Real-time storage and intelligent alarm method and device for abnormal data of Internet of Things gas meter

Abstract

The invention discloses an Internet of Things gas meter abnormal data real-time storage and intelligent alarm method and device, and relates to the technical field of gas safety monitoring and intelligent alarm. According to the main technical scheme, real-time state data corresponding to a target gas meter are collected, wherein the real-time state data comprise a real-time flow value, a real-time temperature value and a real-time pressure value; based on the instant state data, whether the target gas meter meets an abnormal alarm condition or not is judged, the abnormal alarm condition is used for representing that at least one of an instant flow value, an instant temperature value and an instant pressure value exceeds respective target threshold values, and the target threshold values are dynamic self-adaptive threshold values for conducting geographical environment-seasonal coupling compensation on the target gas meter; and if yes, storing the instant state data as abnormal data in real time to a nonvolatile memory and uploading the abnormal data to a cloud, and executing corresponding intelligent alarm operation according to the abnormal alarm level. The invention aims to improve the accuracy of abnormal alarm so as to ensure that related abnormal data is instantly stored and uploaded to the cloud.

Description

Technical Field

[0001] The present application relates to the technical field of gas safety monitoring and intelligent alarm, and in particular 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 relies on Internet of Things technology to achieve efficient data transmission and precise control, which greatly promotes the optimization and upgrading of gas services and the modern management of smart cities.

[0003] At present, the IoT gas meter 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, provided that the non-volatile memory is intact, so as to realize data interaction with the cloud. However, this mechanism has significant limitations: first, the judgment of sudden abnormal situations (such as fire) is not comprehensive and accurate enough; second, when encountering sudden abnormal situations (such as fire), the non-volatile memory in the IoT gas meter may be destroyed and unable to store relevant abnormal data, which makes it difficult to report the alarm in time or perform operations such as cutting off the gas source, which seriously affects the efficiency of emergency response and the ability to investigate after the accident. Summary of the invention

[0004] In view of the above problems, the present application provides a method and device for real-time storage and intelligent alarm of abnormal data of an IoT gas meter. The main purpose is to improve the accuracy of abnormal alarms to ensure real-time storage and uploading of relevant abnormal data to the cloud.

[0005] In order to solve the above technical problems, this application proposes the following solutions: In a first aspect, the present application provides a method for real-time storage and intelligent alarm of abnormal data of an IoT gas meter, which is applied to an IoT gas meter with a locally deployed non-volatile memory, and the method includes: Collecting real-time status data corresponding to a target gas meter, the real-time status data including a real-time flow value, a real-time temperature value, and a real-time pressure value, the target gas meter being an IoT gas meter selected as a monitoring target; Based on the real-time status data, it is determined whether the target gas meter meets an abnormal alarm condition, wherein the abnormal alarm condition is used to indicate that at least one of the real-time flow value, the real-time temperature value, and the real-time pressure value exceeds a respective target threshold, and the target threshold is a dynamic adaptive threshold for performing geographic environment-season coupling compensation on the target gas meter; If the conditions are met, the real-time status data is stored in the non-volatile memory as abnormal data in real time and uploaded to the cloud, and a corresponding intelligent alarm operation is performed according to the abnormal alarm level, where the abnormal alarm level is determined based on the over-limit parameters of the real-time status data exceeding the target threshold.

[0006] In a second aspect, the present application provides an IoT gas meter abnormal data real-time storage and intelligent alarm device, which is applied to an IoT gas meter with a locally deployed non-volatile memory, and the device includes: A collection unit, used for collecting real-time status data corresponding to a target gas meter, wherein the real-time status data includes a real-time flow value, a real-time temperature value and a real-time pressure value, and the target gas meter is an IoT gas meter selected as a monitoring target; a judgment unit, configured to judge whether the target gas meter satisfies an abnormal alarm condition based on the real-time status data, wherein the abnormal alarm condition is used to indicate that at least one of the real-time flow value, the real-time temperature value, and the real-time pressure value exceeds a respective target threshold, and the target threshold is a dynamic adaptive threshold for performing geographic environment-season coupling compensation on the target gas meter; An alarm unit is used to store the real-time status data as abnormal data in the non-volatile memory in real time and upload it to the cloud if the conditions are met, and to perform corresponding intelligent alarm operations according to the abnormal alarm level, wherein the abnormal alarm level is determined according to the over-limit parameters of the real-time status data exceeding the target threshold.

[0007] In order to achieve the above-mentioned purpose, according to the third aspect of the present application, a storage medium is provided, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the real-time storage and intelligent alarm method of abnormal data of the Internet of Things gas meter of the first aspect mentioned above.

[0008] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present application, a processor is provided, which is used to run a program, wherein when the program is running, the real-time storage and intelligent alarm method of abnormal data of the Internet of Things gas meter of the above-mentioned first aspect is executed.

[0009] By means of the above technical scheme, the present application provides a method and device for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter. First, the real-time status data corresponding to the target gas meter is collected. The real-time status data includes the real-time flow value, the real-time temperature value and the real-time pressure value. The target gas meter is the Internet of Things gas meter selected as the monitoring target. Then, based on the real-time status data, it is judged whether the target gas meter meets the abnormal alarm condition. The abnormal alarm condition is used to characterize that at least one of the real-time flow value, the real-time temperature value and the real-time pressure value exceeds the respective target threshold value. The target threshold value is a dynamic adaptive threshold for geographical environment-season coupling compensation of the target gas meter. Finally, if it is met, the real-time status data is stored as abnormal data in real time in a non-volatile memory and uploaded to the cloud, and the corresponding intelligent alarm operation is performed according to the abnormal alarm level. The abnormal alarm level is determined according to the over-limit parameter of the real-time status data exceeding the target threshold value. Through the technical solution provided by the present application, after collecting the real-time status data such as the real-time flow, real-time temperature and real-time pressure corresponding to the gas meter, the target threshold required for the real-time status data can be dynamically compensated according to 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, and can 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 real-time status data can be stored in a non-volatile memory as abnormal data in real time and uploaded to the cloud, ensuring the security and integrity of the abnormal data, and can determine an abnormal alarm level through the over-limit parameters of the real-time status data exceeding the target threshold to perform corresponding alarm operations, such as adjusting the frequency of data uploading to the cloud, sending alarm work order requests, gas source cut-off requests, activating local sound and light alarms, etc., which not only enhances the monitoring and response capabilities to abnormal situations, but also provides strong data support for subsequent investigations.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of a method for real-time storage and intelligent alarm of abnormal data of an IoT gas meter provided in an embodiment of the present application is shown; Figure 2Another flow chart of a method for real-time storage and intelligent alarm of abnormal data of an IoT gas meter provided in an embodiment of the present application is shown; Figure 3 The present invention shows a block diagram of a real-time storage and intelligent alarm device for abnormal data of an IoT gas meter provided in an embodiment of the present application; Figure 4 A block diagram of another IoT gas meter abnormal data real-time storage and intelligent alarm device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0012] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying 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 to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0013] At present, the IoT gas meter 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, provided that the non-volatile memory is intact, so as to realize data interaction with the cloud. However, this mechanism has significant limitations: first, the judgment of sudden abnormal situations (such as fire) is not comprehensive and accurate enough; second, when encountering sudden abnormal situations (such as fire), the non-volatile memory in the IoT gas meter may be destroyed and unable to store relevant abnormal data, which makes it difficult to report the alarm in time or perform operations such as cutting off the gas source, which seriously affects the efficiency of emergency response and the ability to investigate after the accident.

[0014] To this end, the embodiment of the present application provides a method for real-time storage and intelligent alarm of abnormal data of an IoT gas meter, which is applied to an IoT gas meter with a locally deployed non-volatile memory. The method can improve the accuracy of abnormal alarms to ensure real-time storage and upload of relevant abnormal data to the cloud. The specific execution steps are as follows: Figure 1 As shown, including: 101. Collect the real-time status data corresponding to the target gas meter.

[0015] In this step, the target gas meter refers to the IoT gas meter selected as the monitoring target. The real-time status data includes the real-time flow value, the real-time temperature value and the real-time pressure value. In practical applications, a temperature sensor and a pressure sensor can be pre-implanted in the hardware end of the target gas meter, and the real-time temperature value and the real-time pressure value of the target gas meter can be monitored in real time through the temperature sensor and the pressure sensor. The real-time flow value of the target gas meter can be monitored through the flow sensor of the target gas meter itself. Specifically, the sensor data can be automatically obtained at a preset time interval (for example, once per minute), that is, the real-time flow value, the real-time temperature value and the real-time pressure value are obtained as the real-time status data corresponding to the target gas meter, so as to perform the abnormal judgment in step 102.

[0016] 102. Determine whether the target gas meter meets the abnormal alarm conditions based on the real-time status data.

[0017] Among them, the abnormal alarm condition is used to characterize that at least one of the instantaneous flow value, the instantaneous temperature value and the instantaneous pressure value exceeds the respective target threshold value, and the target threshold value is a dynamic adaptive threshold value for geographical environment-season coupling compensation of the target gas meter. In this step, for the instantaneous flow value, the instantaneous temperature value and the instantaneous pressure value in the instantaneous state data, the basic threshold value can be set in advance according to historical data and industry standards, including the basic flow threshold value, the basic temperature threshold value and the basic pressure threshold value, and the target threshold value is obtained by dynamically adjusting the target gas meter based on the above basic threshold value in consideration of the geographical environment and seasonal changes at the location of the target gas meter on the temperature, pressure and flow, which specifically includes the flow determination threshold value, the temperature determination threshold value and the pressure determination threshold value. Specifically, the terrain complexity coefficient of the location of a target gas meter can be pre-calculated by considering geographical environmental factors such as altitude and surrounding building density index, and the adjustment coefficients corresponding to different seasons can be pre-set according to seasonal changes, and the two can be introduced into the dynamic adaptive adjustment of the basic flow threshold value, the basic temperature threshold value and the basic pressure threshold value. 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, the pipeline distance and pipeline material can be introduced into the dynamic adaptive adjustment of the basic pressure threshold 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 to accurately predict and respond to flow changes in different time periods, avoid abnormal alarms caused by special periods such as holidays, and obtain the flow judgment threshold, temperature judgment threshold and pressure judgment threshold that can reflect the actual environmental conditions of the location of the target gas meter.

[0018] In addition, machine learning algorithms can be used to analyze a large amount of historical data and real-time data to predict environmental change trends in the future, and dynamically adjust the above basic thresholds according to environmental change trends. Specifically, a large amount of historical data is collected from IoT gas meters, weather stations and other related sensors, including but not limited to temperature, pressure, flow, geographic location information, timestamps, etc. Based on historical data, features that help predict environmental change trends are extracted. For example: time series features: average temperature, maximum temperature, minimum temperature, etc. in the past few days, geographic location features: altitude, building density index, etc., seasonal features: current month, whether it is a holiday, etc. Use statistical methods or automated feature selection algorithms (such as recursive feature elimination, LASSO regression, etc.) to determine which features are most helpful to the model. Select an appropriate algorithm based on the nature of the problem. For time series prediction problems, you can consider using long short-term memory networks (LSTM), recurrent neural networks (RNN), or gradient boosting decision trees (GBDT). Divide the cleaned data set into training sets and validation sets, use the training set to train the selected model, and evaluate its performance on the validation set. Optimize the model's hyperparameters through methods such as grid search or random search to obtain the best performance. Use the trained model to predict environmental change trends in the future. For example, predict the temperature, pressure, and flow changes in the next few days. Based on the prediction results, dynamically adjust the target thresholds of each parameter. For example: if it is predicted that the temperature will drop significantly in the next few days, the temperature judgment threshold will be appropriately lowered to prevent false alarms. If a large increase in gas demand is expected, the flow judgment threshold will be increased accordingly to avoid unnecessary alarms.

[0019] After obtaining the flow determination threshold, temperature determination threshold and pressure determination threshold, the instantaneous flow value can be compared with the flow determination threshold, the instantaneous temperature value with the temperature determination threshold and the instantaneous pressure value with the pressure determination threshold respectively to determine the over-limit situation. If none of the three parameters of flow, temperature and pressure exceed the limit, 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 sensor's instant status data at a preset time interval (for example, once per minute). If the three parameters of flow, temperature and pressure exceed the limit, that is, there is at least one over-limit parameter, it means that the target gas meter meets the abnormal alarm condition, and step 103 is executed at this time.

[0020] 103. The instant status data is stored in a non-volatile memory as abnormal data in real time and uploaded to the cloud, and corresponding intelligent alarm operations are performed according to the abnormal alarm level.

[0021] Among them, the abnormal alarm level is determined according to the over-limit parameter of the instant status data exceeding the target threshold. In this step, when it is confirmed that the target gas meter meets the abnormal alarm condition, it means that there are abnormal conditions in the three parameters of the current flow, temperature and pressure of the target gas meter. At this time, the data protection mechanism can be immediately started, and the latest instant status data is stored in the non-volatile memory as abnormal data in real time and uploaded to the cloud to ensure that even in extreme cases (such as fire), data loss can be avoided to ensure the security and integrity of the data. In order to ensure that the cloud can receive the above-mentioned 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, switching from the first basic frequency to the second emergency frequency, and the second emergency frequency is higher than the first basic frequency. Exemplarily, it is assumed that the basic frequency is once an 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 a timely manner. In order to notify the operation and maintenance personnel to verify and handle on site and prevent the expansion of the accident, a remote alarm work order request and a gas source cut-off request can be sent to the cloud. In order to alert users to potential dangers and take corresponding safety measures, the sound and light alarm corresponding to the target gas meter can also be activated, that is, the sound and light alarm device installed on the target gas meter can be activated to make it continue to issue warnings.

[0022] 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, in the comparison process of the three parameters of flow, temperature and pressure, the parameters exceeding the target threshold are taken as over-limit parameters, and the number of over-limit items of the over-limit parameters and the difference with the target threshold are counted as over-limit intensity, and the corresponding abnormal alarm level is determined according to the number of over-limit items and over-limit intensity, such as warning level, emergency level and disaster level. Among them, the "warning level" indicates that there are potential problems with the target gas meter, but the current situation has not yet posed a direct threat, the "emergency level" indicates that the target gas meter faces a more serious risk, and immediate attention and corresponding measures need to be taken to avoid accidents, and the "disaster level" indicates that the target gas meter is in an extremely high risk state, and a serious accident may be about to occur, and emergency measures must be taken immediately. Specifically, when the abnormal alarm level is "early warning level", 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 achieve an increase in frequency, so that the cloud can collect and analyze local data including abnormal data, gas usage data, etc. faster and more completely. When the abnormal alarm level is "emergency level", on the basis of executing the "early warning level" action, 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, so as to notify the operation and maintenance personnel to verify and handle the situation on site and prevent the accident from expanding. When the abnormal alarm level is "disaster level", on the basis of executing the "early warning level" action, the sound and light alarm device installed on the target gas meter can be activated to remind users of potential dangers and take corresponding safety measures, further protecting the safety of users' lives and property.

[0023] Based on the above Figure 1It can be seen from the implementation method that, through the above technical scheme, the present application provides a method for real-time storage and intelligent alarm of abnormal data of an IoT gas meter. First, the real-time status data corresponding to the target gas meter is collected. The real-time status data includes the real-time flow value, the real-time temperature value and the real-time pressure value. The target gas meter is the IoT gas meter selected as the monitoring target. Then, based on the real-time status data, it is judged whether the target gas meter meets the abnormal alarm condition. The abnormal alarm condition is used to characterize that at least one of the real-time flow value, the real-time temperature value and the real-time pressure value exceeds the respective target threshold value. The target threshold value is a dynamic adaptive threshold for geographical environment-season coupling compensation of the target gas meter. Finally, if it is satisfied, the real-time status data is stored in a non-volatile memory as abnormal data in real time and uploaded to the cloud, and the corresponding intelligent alarm operation is performed according to the abnormal alarm level. The abnormal alarm level is determined according to the over-limit parameter of the real-time status data exceeding the target threshold value. Through the technical solution provided by the present application, after collecting the real-time status data such as the real-time flow, real-time temperature and real-time pressure corresponding to the gas meter, the target threshold required for the real-time status data can be dynamically compensated according to 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, and can 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 real-time status data can be stored in a non-volatile memory as abnormal data in real time and uploaded to the cloud, ensuring the security and integrity of the abnormal data, and can determine an abnormal alarm level through the over-limit parameters of the real-time status data exceeding the target threshold to perform corresponding alarm operations, such as adjusting the frequency of data uploading to the cloud, sending alarm work order requests, gas source cut-off requests, activating local sound and light alarms, etc., which not only enhances the monitoring and response capabilities to abnormal situations, but also provides strong data support for subsequent investigations.

[0024] Further, the preferred embodiment of the present application is in the above Figure 1 Based on this, a detailed description of the process of real-time storage and intelligent alarm of abnormal data of IoT gas meters is given. The specific steps are as follows: Figure 2 As shown, including: 201. Collect the real-time status data corresponding to the target gas meter.

[0025] This step is combined with the description of step 101 in the above method, and the same contents will not be repeated here.

[0026] 202. Obtain a target threshold corresponding to a target gas meter.

[0027] The target thresholds include a flow determination threshold, a temperature determination threshold, and a pressure determination threshold.

[0028] It should be noted that the specific execution process of obtaining the target threshold corresponding to the target gas meter is: obtaining the basic threshold corresponding to the target gas meter, the basic threshold includes the basic flow threshold, the basic temperature threshold and the basic pressure threshold; calculating the terrain complexity coefficient corresponding to the target gas meter according to the altitude of the location of the target gas meter and the surrounding building density index; obtaining the seasonal adjustment coefficient corresponding to the current month of the location of the target gas meter; obtaining the monthly average temperature and the standard deviation of temperature fluctuation 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 standard deviation of temperature fluctuation to obtain the temperature judgment 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 judgment threshold; obtaining the holiday correction coefficient corresponding to the current month of the location of the target gas meter, and adjusting the basic flow threshold based on the terrain complexity coefficient, the seasonal adjustment coefficient and the holiday correction coefficient to obtain the flow judgment threshold.

[0029] In this step, the basic flow threshold The basic temperature threshold can be set according to the gas type and user needs. The basic pressure threshold can be set based on equipment specifications and safety standards It can be set according to pipeline material, design specifications, etc.

[0030] Obtain the altitude H of the target gas meter location 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: ; in, Represents the terrain complexity coefficient. H represents the altitude. Represents the building density index. and Represent the weights of altitude and building density index respectively.

[0031] Determine the seasonal adjustment coefficient based on the current month. The specific expression is: ; Adaptive adjustment for base temperature threshold: The meteorological data of the target gas meter location in the past five years are obtained from the meteorological database, and the monthly average temperature and temperature fluctuation standard deviation of the current month are calculated based on this data. The terrain complexity coefficient, monthly average temperature and temperature fluctuation standard deviation are introduced to adaptively adjust the basic temperature threshold. The specific expression is: ; in, is the basic temperature threshold. The difference between the current month and the monthly average temperature is taken into account. It reflects the standard deviation of temperature fluctuation and the terrain complexity coefficient relationship.

[0032] Adaptive adjustment for base stress threshold: Obtain the actual pipeline distance from the target gas meter to the nearest pressure regulating station from the geographic information system , set the identifier M according to the actual material of the pipeline (metal pipeline M=1, polyethylene pipeline M=0). , seasonal adjustment factor , pipeline distance The basic pressure threshold is adaptively adjusted by introducing the pipeline material identifier M. The specific expression is: ; in, is the basic pressure threshold. It is the actual pipeline distance from the target gas meter to the nearest pressure regulating station. Used to consider the effect of pipeline distance on pressure. As the pipeline distance increases, the pressure loss increases. Used to consider the impact of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. It is used to consider the effect of pipeline distance on pressure. As the pipeline distance increases, the pressure decays exponentially. Used to consider the impact of terrain complexity coefficient on pressure. The more complex the terrain, the greater the pressure loss. Used to take into account the effect of seasonal adjustment factor on pressure.

[0033] Adaptive adjustment for base stress threshold: According to the number of statutory holidays in the current month Calculating holiday correction factors The specific expression is: ; The terrain complexity coefficient, seasonal adjustment coefficient and holiday correction coefficient are introduced into the adaptive adjustment of the basic threshold. The specific expression is: ; in, is the basic traffic threshold. It is used to reflect the impact of seasonal adjustment coefficient on flow. It reflects the impact of holidays on traffic. Used to consider the influence of terrain complexity coefficient. The more complex the terrain, the higher the flow threshold.

[0034] Through the above expressions, the direct or indirect effects of factors such as seasonality, holidays and terrain complexity on temperature, pressure and flow can be comprehensively considered, and the flow determination threshold, temperature determination threshold and pressure determination threshold can be set more accurately to better adapt to various situations and reduce false alarms and missed alarms. More accurate flow determination thresholds, temperature determination thresholds and pressure determination thresholds can help to detect abnormal situations in a timely manner, reduce unnecessary maintenance work, and thus reduce overall operating costs. Through real-time monitoring and dynamic adjustment of thresholds, potential safety hazards of target gas meters can be discovered earlier, thereby improving the accuracy of abnormal alarms to ensure that relevant abnormal data is stored and uploaded to the cloud in real time.

[0035] It should be noted that, since the gas meter will face aging and maintenance during long-term use, which usually has an adverse effect 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 corresponding usage time and number of starts of the target gas meter; determining the maintenance correction coefficient of the target gas meter according to the number of maintenance and maintenance interval corresponding to the target gas meter; calculating the 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, basic temperature threshold and basic pressure threshold based on the comprehensive correction coefficient to obtain the basic threshold.

[0036] In this step, the usage time (T) and the number of starts (S) of the target gas meter are collected in advance. According to the pre-set standard interval, the corresponding correction coefficient is determined. For example, the usage time can be divided into several intervals: [0-5000 hours], [5000-10000 hours], [>10000 hours], and each interval corresponds to a different coefficient value. ; Similarly, the number of starts can also be divided into different intervals, each interval corresponds to a different coefficient . Calculate the aging correction factor based on the length of use and the number of starts, specifically: , we can assume a simple linear combination: .

[0037] Similarly, the maintenance times (R) and average maintenance intervals (I) of the target gas meters are collected in advance. The maintenance times and maintenance intervals can also be divided into different intervals according to the actual situation, and a specific adjustment coefficient is specified for each interval. and . Calculate the maintenance correction factor based on the number of maintenance and the average maintenance interval, specifically: , we can assume a simple linear combination: .

[0038] Calculate the comprehensive correction factor based on the above aging correction factor and maintenance correction parameter: The square root method is used to balance the weights of the two, while avoiding the influence of extreme values. For a given basic traffic threshold , basic temperature threshold and basic pressure threshold Corrections are performed respectively to obtain the basic thresholds used subsequently.

[0039] By introducing correction coefficients based on factors such as usage time, number of starts, number of maintenance times and maintenance intervals, 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 to reduce the occurrence of failures. The changes in environmental conditions in different seasons will affect the normal operation of the gas meter. On this basis, further combining environmental-seasonal dynamic compensation can ensure that abnormal judgments are more in line with actual conditions and improve the accuracy and adaptability of abnormal detection.

[0040] 203. Compare 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 to obtain a comparison result.

[0041] The comparison result includes the number of out-of-limit items corresponding to the out-of-limit parameters.

[0042] In this step, the instantaneous flow value, instantaneous temperature value and instantaneous pressure value of the target gas meter, as well as the dynamic adaptive threshold calculated in step 202, i.e., the corresponding flow determination threshold, temperature determination threshold and pressure determination threshold, are obtained. For each parameter of flow, temperature and pressure, its instantaneous value is compared with the corresponding determination threshold. If the instantaneous value of a parameter exceeds its determination threshold, the parameter is recorded as an over-limit parameter. The number of all over-limit parameters is counted to obtain the number of over-limit items. If the number of over-limit items is zero, i.e., none of the three parameters of flow, temperature and pressure exceeds its corresponding determination threshold, step 204 is executed. On the contrary, if the number of over-limit items is not zero, i.e., one or more of the three parameters of flow, temperature and pressure exceed their respective determination thresholds, step 205 is executed.

[0043] 204. If the number of exceeded items is zero, it is determined that the target gas meter does not meet the abnormal alarm conditions.

[0044] In this step, since there is no out-of-limit parameter among the three parameters of flow, temperature and pressure, that is, there is no out-of-limit parameter, it can be considered that the target gas meter does not meet the abnormal alarm condition.

[0045] 205. If the number of exceeded items is not zero, it is determined that the target gas meter meets the abnormal alarm conditions.

[0046] In this step, since there are out-of-limit parameters among the three parameters of flow, temperature and pressure, that is, there are one or more out-of-limit parameters, 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 out-of-limit items and the intensity of the out-of-limit items.

[0047] The comparison result in step 203 also includes the over-limit intensity corresponding to the over-limit parameter. The over-limit intensity represents the degree to which the over-limit parameter exceeds its judgment threshold, and is used for subsequent risk level assessment. Specifically, the over-limit intensity can be calculated by subtracting the judgment threshold from the instantaneous value, dividing it by the judgment threshold, and then multiplying it by 100%, to obtain a percentage of the over-limit intensity.

[0048] 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 exceeding limit items and the exceeding limit intensity is: obtain the importance weight corresponding to the number of exceeding limit items; when the number of exceeding limit items is one, calculate the single item collaborative risk coefficient according to the exceeding limit intensity corresponding to the single exceeding limit parameter, and calculate the comprehensive risk value based on the importance weight corresponding to the single exceeding limit item number and the single collaborative risk coefficient; when the number of exceeding limit items is multiple, calculate multiple collaborative risk coefficients by multiplying the number of exceeding limit items and the exceeding limit intensity corresponding to the multiple exceeding limit parameters, and determine the comprehensive risk value based on the importance weights corresponding to the multiple exceeding limit parameters, the multiple collaborative risk coefficients and the abnormal superposition risk coefficients of the multiple exceeding limit parameters.

[0049] In this step, the number of exceeded parameters X represents the number of exceeded parameters, and the exceeded intensity Indicates the degree to which the over-limit parameter exceeds its judgment threshold. Since the over-limit parameter may be one or more, when there are multiple over-limit parameters, considering the different types of the over-limit parameters (flow, temperature or pressure), 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, so it is assigned a higher weight. , the traffic parameters are relatively safe and are given a lower weight .

[0050] When only one parameter exceeds the limit ( ) can be based on the over-limit strength Calculate the single synergy risk coefficient , and combined with the importance weights Get the comprehensive risk value of the target gas meter The specific expression is: ; ; in, Represents the single-item synergy risk coefficient. It represents the comprehensive risk value of the single synergy risk coefficient. 50 is a preset constant. By dividing by 50, the excess strength Converting to a relatively small proportional value helps control the magnitude of the impact of different over-limit intensities on the synergistic effect. ), considering the amplification effect of the interaction between these over-limit parameters on risk, multiple synergistic risk coefficients can be calculated by multiplication , the specific expression is: ; in, Indicates the overlimit intensity of the i-th overlimit parameter A standardized adjustment factor is obtained by conversion, 50 is a preset constant, and the over-limit intensity is divided by 50. Converting to a relatively small proportional value helps to control the magnitude of the impact of different over-limit intensities on the synergistic effect. is a multi-factor synergy risk factor, which is obtained by multiplying the value of each over-limit parameter. Subtract 1 from the result to reflect the risk amplification factor caused by multiple over-limit parameters exceeding the limit at the same time. Add the weighted over-limit intensity of each over-limit parameter and add it to multiple synergistic risk coefficients. Add the square of the number of excess items multiplied by a fixed coefficient (such as 0.3) to strengthen the risk of multiple parameter anomalies and obtain the comprehensive risk value of the target gas meter , the specific expression is: .

[0051] in, It represents the weighted average excess intensity of all excess parameters. It represents the risk amplification factor of the basic risk plus the synergy effect. If , then this part is equal to 1, indicating that there is no additional synergistic effect; if , it means there is a synergistic effect and the risk will be further amplified. It represents the product of the square of the number of exceeded items and the fixed coefficient, emphasizing the risk of multiple exceeded parameters abnormal superposition. When multiple exceeded parameters exceed the limit at the same time, the comprehensive risk value will increase significantly. It is a fixed coefficient used to enhance the risk of superposition of multiple out-of-limit parameter anomalies.

[0052] 207. If the comprehensive risk value does not exceed the first risk threshold, the abnormal alarm level is determined to be a warning level.

[0053] In this step, if the calculated comprehensive risk value If the value is lower than the set first risk threshold, the abnormal alarm level is determined as "warning level". "Warning level" indicates that there is a potential problem 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.

[0054] 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 an emergency level.

[0055] In this step, if the calculated comprehensive risk value If the first risk threshold is exceeded but the second risk threshold is not exceeded, the abnormal alarm level is determined as "emergency level". "Emergency level" indicates that the target gas meter faces a relatively serious risk and requires immediate attention and corresponding measures to avoid accidents.

[0056] 209. If the comprehensive risk value exceeds the second risk threshold, the abnormal alarm level is determined to be a disaster level.

[0057] In this step, if the calculated comprehensive risk value If the second risk threshold is exceeded, the abnormal alarm level is set to "disaster level". "Disaster level" means that the target gas meter is in an extremely high risk state and a serious accident may occur soon, and emergency measures must be taken immediately.

[0058] 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, and specifically can be set in the following ways: A. Analyze the abnormal events that have occurred in the gas meter in the past and their consequences, find out at what risk level minor problems (warning level), serious problems (emergency level) and situations that may lead to catastrophic consequences (disaster level) begin to occur, and set the first risk threshold and the second risk threshold accordingly.

[0059] B. Refer to the safety standards and operating specifications of gas meters and related industries, and use them as the basis for the first risk threshold and the second risk threshold.

[0060] C. Evaluate the response of the gas meter under different conditions through testing under laboratory conditions or computer simulation, understand the possible impact when specific parameters exceed the normal range, and set reasonable first risk thresholds and second risk thresholds accordingly.

[0061] Through the above-mentioned detailed implementation method, a collaborative risk coefficient is introduced to accurately calculate the comprehensive risk value based on the number of exceeded items and the intensity of exceeded limits, and the abnormal alarm level is divided accordingly, so as to achieve effective monitoring and timely response to the operating status of the target gas meter and ensure safe use.

[0062] 210. The instant status data is stored in a non-volatile memory as abnormal data in real time and uploaded to the cloud, and corresponding intelligent alarm operations are performed according to the abnormal alarm level.

[0063] This step is combined with the description of step 103 in the above method, and the same contents are not repeated here.

[0064] It should be noted that the intelligent alarm operation includes three specific 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 initiate a remote alarm work order request and a gas source cut-off request to the cloud, and activating the sound and light alarm corresponding to the target gas meter. However, since the abnormal alarm level is clearly defined in steps 207-209, the corresponding intelligent alarm operation can be performed in combination with the abnormal alarm level to achieve multi-level alarm. Specifically, there are three situations: Case 1: 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, and the local data at least includes abnormal data and gas usage data stored in the non-volatile memory.

[0065] Since the "warning level" indicates that there are potential problems with the target gas meter, but the current situation does not pose a direct threat, in order 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 (for example, once an hour) to the second emergency frequency (for example, once every 10 minutes) to increase the frequency. This allows the cloud to collect and analyze local data including abnormal data, gas usage data, etc. more quickly and completely, so as to promptly discover the development trend of potential problems.

[0066] Case 2: If the abnormal alarm level is 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.

[0067] Since the "emergency level" indicates that the target gas meter faces a more serious risk, it needs to be paid attention to immediately and take corresponding measures to avoid accidents. Therefore, in addition to executing the actions in situation one, the target gas meter must be controlled to send a remote alarm work order request to the cloud to notify relevant personnel to take immediate action. The 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 develop a solution. At the same time, the target gas meter is controlled to send a request to the cloud to cut off the gas source to prevent potential risks from escalating into more serious accidents. It should be noted that this operation usually requires manual confirmation or automatic execution under specific conditions to ensure that it does not cause unnecessary interference to normal use.

[0068] Case three: If the abnormal alarm level is a 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 sound and light alarm of the target gas meter.

[0069] Since the "disaster level" indicates that the target gas meter is in an extremely high-risk state, a serious accident may be about to occur, and emergency measures must be taken immediately. Therefore, in addition to executing the actions in situation 2, the sound and light alarm device installed on the target gas meter must be activated to alert users to danger through visual and auditory signals, further ensuring the safety of users' lives and property.

[0070] Furthermore, as a response to the above Figure 1-2 The implementation of the method embodiment shown in the figure, the embodiment of the present application provides a real-time storage and intelligent alarm device for abnormal data of an IoT gas meter, which is used to improve the accuracy of abnormal alarms to ensure the immediate storage and upload of relevant abnormal data to the cloud. The embodiment of the device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will no longer repeat the details of the aforementioned method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the aforementioned method embodiment. Specifically, Figure 3 As shown, an IoT gas meter for locally deploying non-volatile memory includes: A collection unit 31 is used to collect real-time status data corresponding to a target gas meter, wherein the real-time status data includes a real-time flow value, a real-time temperature value, and a real-time pressure value. The target gas meter is an IoT gas meter selected as a monitoring target; A judging unit 32, configured to judge whether the target gas meter satisfies an abnormal alarm condition based on the real-time status data, wherein the abnormal alarm condition is used to indicate that at least one of the real-time flow value, the real-time temperature value, and the real-time pressure value exceeds a respective target threshold, and the target threshold is a dynamic adaptive threshold for performing geographic environment-season coupling compensation on the target gas meter; The alarm unit 33 is used to store the real-time status data as abnormal data in the non-volatile memory in real time and upload it to the cloud if the conditions are met, and to perform corresponding intelligent alarm operations according to the abnormal alarm level, wherein the abnormal alarm level is determined according to the over-limit parameters of the real-time status data exceeding the target threshold.

[0071] Further, such as Figure 4 As shown, the judging unit 32 includes: A first acquisition module 321 is used to acquire the target threshold corresponding to the target gas meter, where the target threshold includes a flow determination threshold, a temperature determination threshold and a pressure determination threshold; A comparison module 322, used to compare 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, wherein the comparison result includes the number of over-limit items corresponding to the number of over-limit items; A first determination module 323, configured to determine that the target gas meter does not meet the abnormal alarm condition if the number of over-limit items is zero; The second determination module 324 is configured to determine that the target gas meter meets the abnormal alarm condition if the number of exceeded items is not zero.

[0072] Further, such as Figure 4 As shown, the first acquisition module 321 is specifically used to: Obtaining basic thresholds corresponding to the target gas meter, wherein the basic thresholds include 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 of the location of the target gas meter and the surrounding building density index; Obtain the seasonal adjustment coefficient corresponding to the current month at the location of the target gas meter; Obtaining the monthly average temperature and the temperature fluctuation standard deviation of the current month at 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 and pipeline material identification between the target gas meter and the nearest pressure regulating station, 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; The holiday correction coefficient corresponding to the current month of the location of the target gas meter is obtained, and the basic threshold is adjusted based on the terrain complexity coefficient, the seasonal adjustment coefficient and the holiday correction coefficient to obtain the flow determination threshold.

[0073] Further, such as Figure 4 As shown, the device also includes: Before obtaining the basic threshold value corresponding to the target gas meter, determining the maintenance correction coefficient of the target gas meter according to the usage time and the number of starts corresponding to the target gas meter; Determine a maintenance correction coefficient of the target gas meter according to the maintenance times and maintenance intervals corresponding to the target gas meter; The comprehensive correction coefficient of the target gas meter is calculated based on the maintenance correction coefficient and the maintenance correction coefficient, and the basic flow threshold, the basic temperature threshold and the basic pressure threshold are corrected based on the comprehensive correction coefficient to obtain the basic threshold.

[0074] Further, such as Figure 4 As shown, the comparison result also includes the over-limit intensity corresponding to the over-limit parameter; the device also includes: The calculation unit 34 is used to calculate the comprehensive risk value of the target gas meter based on the number of exceeded items and the exceeded intensity before storing the real-time status data as abnormal data in the non-volatile memory in real time and uploading it to the cloud, and performing the corresponding intelligent alarm operation according to the abnormal alarm level; A determination unit 35, configured to determine the abnormal alarm level as a warning level if the comprehensive risk value does not exceed a first risk threshold; The determination unit 35 is further configured to determine the abnormal alarm level as an emergency level if the comprehensive risk value exceeds the first risk threshold but does not exceed the second risk threshold; The determination unit 35 is further configured to determine the abnormality alarm level as a disaster level if the comprehensive risk value exceeds the second risk threshold.

[0075] Further, such as Figure 4 As shown, the calculation unit 34 includes: The second acquisition module 341 is used to obtain the importance weight corresponding to the number of items exceeding the limit; A first calculation module 342 is used for calculating a single collaborative risk coefficient according to the overlimit intensity corresponding to the single overlimit parameter when the number of overlimit items is one, and calculating the comprehensive risk value based on the importance weight corresponding to the single overlimit number and the single collaborative risk coefficient; The second calculation module 343 is used to calculate multiple collaborative risk coefficients based on the number of exceeded items and the exceeded intensity corresponding to the multiple exceeded parameters when there are multiple exceeded items, and to determine the comprehensive risk value based on the importance weights corresponding to each of the multiple exceeded parameters, the multiple collaborative risk coefficients and the abnormal superposition risk coefficients of the multiple exceeded parameters.

[0076] Further, such as Figure 4 As shown, the alarm unit 33 includes: A first alarm module 331 is configured to switch the frequency of uploading local data from the non-volatile memory to the cloud from a first basic frequency to a second emergency frequency if the abnormal alarm level is the early warning level, wherein 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; The second alarm module 332 is used for 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 if the abnormal alarm level is the emergency level, and controlling the target gas meter to initiate a remote alarm work order request and a gas source cut-off request to the cloud; The third alarm module 333 is used to switch 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 if the abnormal alarm level is the disaster level, 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 sound and light alarm warning of the target gas meter.

[0077] Furthermore, an embodiment of the present application further provides a storage medium, wherein the storage medium is used to store a computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the above Figure 1-2 The real-time storage and intelligent alarm method of abnormal data of the Internet of Things gas meter described in the present invention.

[0078] Furthermore, the embodiment of the present application also provides a processor, the processor is used to run a program, wherein the program executes the above Figure 1-2 The real-time storage and intelligent alarm method of abnormal data of the Internet of Things gas meter described in the present invention.

[0079] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0080] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0081] 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 aforementioned method embodiments and will not be repeated here.

[0082] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the specific language above is for the purpose of disclosing the best mode of implementation of the application.

[0083] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt 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 codes.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0089] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0090] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (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 temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0091] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0092] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, 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.) containing computer-usable program codes.

[0093] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should 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: The method is applied to an IoT gas meter with a locally deployed non-volatile memory, and comprises: Collecting real-time status data corresponding to a target gas meter, the real-time status data including a real-time flow value, a real-time temperature value, and a real-time pressure value, the target gas meter being an IoT gas meter selected as a monitoring target; Based on the real-time status data, it is determined whether the target gas meter meets an abnormal alarm condition, wherein the abnormal alarm condition is used to indicate that at least one of the real-time flow value, the real-time temperature value, and the real-time pressure value exceeds a respective target threshold, and the target threshold is a dynamic adaptive threshold for performing geographic environment-season coupling compensation on the target gas meter; If the conditions are met, the real-time status data is stored in the non-volatile memory as abnormal data in real time and uploaded to the cloud, and a corresponding intelligent alarm operation is performed according to the abnormal alarm level, where the abnormal alarm level is determined based on the over-limit parameters of the real-time status data exceeding the target threshold.

2. The method according to claim 1, characterized in that: Judging whether the target gas meter meets the abnormal alarm condition based on the real-time status data includes: Acquire the target threshold corresponding to the target gas meter, wherein the target threshold includes a flow determination threshold, a temperature determination threshold, and a pressure determination threshold; Respectively 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 to obtain a comparison result, wherein the comparison result includes the number of over-limit items corresponding to the over-limit parameter; If the number of exceeded items is zero, it is determined that the target gas meter does not meet the abnormal alarm condition; If the number of exceeded items is not zero, it is determined that the target gas meter meets the abnormal alarm condition.

3. The method according to claim 2, characterized in that Acquiring the target threshold corresponding to the target gas meter includes: Obtaining basic thresholds corresponding to the target gas meter, wherein the basic thresholds include 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 of the location of the target gas meter and the surrounding building density index; Obtain the seasonal adjustment coefficient corresponding to the current month at the location of the target gas meter; Obtaining the monthly average temperature and the temperature fluctuation standard deviation of the current month at 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 and pipeline material identification between the target gas meter and the nearest pressure regulating station, 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; The holiday correction coefficient corresponding to the current month of the location of the target gas meter is obtained, and the basic threshold is adjusted based on the terrain complexity coefficient, the seasonal adjustment coefficient and the holiday correction coefficient to obtain the flow determination threshold.

4. The method according to claim 3, characterized in that: Before obtaining the basic threshold value corresponding to the target gas meter, the method further includes: Determine an aging correction coefficient of the target gas meter according to the usage time and the number of starts corresponding to the target gas meter; Determine a maintenance correction coefficient of the target gas meter according to the maintenance times and maintenance intervals corresponding to the target gas meter; The comprehensive correction coefficient of the target gas meter is calculated based on the aging correction coefficient and the maintenance correction coefficient, and the basic flow threshold, the basic temperature threshold and the basic pressure threshold are corrected based on the comprehensive correction coefficient to obtain the basic threshold.

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

6. The method according to claim 5, characterized in that Calculating the comprehensive risk value of the target gas meter based on the number of exceeded items and the exceeded intensity includes: Obtaining the importance weight corresponding to the number of items exceeding the limit; When the number of exceeded items is one, the single collaborative risk coefficient is calculated according to the exceeded intensity corresponding to the single exceeded parameter, and the comprehensive risk value is calculated based on the importance weight corresponding to the single exceeded number of items and the single collaborative risk coefficient; When there are multiple items exceeding the limit, multiple collaborative risk coefficients are calculated based on the number of items exceeding the limit corresponding to the multiple parameters exceeding the limit and the multiplication of the intensity of the exceeding limit, and the comprehensive risk value is determined based on the importance weights corresponding to each of the multiple parameters exceeding the limit, the multiple collaborative risk coefficients and the abnormal superposition risk coefficients of the multiple parameters exceeding the limit.

7. The method according to claim 5, characterized in that Execute corresponding intelligent alarm operations according to the abnormal alarm level, including: If the abnormal alarm level is the early warning level, the frequency of uploading local data from the non-volatile memory to the cloud is switched from a first basic frequency to a second emergency frequency, the second emergency frequency being 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, the frequency of uploading the 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; If the abnormal alarm level is the disaster level, the frequency of uploading the 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 the remote alarm work order request and the gas source cut-off request to the cloud, and activate the corresponding sound and light alarm warning of the target gas meter.

8. A real-time storage and intelligent alarm device for abnormal data of an Internet of Things gas meter, characterized in that: An IoT gas meter for locally deploying a non-volatile memory comprises: A collection unit, used for collecting real-time status data corresponding to a target gas meter, wherein the real-time status data includes a real-time flow value, a real-time temperature value and a real-time pressure value, and the target gas meter is an IoT gas meter selected as a monitoring target; a judgment unit, configured to judge whether the target gas meter satisfies an abnormal alarm condition based on the real-time status data, wherein the abnormal alarm condition is used to indicate that at least one of the real-time flow value, the real-time temperature value, and the real-time pressure value exceeds a respective target threshold, and the target threshold is a dynamic adaptive threshold for performing geographic environment-season coupling compensation on the target gas meter; An alarm unit is used to store the real-time status data as abnormal data in the non-volatile memory in real time and upload it to the cloud if the conditions are met, and to perform corresponding intelligent alarm operations according to the abnormal alarm level, wherein the abnormal alarm level is determined according to the over-limit parameters of the real-time status data exceeding the target threshold.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the real-time storage and intelligent alarm method for abnormal data of an Internet of Things gas meter as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein when the program is running, the method for real-time storage and intelligent alarm of abnormal data of an Internet of Things gas meter as described in any one of claims 1 to 7 is executed.

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