatile memory to the cloud can be switched from the first basic frequency (e.g., once per hour) to the second emergency frequency (e.g., once every 10 minutes) to increase the frequency, so that the cloud can collect and analyze the local data including abnormal data, gas usage data, etc. faster and more completely, in order to timely detect the development trend of potential problems.

[0103] Case 2: If the abnormal alarm level is the emergency level, the frequency of uploading local data from the non-volatile memory to the cloud is switched from the first basic frequency to the second emergency frequency, and the target gas meter is controlled to initiate a remote alarm work order request and a gas source cut-off request to the cloud.

[0104] Since the "emergency level" indicates that the target gas meter faces relatively serious risks and immediate attention and corresponding measures need to be taken to avoid accidents. Therefore, at this time, in addition to performing the actions in Case 1, the target gas meter is also controlled to send a remote alarm work order request to the cloud to notify relevant personnel to take immediate action. This remote alarm work order request contains detailed abnormal information, such as over-limit parameters, over-limit intensity, etc., so as to quickly locate the problem and formulate a solution. At the same time, the target gas meter is controlled to send a request to cut off the gas source to the cloud to prevent potential risks from escalating into more serious accidents. It should be noted that this operation usually requires manual confirmation or automated execution under specific conditions to ensure that it will not cause unnecessary interference to normal use.

[0105] Case 3: If the abnormal alarm level is the disaster level, the frequency of uploading local data from the non-volatile memory to the cloud is switched from the first basic frequency to the second emergency frequency, and the target gas meter is controlled to initiate a remote alarm work order request and a gas source cut-off request to the cloud, and activate the corresponding audible and visual alarm warning of the target gas meter.

[0106] Since the "disaster level" indicates that the target gas meter is in an extremely high-risk state and a serious accident may occur soon, immediate emergency measures must be taken. Therefore, at this time, in addition to performing the actions in Case 2, the audible and visual alarm device installed on the target gas meter is also activated to prompt users to pay attention to the danger through visual and auditory signals, further protecting the life and property safety of users.

[0107] Furthermore, as for the above Figure 1-2For the implementation of the method embodiments shown, embodiments of the present application provide an Internet of Things gas meter abnormal data real-time storage and intelligent warning device, which is used to improve the accuracy of abnormal warnings to ensure immediate storage and upload of relevant abnormal data to the cloud. The embodiments of this device correspond to the foregoing method embodiments. For the convenience of reading, the details in the foregoing method embodiments will not be repeated one by one in this embodiment. However, it should be clear that the device in this embodiment can correspondingly implement all the content in the foregoing method embodiments. Specifically, as Figure 3 shown, for an Internet of Things gas meter applying a locally deployed non-volatile memory, the device includes:

[0108] A collection unit 31, configured to collect the instant status data corresponding to the target gas meter, where the instant status data includes an instant flow value, an instant temperature value, and an instant pressure value, and the target gas meter is an Internet of Things gas meter selected as the monitoring target;

[0109] A judgment unit 32, configured to judge whether the target gas meter meets the abnormal warning condition based on the instant status data, where the abnormal warning condition is used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, and the target threshold is a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter;

[0110] An alarm unit 33, configured to, if satisfied, store the instant status data as abnormal data in the non-volatile memory in real time and upload it to the cloud, and perform corresponding intelligent alarm operations according to the abnormal alarm level, where the abnormal alarm level is determined according to the overlimit parameter of the instant status data exceeding the target threshold.

[0111] Further, as Figure 4 shown, the judgment unit 32 includes:

[0112] A first acquisition module 321, configured to acquire the target threshold corresponding to the target gas meter, where the target threshold includes a flow judgment threshold, a temperature judgment threshold, and a pressure judgment threshold;

[0113] A comparison module 322, configured to compare the flow value with the flow judgment threshold, the temperature value with the temperature judgment threshold, and the pressure value with the pressure judgment threshold respectively to obtain a comparison result, where the comparison result includes the number of overlimit items corresponding to the number of overlimit items;

[0114] A first determination module 323, configured to determine that the target gas meter does not meet the abnormal warning condition if the number of overlimit items is zero;

[0115] A second determination module 324, configured to determine that the target gas meter meets the abnormal alarm condition if the number of over-limit items is not zero.

[0116] Further, as Figure 4 shown, the first acquisition module 321 is specifically configured to

[0117] acquire the basic thresholds corresponding to the target gas meter, where the basic thresholds include a basic flow threshold, a basic temperature threshold, and a basic pressure threshold;

[0118] calculate the terrain complexity coefficient corresponding to the target gas meter according to the altitude and the surrounding building density index of the location where the target gas meter is located;

[0119] acquire the seasonal adjustment coefficient corresponding to the current month of the location where the target gas meter is located;

[0120] acquire the monthly average temperature and the temperature fluctuation standard deviation of the current month of the location where the target gas meter is located, and adjust the basic temperature threshold based on the terrain complexity coefficient, the monthly average temperature, and the temperature fluctuation standard deviation to obtain the temperature determination threshold;

[0121] acquire the pipeline distance between the target gas meter and the nearest pressure regulating station and the pipeline material identifier, and adjust the basic pressure threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, the pipeline distance, and the pipeline material identifier to obtain the pressure determination threshold;

[0122] acquire the holiday correction coefficient corresponding to the current month of the location where the target gas meter is located, and adjust the basic threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, and the holiday correction coefficient to obtain the flow determination threshold.

[0123] Further, as Figure 4 shown, the device further includes:

[0124] Before acquiring the basic thresholds corresponding to the target gas meter, determine the maintenance correction coefficient of the target gas meter according to the usage duration and the startup times corresponding to the target gas meter;

[0125] determine the maintenance correction coefficient of the target gas meter according to the number of repairs and the repair intervals corresponding to the target gas meter;

[0126] calculate the comprehensive correction coefficient of the target gas meter based on the maintenance correction coefficient and the maintenance correction coefficient, and correct the basic flow threshold, the basic temperature threshold, and the basic pressure threshold respectively based on the comprehensive correction coefficient to obtain the basic threshold.

[0127] Further, as Figure 4 shown, the comparison result further includes the overrun intensity corresponding to the overrun parameter; the apparatus further includes:

[0128] A calculation unit 34, configured to calculate a comprehensive risk value of the target gas meter based on the number of overrun items and the overrun intensity before storing the instant status data as abnormal data in the non-volatile memory in real time and uploading it to the cloud, and performing corresponding intelligent alarm operations according to the abnormal alarm level;

[0129] A determination unit 35, configured to determine the abnormal alarm level as the early warning level if the comprehensive risk value does not exceed the first risk threshold;

[0130] The determination unit 35 is further configured to determine the abnormal alarm level as the emergency level if the comprehensive risk value exceeds the first risk threshold but does not exceed the second risk threshold;

[0131] The determination unit 35 is further configured to determine the abnormal alarm level as the disaster level if the comprehensive risk value exceeds the second risk threshold.

[0132] Further, as Figure 4 shown, the calculation unit 34 includes:

[0133] A second acquisition module 341, configured to acquire the importance weight corresponding to the number of overrun items;

[0134] A first calculation module 342, configured to, when the number of overrun items is one, calculate a single-item collaborative risk coefficient according to the overrun intensity corresponding to the single overrun parameter, and calculate the comprehensive risk value based on the importance weight corresponding to the single overrun item and the single-item collaborative risk coefficient;

[0135] A second calculation module 343, configured to, when the number of overrun items is multiple, calculate a multi-item collaborative risk coefficient by multiplying the number of overrun items and the overrun intensity corresponding to the multiple overrun parameters, and determine the comprehensive risk value based on the importance weights corresponding to the multiple overrun parameters respectively, the multi-item collaborative risk coefficient, and the abnormal superposition risk coefficient of the multiple overrun parameters.

[0136] Further, as Figure 4 shown, the alarm unit 33 includes:

[0137] The first alarm module 331 is configured to, if the abnormal alarm level is the early warning level, switch the frequency of uploading local data from the non-volatile memory to the cloud from a first base frequency to a second emergency frequency, where the second emergency frequency is higher than the first base frequency, and the local data includes at least the abnormal data and gas usage data stored in the non-volatile memory;

[0138] The second alarm module 332 is configured to, if the abnormal alarm level is the emergency level, switch the frequency of uploading the local data from the non-volatile memory to the cloud from the first base frequency to the second emergency frequency, and control the target gas meter to initiate a remote alarm work order request and a gas source cut-off request to the cloud;

[0139] The third alarm module 333 is configured to, if the abnormal alarm level is the disaster level, switch the frequency of uploading the local data from the non-volatile memory to the cloud from the first base frequency to the second emergency frequency, and control the target gas meter to initiate the remote alarm work order request and the gas source cut-off request to the cloud, and activate the corresponding audible and visual alarm warning of the target gas meter.

[0140] Further, an embodiment of the present application further provides a storage medium for storing a computer program, where the computer program, when running, controls the device where the storage medium is located to execute the above Figure 1-2 Internet of Things gas meter abnormal data real-time storage and intelligent alarm method described above.

[0141] Further, an embodiment of the present application further provides a processor for running a program, where the program, when running, executes the above Figure 1-2 Internet of Things gas meter abnormal data real-time storage and intelligent alarm method described above.

[0142] In the above embodiments, each embodiment is described with its own emphasis. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0143] It can be understood that the relevant features in the above methods and devices can be referred to each other. In addition, the "first", "second", etc. in the above embodiments are used to distinguish each embodiment, and do not represent the advantages and disadvantages of each embodiment.

[0144] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0145] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings provided herein. The structure required to construct such systems will be apparent from the above description. Additionally, the present application is not directed to any particular programming language. It should be understood that the content of the present application described herein can be implemented using various programming languages, and the description of a particular language above is for the purpose of disclosing the best mode of the present application.

[0146] In addition, the memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0147] Those skilled in the art will appreciate that the embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing device generate means for implementing the functions specified in Figure 1 one or more of the flows or Figure 1 blocks or combinations of blocks.

[0149] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows or Figure 1 blocks or combinations of blocks.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process Figure 1 one process or a plurality of processes and / or boxes Figure 1 steps for implementing the functions specified in one box or a plurality of boxes.

[0151] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0152] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0153] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storing information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0154] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, commodity or device including the element.

[0155] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter, characterized in that, An Internet of Things gas meter applied to locally deployed non-volatile memory, the method comprising: Collecting instant status data corresponding to a target gas meter, the instant status data including an instant flow value, an instant temperature value, and an instant pressure value, the target gas meter being an Internet of Things gas meter selected as a monitoring target; Judging whether the target gas meter meets an abnormal alarm condition based on the instant status data, the abnormal alarm condition being used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, the target threshold being a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter; If it is satisfied, storing the instant status data as abnormal data in the non-volatile memory in real time and uploading it to the cloud, and performing corresponding intelligent alarm operations according to the abnormal alarm level, the abnormal alarm level being determined according to the over-limit parameters of the instant status data exceeding the target threshold; The target threshold includes a flow determination threshold, a temperature determination threshold, and a pressure determination threshold; obtaining the target threshold corresponding to the target gas meter includes: Obtaining a basic threshold corresponding to the target gas meter, the basic threshold including a basic flow threshold, a basic temperature threshold, and a basic pressure threshold; calculating a terrain complexity coefficient corresponding to the target gas meter according to the altitude and the surrounding building density index of the location of the target gas meter; obtaining a seasonal adjustment coefficient corresponding to the current month of the location of the target gas meter; obtaining the monthly average temperature and the temperature fluctuation standard deviation of the current month of the location of the target gas meter, and adjusting the basic temperature threshold based on the terrain complexity coefficient, the monthly average temperature, and the temperature fluctuation standard deviation to obtain the temperature determination threshold; obtaining the pipeline distance between the target gas meter and the nearest pressure regulating station and the pipeline material identification, and adjusting the basic pressure threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, the pipeline distance, and the pipeline material identification to obtain the pressure determination threshold; obtaining a holiday correction coefficient corresponding to the current month of the location of the target gas meter, and adjusting the basic threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, and the holiday correction coefficient to obtain the flow determination threshold; The specific expression for calculating the temperature determination threshold is: ; wherein, is the base temperature threshold, taking into account the difference between the current month and the monthly average temperature, reflects the standard deviation of temperature fluctuations and its relationship with the terrain complexity coefficient ; The specific expression for calculating the pressure determination threshold is: ; wherein, is the basic pressure threshold, is the actual pipeline distance from the target gas meter to the nearest pressure regulating station, is used to consider the influence of pipeline distance on pressure. As the pipeline distance increases, the pressure loss increases. is used to consider the influence of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of pipeline distance on pressure. As the pipeline distance increases, the pressure decays exponentially. is used to consider the influence of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of seasonal adjustment coefficient on pressure; The specific expression for calculating the flow determination threshold is: ; wherein, is the base traffic threshold, which reflects the impact of the seasonal adjustment coefficient on traffic, which reflects the impact of holidays on traffic, which takes into account the impact of the terrain complexity coefficient. The more complex the terrain, the higher the traffic threshold.

2. The method according to claim 1, wherein Judging whether the target gas meter meets the abnormal alarm condition based on the instant status data includes: Obtaining the target threshold corresponding to the target gas meter, the target threshold including a flow determination threshold, a temperature determination threshold, and a pressure determination threshold; Comparing the flow value with the flow determination threshold, the temperature value with the temperature determination threshold, and the pressure value with the pressure determination threshold respectively to obtain a comparison result, the comparison result including the number of over-limit items corresponding to the over-limit parameters; If the number of over-limit items is zero, it is determined that the target gas meter does not meet the abnormal alarm condition; If the number of over-limit items is not zero, it is determined that the target gas meter meets the abnormal alarm condition.

3. The method according to claim 1, wherein Before obtaining the basic threshold corresponding to the target gas meter, the method further includes: Determining an aging correction coefficient of the target gas meter according to the usage duration and startup times corresponding to the target gas meter; Determining a maintenance correction coefficient of the target gas meter according to the number of repairs and maintenance intervals corresponding to the target gas meter; Calculating a comprehensive correction coefficient of the target gas meter based on the aging correction coefficient and the maintenance correction coefficient, and correcting the basic flow threshold, the basic temperature threshold, and the basic pressure threshold respectively based on the comprehensive correction coefficient to obtain the basic threshold.

4. The method according to claim 2, characterized in that The comparison result further includes the over-limit intensity corresponding to the over-limit parameter; before performing corresponding intelligent alarm operations according to the abnormal alarm level, the method further includes: Calculating a comprehensive risk value of the target gas meter based on the number of over-limit items and the over-limit intensity; If the comprehensive risk value does not exceed the first risk threshold, the abnormal alarm level is determined to be the warning level; If the comprehensive risk value exceeds the first risk threshold but does not exceed the second risk threshold, the abnormal alarm level is determined to be the emergency level; If the comprehensive risk value exceeds the second risk threshold, the abnormal alarm level is determined to be the disaster level.

5. The method according to claim 4, wherein Calculating a comprehensive risk value of the target gas meter based on the number of over-limit items and the over-limit intensity includes: Obtaining the importance weight corresponding to the number of over-limit items; When the number of over-limit items is one, calculating a single-item collaborative risk coefficient according to the over-limit intensity corresponding to the single over-limit parameter, and calculating the comprehensive risk value based on the importance weight and the single-item collaborative risk coefficient corresponding to the single over-limit item; When the number of over-limit items is multiple, calculating a multi-item collaborative risk coefficient by multiplying the number of over-limit items and the over-limit intensity corresponding to the multiple over-limit parameters, and determining the comprehensive risk value based on the importance weights, the multi-item collaborative risk coefficient, and the abnormal superposition risk coefficient corresponding to the multiple over-limit parameters respectively.

6. The method according to claim 4, characterized in that, Performing corresponding intelligent alarm operations according to the abnormal alarm level includes: If the abnormal alarm level is the warning level, switching the frequency of uploading local data from the non-volatile memory to the cloud from the first basic frequency to the second emergency frequency, where the second emergency frequency is higher than the first basic frequency, and the local data at least includes the abnormal data and gas usage data stored in the non-volatile memory; If the abnormal alarm level is the emergency level, switching the frequency of uploading the local data from the non-volatile memory to the cloud from the first basic frequency to the second emergency frequency, and controlling the target gas meter to send a remote alarm work order request and a gas source cut-off request to the cloud; If the abnormal alarm level is the disaster level, switch the frequency of uploading local data from the non-volatile memory to the cloud from the first basic frequency to the second emergency frequency, and control the target gas meter to initiate the remote alarm work order request and the gas source cut-off request to the cloud, and activate the corresponding audible and visual alarm warning of the target gas meter.

7. An abnormal data real-time storage and intelligent warning device for an Internet of Things gas meter, characterized in that, An Internet of Things gas meter applied to a locally deployed non-volatile memory, the device includes: A collection unit, configured to collect instant status data corresponding to a target gas meter, the instant status data including an instant flow value, an instant temperature value, and an instant pressure value, and the target gas meter being an Internet of Things gas meter selected as a monitoring target; A judgment unit, configured to judge whether the target gas meter meets the abnormal alarm condition based on the instant status data, the abnormal alarm condition being used to characterize that at least one of the instant flow value, the instant temperature value, and the instant pressure value exceeds its respective target threshold, and the target threshold being a dynamically adaptive threshold for geographical environment-season coupling compensation of the target gas meter; An alarm unit, configured to, if satisfied, store the instant status data as abnormal data in the non-volatile memory in real time and upload it to the cloud, and perform corresponding intelligent alarm operations according to the abnormal alarm level, and the abnormal alarm level being determined according to the over-limit parameter of the instant status data exceeding the target threshold; The target threshold includes a flow judgment threshold, a temperature judgment threshold, and a pressure judgment threshold; an acquisition module, specifically configured to, Acquire the basic threshold corresponding to the target gas meter, the basic threshold including a basic flow threshold, a basic temperature threshold, and a basic pressure threshold; Calculate the terrain complexity coefficient corresponding to the target gas meter according to the altitude and the surrounding building density index of the location where the target gas meter is located; Acquire the seasonal adjustment coefficient corresponding to the current month of the location where the target gas meter is located; Acquire the monthly average temperature and the temperature fluctuation standard deviation of the current month of the location where the target gas meter is located, and adjust the basic temperature threshold based on the terrain complexity coefficient, the monthly average temperature, and the temperature fluctuation standard deviation to obtain the temperature judgment threshold; Acquire the pipeline distance between the target gas meter and the nearest pressure regulating station and the pipeline material identification, and adjust the basic pressure threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, the pipeline distance, and the pipeline material identification to obtain the pressure judgment threshold; Acquire the holiday correction coefficient corresponding to the current month of the location where the target gas meter is located, and adjust the basic threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient, and the holiday correction coefficient to obtain the flow judgment threshold; The specific expression for calculating the temperature judgment threshold is: ; wherein, is the base temperature threshold, which takes into account the difference between the current month and the monthly average temperature, while reflects the standard deviation of temperature fluctuations and its relationship with the terrain complexity coefficient The specific expression for calculating the pressure judgment threshold is: ; wherein, is the basic pressure threshold, is the actual pipeline distance from the target gas meter to the nearest pressure regulating station, is used to consider the influence of pipeline distance on pressure. As the pipeline distance increases, the pressure loss increases. is used to consider the influence of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of pipeline distance on pressure. As the pipeline distance increases, the pressure decays exponentially. is used to consider the influence of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. is used to consider the influence of seasonal adjustment coefficient on pressure; The specific expression for calculating the flow judgment threshold is: ； Among them, is the basic traffic threshold, which is used to reflect the impact of the seasonal adjustment coefficient on traffic, which is used to reflect the impact of holidays on traffic, which is used to consider the impact of the terrain complexity coefficient. The more complex the terrain is, the higher the traffic threshold is.

8. A storage medium, characterized in that, The storage medium includes a stored program, wherein when the program runs, it controls the device where the storage medium is located to execute the method for real-time storage and intelligent warning of abnormal data of the Internet of Things gas meter according to any one of claims 1 to 6.

9. A processor, characterized in that, The processor is used to run a program, wherein when the program runs, it executes the method for real-time storage and intelligent warning of abnormal data of the Internet of Things gas meter according to any one of claims 1 to 6.

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