NB remote ultrasonic gas meter and its monitoring method, system and storage medium

Abstract

The present application relates to the technical field of gas meter supervision, and in particular to NB remote ultrasonic gas meters and supervision methods, systems and storage media thereof, the method comprising: calculating gas interference data and gas state data, extracting features of the gas interference data and gas state data respectively, obtaining interference feature sets and state feature sets, calculating the correlation coefficient between the gas interference data and the gas state data, and performing feature encoding and vector concatenation on the interference feature set and the state feature set to obtain a target fusion state feature vector, inputting the target fusion state feature vector of each gas application environment into a preset gas meter safety analysis model set to perform gas meter safety analysis, obtaining gas meter safety analysis results, and performing safety warning management on the current NB remote ultrasonic gas meter according to the gas meter safety analysis results, and generating a safety warning management strategy. The present application improves the adaptive capability of NB remote ultrasonic gas meters to ultrasonic signals.

Description

Technical Field

[0001] The present application relates to the technical field of gas meter supervision, and in particular to NB remote ultrasonic gas meters and supervision methods, systems and storage media thereof. Background Art

[0002] With the rapid development of Internet of Things (IoT) technology, narrowband Internet of Things (NB-IoT) technology has been widely used in the field of remote management of smart devices due to its low power consumption, wide coverage, and large number of connections. NB-IoT technology does not need to re-establish servers, and can directly lease the existing service platforms of the three major operators, thereby reducing construction and maintenance costs. In addition, the technology has good signal penetration and can achieve good coverage in densely populated buildings, basements, tunnels and other environments. In terms of the number of connections, the number of connections in a single NB-IoT sector can reach tens of thousands, which is 50 to 100 times that of traditional mobile communication technology. These advantages make NB-IoT technology have significant advantages in the remote management of smart electricity meters, smart gas meters, smart water meters and other equipment, and can effectively solve problems such as unstable long-distance data transmission and high power consumption of equipment in the Internet of Things.

[0003] Ultrasonic technology was mainly used in the field of industrial and commercial trade settlement in the early days. With the improvement of production technology and the progress of science and technology, the technology of ultrasonic gas meters for residential use has made great breakthroughs. Ultrasonic gas meters measure gas flow based on the time difference method, and have the characteristics of high measurement accuracy, wide range, strong durability, no mechanical loss, and strong anti-interference ability. Compared with traditional diaphragm mechanical meters, ultrasonic gas meters have incomparable advantages in terms of volume, accuracy, repeatability and life. In addition, ultrasonic gas meters have a simple structure and high reliability. The main measuring components are the airway and ultrasonic transducer, and the assembly process is simpler.

[0004] In recent years, with the increase in urban gas consumption, the costs of gas companies in gas data reading, gas meter safety inspection, and maintenance remain high. At the same time, the implementation of tiered gas prices has made the traditional management model no longer applicable, bringing new challenges to the operation and management of gas companies. Therefore, combining NB-IoT technology with ultrasonic metering technology to develop a new type of IoT ultrasonic gas meter has become an important direction for the transformation and upgrading of the gas industry.

[0005] NB remote ultrasonic gas meter combines the dual advantages of NB-IoT technology and ultrasonic metering technology to achieve low power consumption and wide coverage of the Internet of Things. The gas meter is equipped with an NB-IoT communication module and an ultrasonic metering module, which can collect gas usage data in real time and upload it to the backend server through the NB-IoT network. The backend management system can remotely read the historical data and abnormal record data in the meter, and monitor and analyze the gas usage in real time. At the same time, the gas meter also supports remote control function, which can remotely close the valve according to the requirements of the gas company.

[0006] At present, the disadvantage of large-caliber NB remote ultrasonic gas meters is that they are highly sensitive to flow fields. NB remote ultrasonic gas meters are more sensitive to changes in gas flow fields. When the flow field is unstable or turbulent, it will affect the measurement accuracy and the ability to track flow changes is weak. In some cases, NB remote ultrasonic gas meters cannot quickly and accurately track flow changes, especially in situations where flow fluctuations are large. The measurement accuracy is affected by the medium. If there are other obstacles in the pipeline, these obstacles will interfere with the propagation of ultrasonic signals, thereby affecting the measurement results. Some obstacles will change the acoustic properties of the medium, causing the ultrasonic signal to scatter or reflect during propagation, resulting in measurement errors. Summary of the invention

[0007] In order to solve at least one of the above-mentioned technical problems, the present application provides a NB remote ultrasonic gas meter and a supervision method, system and storage medium thereof.

[0008] In the first aspect, the present application provides a NB remote ultrasonic gas meter monitoring method, which adopts the following technical solution:

[0009] Obtaining gas application information and gas interference information, wherein the gas application information is data information of gas distribution recorded by the current NB remote ultrasonic gas meter for different gas application environments, and the gas interference information is interference factor information affecting ultrasonic signal transmission of the current NB remote ultrasonic gas meter and gas interference degree standards corresponding to the interference factor information;

[0010] Performing data monitoring on different gas application environments in the gas application information to obtain a gas application data sequence for each gas application environment;

[0011] Performing gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain gas flow data of each gas application environment;

[0012] Calculating the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, and performing feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment;

[0013] The state feature vector is input into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain a gas meter monitoring result;

[0014] According to the monitoring result of the gas meter, error warning management is performed on the current NB remote ultrasonic gas meter to obtain an error warning strategy.

[0015] By adopting the above technical solution, gas application information and gas interference information are obtained, providing a comprehensive data basis for gas meter management. Gas application information records the gas distribution of the gas meter to different devices, while gas interference information reveals the factors and degree that affect the transmission of ultrasonic signals. The combination of the two provides an accurate reference for subsequent data monitoring and flow detection, effectively improving the accuracy and pertinence of data processing. Data monitoring of gas application information is performed to obtain the gas application data sequence of each device. The continuity and integrity of the data are ensured, providing a reliable data source for subsequent flow detection. Flow detection is performed in the order of gas distribution records, which not only ensures the orderliness of detection, but also improves the detection efficiency, so that the gas flow data of each device can be accurately recorded and analyzed. Gas interference data and gas status data are calculated based on gas interference information and gas flow data, and feature fusion analysis is performed. Various interference factors in the gas transmission process and the actual gas flow of the equipment are comprehensively considered, thereby more accurately reflecting the true status of each device. Feature fusion analysis further improves the comprehensiveness and readability of the data, providing strong support for subsequent verification analysis. The state feature vector is input into the preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis, and a comprehensive and in-depth evaluation of the gas meter status is conducted. The results of the calibration analysis not only accurately reflect the current status of the gas meter, but also provide a scientific basis for subsequent error warning management. According to the gas meter monitoring results, the error warning management of the current NB remote ultrasonic gas meter is carried out to obtain the error warning strategy. In this way, possible problems with the gas meter are discovered in time, and corresponding warning measures are taken, effectively avoiding energy waste and safety hazards caused by gas meter errors. The formulation and implementation of the error warning strategy not only improves the accuracy and reliability of the gas meter, but also provides users with a safer and more convenient gas use experience.

[0016] In a possible implementation, the gas flow detection is performed on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment, including:

[0017] Inputting the gas application data sequence of each gas application environment into a preset flow analysis model according to the order of gas distribution records, and calling the preset feature center of the flow analysis model to calculate the feature clustering points of the gas application data sequence of each gas application environment to obtain the feature clustering points of each gas application environment;

[0018] Calculating the distance mean between the gas application data sequence of each gas application environment and the characteristic clustering points to obtain an average point distance;

[0019] Adjusting the center displacement of the preset feature center based on the average point distance to obtain displacement deviation data;

[0020] Determine whether the displacement deviation data exceeds the preset deviation data, and if so, determine a replacement center displacement based on the displacement deviation data and the preset feature center, and replace the center displacement of the preset feature center with the replacement center displacement to obtain a replaced preset feature center;

[0021] According to the replaced preset feature center, gas flow clustering calculation is performed on the gas application data sequence of each gas application environment to obtain the gas flow data of each gas application environment.

[0022] In a possible implementation, the calculating the gas interference data and the gas status data of each gas application environment according to the gas interference information and the gas flow data includes:

[0023] Perform harmonic distortion and fluctuation calculation on the gas flow data according to the gas interference information to obtain distortion data and application fluctuation data of each gas application environment;

[0024] Performing time series matching on the distortion data and the application fluctuation data respectively to obtain gas interference data of each gas application environment;

[0025] Performing transmission stability calculation on the gas flow data according to the gas interference information to obtain stability data for each gas application environment;

[0026] The stability data of each gas application environment is time-series matched respectively to obtain the gas status data of each gas application environment.

[0027] In a possible implementation, the performing feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment includes:

[0028] Extracting features from the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment;

[0029] The correlation coefficient between the gas interference data and the gas state data is calculated, and the interference feature set and the state feature set are feature encoded and vector concatenated according to the correlation coefficient to obtain a state feature vector of each gas application environment.

[0030] In a possible implementation, the feature extraction is performed on the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set of each gas application environment, including:

[0031] Constructing ultrasonic interference coordinates, and importing the gas interference data into the ultrasonic interference coordinates to obtain an ultrasonic interference curve, wherein the X-axis of the ultrasonic interference coordinates is different unit time, and the Y-axis of the ultrasonic interference coordinates is different unit interference data;

[0032] Extracting curve feature points from the ultrasonic interference curve to obtain at least two interference feature points;

[0033] Performing feature clustering analysis on the at least two interference feature points to obtain an interference clustering result for each interference feature point, and generating an interference feature set for each gas application environment according to the interference clustering result;

[0034] Construct the ultrasonic state coordinates, and import the gas state data into the ultrasonic state coordinates to obtain an ultrasonic state curve, wherein the X-axis of the ultrasonic state coordinates is different unit time, and the Y-axis of the ultrasonic state coordinates is different unit gas state data;

[0035] Performing curve feature point identification on the ultrasonic state curve to obtain at least two feature points to be selected;

[0036] Calculating standard deviation data of the ultrasonic state curve, and screening the at least two candidate feature points for curve feature points according to the standard deviation data to obtain state curve feature points;

[0037] A state feature set for each gas application environment is generated according to the state curve feature points.

[0038] In a possible implementation, the calculating of the correlation coefficient between the gas interference data and the gas state data, and performing feature encoding and vector concatenation on the interference feature set and the state feature set according to the correlation coefficient to obtain the state feature vector of each gas application environment includes:

[0039] Calculating a first mean and a first standard deviation of the gas interference data;

[0040] Calculate a second mean and a second standard deviation of the gas state data;

[0041] Calculating a correlation coefficient between the gas interference data and the gas state data according to the first mean value and the first standard deviation, the second mean value and the second standard deviation;

[0042] Performing feature coding and vector conversion on the interference feature set to obtain an ultrasonic interference feature vector;

[0043] Performing feature encoding and vector conversion on the state feature set to obtain an ultrasonic state feature vector;

[0044] The ultrasonic interference feature vector and the ultrasonic state feature vector are fused according to the correlation coefficient to obtain a state feature vector of each gas application environment.

[0045] In a possible implementation, performing error warning management on the current NB remote ultrasonic gas meter according to the gas meter monitoring result to obtain an error warning strategy, and then further comprising:

[0046] Collecting management personnel information and gas meter location, and determining the target terminal device based on the management personnel information, wherein the gas meter location information is the gas meter geographical location of the current NB remote ultrasonic gas meter;

[0047] Determine a maintenance time range corresponding to the gas meter monitoring result according to the gas meter monitoring result and the preset monitoring standard;

[0048] When the error warning strategy is detected and sent to the target terminal device, the arrival time of the manager at the gas meter location is recorded, and the arrival time is matched with the maintenance time range to determine whether there is an abnormality in maintenance timeliness;

[0049] If the maintenance timeliness is abnormal, the manager information is marked, and the marked manager information is sent to the terminal device of the manager's upper-level manager.

[0050] In the second aspect, the present application provides an NB remote ultrasonic gas meter monitoring system, which adopts the following technical solution:

[0051] A NB remote ultrasonic gas meter monitoring system, comprising:

[0052] An information acquisition module, used to acquire gas application information and gas interference information, wherein the gas application information is data information of gas distribution records of the current NB remote ultrasonic gas meter for different gas application environments, and the gas interference information is interference factor information affecting ultrasonic signal transmission of the current NB remote ultrasonic gas meter and a gas interference degree standard corresponding to the interference factor information;

[0053] A data monitoring module, used to perform data monitoring on different gas application environments in the gas application information to obtain a gas application data sequence for each gas application environment;

[0054] A flow detection module, used to perform gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order, to obtain the gas flow data of each gas application environment;

[0055] a state analysis module, used to calculate the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, and perform feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment;

[0056] A gas analysis module, used for inputting the state characteristic vector into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain a gas meter monitoring result;

[0057] The error warning module is used to perform error warning management on the current NB remote ultrasonic gas meter according to the monitoring result of the gas meter to obtain an error warning strategy.

[0058] In a possible implementation, the flow detection model performs gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment, specifically for:

[0059] Inputting the gas application data sequence of each gas application environment into a preset flow analysis model according to the order of gas distribution records, and calling the preset feature center of the flow analysis model to calculate the feature clustering points of the gas application data sequence of each gas application environment to obtain the feature clustering points of each gas application environment;

[0060] Calculating the distance mean between the gas application data sequence of each gas application environment and the characteristic clustering points to obtain an average point distance;

[0061] Adjusting the center displacement of the preset feature center based on the average point distance to obtain displacement deviation data;

[0062] Determine whether the displacement deviation data exceeds the preset deviation data, and if so, determine a replacement center displacement based on the displacement deviation data and the preset feature center, and replace the center displacement of the preset feature center with the replacement center displacement to obtain a replaced preset feature center;

[0063] According to the replaced preset feature center, gas flow clustering calculation is performed on the gas application data sequence of each gas application environment to obtain the gas flow data of each gas application environment.

[0064] In another possible implementation, when the state analysis module calculates the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, it is specifically used to:

[0065] Perform harmonic distortion and fluctuation calculation on the gas flow data according to the gas interference information to obtain distortion data and application fluctuation data of each gas application environment;

[0066] Performing time series matching on the distortion data and the application fluctuation data respectively to obtain gas interference data of each gas application environment;

[0067] Performing transmission stability calculation on the gas flow data according to the gas interference information to obtain stability data for each gas application environment;

[0068] The stability data of each gas application environment is time-series matched respectively to obtain the gas status data of each gas application environment.

[0069] In another possible implementation, when the state analysis module performs feature fusion analysis on the gas interference data and the gas state data to obtain the state feature vector of each gas application environment, it is specifically used to:

[0070] Extracting features from the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment;

[0071] The correlation coefficient between the gas interference data and the gas state data is calculated, and the interference feature set and the state feature set are feature encoded and vector concatenated according to the correlation coefficient to obtain a state feature vector of each gas application environment.

[0072] In another possible implementation, when the state analysis module extracts features from the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment, the state analysis module is specifically used to:

[0073] Constructing ultrasonic interference coordinates, and importing the gas interference data into the ultrasonic interference coordinates to obtain an ultrasonic interference curve, wherein the X-axis of the ultrasonic interference coordinates is different unit time, and the Y-axis of the ultrasonic interference coordinates is different unit interference data;

[0074] Extracting curve feature points from the ultrasonic interference curve to obtain at least two interference feature points;

[0075] Performing feature clustering analysis on the at least two interference feature points to obtain an interference clustering result for each interference feature point, and generating an interference feature set for each gas application environment according to the interference clustering result;

[0076] Construct the ultrasonic state coordinates, and import the gas state data into the ultrasonic state coordinates to obtain an ultrasonic state curve, wherein the X-axis of the ultrasonic state coordinates is different unit time, and the Y-axis of the ultrasonic state coordinates is different unit gas state data;

[0077] Performing curve feature point identification on the ultrasonic state curve to obtain at least two feature points to be selected;

[0078] Calculating standard deviation data of the ultrasonic state curve, and screening the at least two candidate feature points for curve feature points according to the standard deviation data to obtain state curve feature points;

[0079] A state feature set for each gas application environment is generated according to the state curve feature points.

[0080] In another possible implementation, when the state analysis module calculates the correlation coefficient between the gas interference data and the gas state data, and performs feature encoding and vector concatenation on the interference feature set and the state feature set according to the correlation coefficient to obtain the state feature vector of each gas application environment, it is specifically used to:

[0081] Calculating a first mean and a first standard deviation of the gas interference data;

[0082] Calculate a second mean and a second standard deviation of the gas state data;

[0083] Calculating a correlation coefficient between the gas interference data and the gas state data according to the first mean value and the first standard deviation, the second mean value and the second standard deviation;

[0084] Performing feature coding and vector conversion on the interference feature set to obtain an ultrasonic interference feature vector;

[0085] Performing feature encoding and vector conversion on the state feature set to obtain an ultrasonic state feature vector;

[0086] The ultrasonic interference feature vector and the ultrasonic state feature vector are fused according to the correlation coefficient to obtain a state feature vector of each gas application environment.

[0087] In another possible implementation, the system further includes: an information collection module, a time determination module, an abnormality determination module, and an abnormality handling module, wherein:

[0088] The information collection module is used to collect management personnel information and gas meter location, and determine the target terminal device based on the management personnel information, and the gas meter location information is the gas meter geographical location of the current NB remote ultrasonic gas meter;

[0089] The time determination module is used to determine the maintenance time range corresponding to the monitoring result of the gas meter according to the monitoring result of the gas meter and the preset monitoring standard;

[0090] The abnormality determination module is used to record the arrival time of the manager at the gas meter location after detecting that the error warning strategy is sent to the target terminal device, and match the arrival time with the maintenance time range to determine whether there is a maintenance timeliness abnormality;

[0091] The exception handling module is used to mark the management personnel information when the maintenance timeliness exception exists, and send the marked management personnel information to the terminal device of the management personnel at the next higher level of the management personnel.

[0092] In a third aspect, the present application provides an NB remote ultrasonic gas meter, which adopts the following technical solution:

[0093] at least one processor;

[0094] Memory;

[0095] At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one application is configured to: execute a NB remote ultrasonic gas meter supervision method as described in any one of the first aspects.

[0096] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0097] A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute any NB remote ultrasonic gas meter monitoring method as described in the first aspect.

[0098] In summary, the present application includes at least one of the following beneficial technical effects:

[0099] By adopting the above technical solution, gas application information and gas interference information are obtained, providing a comprehensive data basis for gas meter management. Gas application information records the gas distribution of the gas meter to different devices, while gas interference information reveals the factors and degree that affect the transmission of ultrasonic signals. The combination of the two provides an accurate reference for subsequent data monitoring and flow detection, effectively improving the accuracy and pertinence of data processing. Data monitoring of gas application information is performed to obtain the gas application data sequence of each device. The continuity and integrity of the data are ensured, providing a reliable data source for subsequent flow detection. Flow detection is performed in the order of gas distribution records, which not only ensures the orderliness of detection, but also improves the detection efficiency, so that the gas flow data of each device can be accurately recorded and analyzed. Gas interference data and gas status data are calculated based on gas interference information and gas flow data, and feature fusion analysis is performed. Various interference factors in the gas transmission process and the actual gas flow of the equipment are comprehensively considered, thereby more accurately reflecting the true status of each device. Feature fusion analysis further improves the comprehensiveness and readability of the data, providing strong support for subsequent verification analysis. The state feature vector is input into the preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis, and a comprehensive and in-depth evaluation of the gas meter status is conducted. The results of the calibration analysis not only accurately reflect the current status of the gas meter, but also provide a scientific basis for subsequent error warning management. According to the gas meter monitoring results, the error warning management of the current NB remote ultrasonic gas meter is carried out to obtain the error warning strategy. In this way, possible problems with the gas meter are discovered in time, and corresponding warning measures are taken, effectively avoiding energy waste and safety hazards caused by gas meter errors. The formulation and implementation of the error warning strategy not only improves the accuracy and reliability of the gas meter, but also provides users with a safer and more convenient gas use experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0100] Figure 1 A flow chart of a NB remote ultrasonic gas meter monitoring method provided in an embodiment of the present application.

[0101] Figure 2A schematic diagram of the structure of a NB remote ultrasonic gas meter monitoring system provided in an embodiment of the present application.

[0102] Figure 3 A schematic diagram of the structure of a NB remote ultrasonic gas meter provided in an embodiment of the present application. DETAILED DESCRIPTION

[0103] The following is combined with Figure 1-3 This application is described in further detail.

[0104] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0105] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0106] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0107] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0108] The embodiment of the present application provides a method for supervising a NB remote ultrasonic gas meter, which is executed by a NB remote ultrasonic gas meter, wherein the NB remote ultrasonic gas meter can be an independent physical NB remote ultrasonic gas meter, or a NB remote ultrasonic gas meter cluster or distributed system composed of multiple physical NB remote ultrasonic gas meters, or a cloud NB remote ultrasonic gas meter that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes:

[0109] Step S10: Obtain gas application information and gas interference information.

[0110] Among them, the gas application information is the data information of the gas distribution record of the current NB remote ultrasonic gas meter for different gas application environments, and the gas interference information is the interference factor information affecting the ultrasonic signal transmission of the current NB remote ultrasonic gas meter and the gas interference degree standard corresponding to the interference factor information.

[0111] For the embodiment of the present application, the gas application information represents the data information recorded when the current NB remote ultrasonic gas meter distributes gas to different gas application environments. These data usually contain detailed records of the gas flow, usage time, usage mode, etc. of each device, which are used to reflect the distribution of the gas meter. The gas application environment refers to those devices that supply gas through the NB remote ultrasonic gas meter, such as gas stoves, water heaters, etc. The gas interference information refers to the various interference factor information that affects the ultrasonic signal transmission of the current NB remote ultrasonic gas meter, as well as the gas interference degree standards corresponding to these interference factors. Ultrasonic signals are often used to measure gas flow in gas meters, but may be affected by various factors, such as temperature, pressure changes, impurities or gas components in the pipeline, etc. The interference factor information specifically describes these factors that affect signal transmission, and the gas interference degree standard is a standard for quantitatively evaluating these interference factors, which is used to measure their impact on the measurement accuracy of the gas meter.

[0112] For the embodiment of the present application, data is automatically collected through the built-in sensors and control system of the NB remote ultrasonic gas meter. The NB remote ultrasonic gas meter is usually equipped with a variety of sensors for real-time monitoring of gas flow, pressure, temperature and other parameters. At the same time, the control system will process and analyze these data to generate gas application information and gas interference information. It is worth mentioning that the gas interference degree standard is a set of standards pre-established by the staff based on historical gas interference situations, and is not limited here.

[0113] Step S11: monitoring data of different gas application environments in the gas application information to obtain a gas application data sequence of each gas application environment.

[0114] For the embodiment of the present application, the gas application data corresponding to the gas application environment is extracted from the gas application information through data label screening, and then the extracted gas application data is sequenced according to the time nodes to obtain the gas application data sequence of each gas application environment.

[0115] Step S12: perform gas flow detection on the gas application data sequence of each gas application environment one by one according to the order of gas distribution records to obtain the gas flow data of each gas application environment.

[0116] Specifically, the gas application data sequence of each gas application environment is input into the preset flow analysis model according to the order of gas distribution records, and the preset feature center of the flow analysis model is called to perform feature clustering point calculation on the gas application data sequence of each gas application environment to obtain the feature clustering point of each gas application environment, and the distance mean is calculated between the gas application data sequence of each gas application environment and the feature clustering point to obtain the average point distance, and the center displacement of the preset feature center is adjusted based on the average point distance to obtain displacement deviation data, and it is judged whether the displacement deviation data exceeds the preset deviation data, and if so, the replacement center displacement is determined based on the displacement deviation data and the preset feature center, and the center displacement of the preset feature center is replaced with the replacement center displacement to obtain the replaced preset feature center, and the gas flow clustering calculation is performed on the gas application data sequence of each gas application environment according to the replaced preset feature center to obtain the gas flow data of each gas application environment.

[0117] For the embodiment of the present application, the flow analysis model is specially designed to analyze the flow data of the gas supply system to identify and understand the characteristics and patterns of the flow. The gas application data sequence is input into the model for in-depth analysis. The model calls its preset feature center (i.e., the initial feature center) to calculate the feature clustering points of the gas application data sequence of each level of gas application environment. The calculation of feature clustering points is a data clustering method that aims to find the inherent patterns and structures in the data sequence, and help identify the key features of the application data of each gas application environment, such as the typical value and distribution range of the gas application flow.

[0118] For the embodiment of the present application, the mean distance calculation is performed between the gas application data sequence and the characteristic clustering points of each gas application environment to obtain the average point distance. The average point distance is an important metric that reflects the average similarity between the overall data sequence and the cluster center. The distance calculation is performed between the gas application data sequence and the characteristic clustering points of each gas application environment, and the distance between the data point and the cluster center (i.e., the characteristic point distance) is measured to evaluate the similarity between each data point and the cluster center. By calculating these distances, the degree of deviation of each data point from the cluster center to which it belongs is understood, and the mean of the characteristic point distance is calculated to obtain the average point distance. For example, if the average point distance is small, it indicates that most of the data points are closely surrounding the cluster center, indicating that the operating state of the gas application environment is relatively stable. The center displacement of the preset characteristic center is adjusted by the average point distance, and the cluster center is optimized so that the cluster center more accurately represents the center of the actual data. The adjusted center displacement is used to update the preset characteristic center, thereby obtaining the adjusted preset characteristic center.

[0119] Step S13: Calculate the gas interference data and gas state data of each gas application environment according to the gas interference information and the gas flow data, and perform feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment.

[0120] Specifically, the harmonic distortion and fluctuation of the gas flow data are calculated according to the gas interference information to obtain the distortion data and application fluctuation data of each gas application environment, the distortion data and application fluctuation data are time-matched respectively to obtain the gas interference data of each gas application environment, the transmission stability of the gas flow data is calculated according to the gas interference information to obtain the stability data of each gas application environment, the stability data of each gas application environment is time-matched respectively to obtain the gas status data of each gas application environment.

[0121] For the embodiment of the present application, the distortion can reflect the impact of the nonlinear flow rate in the gas application environment, and the application fluctuation data shows the flow rate stability.

[0122] Specifically, feature extraction is performed on the gas interference data and the gas state data respectively to obtain the interference feature set and the state feature set of each gas application environment, the correlation coefficient between the gas interference data and the gas state data is calculated, and the interference feature set and the state feature set are feature encoded and vector spliced ​​according to the correlation coefficient to obtain the state feature vector of each gas application environment.

[0123] Specifically, ultrasonic interference coordinates are constructed, and gas interference data is imported into the ultrasonic interference coordinates to obtain an ultrasonic interference curve, wherein the X-axis of the ultrasonic interference coordinates is different unit time, and the Y-axis of the ultrasonic interference coordinates is different unit interference data, curve feature points are extracted from the ultrasonic interference curve to obtain at least two interference feature points, feature clustering analysis is performed on at least two interference feature points to obtain interference clustering results for each interference feature point, and an interference feature set for each gas application environment is generated based on the interference clustering results, ultrasonic state coordinates are constructed, and gas state data is imported into the ultrasonic state coordinates to obtain an ultrasonic state curve, wherein the X-axis of the ultrasonic state coordinates is different unit time, and the Y-axis of the ultrasonic state coordinates is different unit gas state data, curve feature points are identified for the ultrasonic state curve to obtain at least two candidate feature points, standard deviation data of the ultrasonic state curve is calculated, and curve feature points of at least two candidate feature points are screened based on the standard deviation data to obtain state curve feature points, and a state feature set for each gas application environment is generated based on the state curve feature points.

[0124] Specifically, the first mean and the first standard deviation of the gas interference data are calculated, the second mean and the second standard deviation of the gas state data are calculated, and the correlation coefficient between the gas interference data and the gas state data is calculated according to the first mean and the first standard deviation, the second mean and the second standard deviation. The interference feature set is feature encoded and vector converted to obtain the ultrasonic interference feature vector. The state feature set is feature encoded and vector converted to obtain the ultrasonic state feature vector. The ultrasonic interference feature vector and the ultrasonic state feature vector are feature fused according to the correlation coefficient to obtain the state feature vector of each gas application environment.

[0125] Step S14: input the state feature vector into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain a gas meter monitoring result.

[0126] For the embodiment of the present application, the gas meter verification model is a pre-trained neural network model, which is designed to identify abnormal feature vectors in the state feature vector, and after detection, the abnormal feature vectors are marked and the causes of the abnormalities are explained.

[0127] Step S15: perform error warning management on the current NB remote ultrasonic gas meter according to the gas meter monitoring result to obtain an error warning strategy.

[0128] For the embodiment of the present application, obtaining gas application information and gas interference information provides a comprehensive data basis for gas meter management. The gas application information records the gas distribution of the gas meter to different devices, while the gas interference information reveals the factors and their degree that affect the transmission of ultrasonic signals. The combination of the two provides an accurate reference for subsequent data monitoring and flow detection, effectively improving the accuracy and pertinence of data processing. Data monitoring is performed on the gas application information to obtain the gas application data sequence of each device. The continuity and integrity of the data are ensured, and a reliable data source is provided for subsequent flow detection. Flow detection is performed in the order of gas distribution records, which not only ensures the orderliness of the detection, but also improves the detection efficiency, so that the gas flow data of each device can be accurately recorded and analyzed. Gas interference data and gas status data are calculated based on the gas interference information and gas flow data, and feature fusion analysis is performed. Various interference factors in the gas transmission process and the actual gas flow of the equipment are comprehensively considered, thereby more accurately reflecting the true state of each device. Feature fusion analysis further improves the comprehensiveness and readability of the data, and provides strong support for subsequent verification analysis. The state feature vector is input into the preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis, and a comprehensive and in-depth evaluation of the gas meter status is conducted. The results of the calibration analysis not only accurately reflect the current status of the gas meter, but also provide a scientific basis for subsequent error warning management. According to the gas meter monitoring results, the error warning management of the current NB remote ultrasonic gas meter is carried out to obtain the error warning strategy. In this way, possible problems with the gas meter are discovered in time, and corresponding warning measures are taken, effectively avoiding energy waste and safety hazards caused by gas meter errors. The formulation and implementation of the error warning strategy not only improves the accuracy and reliability of the gas meter, but also provides users with a safer and more convenient gas use experience.

[0129] Furthermore, error warning management is performed on the current NB remote ultrasonic gas meter according to the gas meter monitoring results to obtain an error warning strategy, which then includes: collecting management personnel information and gas meter location, and determining the target terminal device based on the management personnel information. The gas meter location information is the geographical location of the gas meter of the current NB remote ultrasonic gas meter. According to the gas meter monitoring results and the preset monitoring standards, the maintenance time range corresponding to the gas meter monitoring results is determined. When it is detected that the error warning strategy is sent to the target terminal device, the arrival time of the management personnel at the gas meter location is recorded, and the arrival time is matched with the maintenance time range to determine whether there is any abnormality in maintenance timeliness. If there is any abnormality in maintenance timeliness, the management personnel information is marked, and the marked management personnel information is sent to the terminal device of the management personnel's upper-level management personnel.

[0130] The following is an introduction to a NB remote ultrasonic gas meter monitoring system provided in an embodiment of the present application. The NB remote ultrasonic gas meter monitoring system described below and the NB remote ultrasonic gas meter monitoring method described above can be referred to each other. Please refer to Figure 2 , Figure 2 2 is a schematic diagram of the structure of a NB remote ultrasonic gas meter monitoring system 20 provided in an embodiment of the present application, including:

[0131] The information acquisition module 21 is used to acquire gas application information and gas interference information. The gas application information is data information of gas distribution recorded by the current NB remote ultrasonic gas meter in different gas application environments. The gas interference information is information of interference factors affecting the ultrasonic signal transmission of the current NB remote ultrasonic gas meter and gas interference degree standards corresponding to the interference factor information.

[0132] The data monitoring module 22 is used to perform data monitoring on different gas application environments in the gas application information to obtain a gas application data sequence for each gas application environment;

[0133] A flow detection module 23 is used to perform gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment;

[0134] The state analysis module 24 is used to calculate the gas interference data and gas state data of each gas application environment according to the gas interference information and the gas flow data, and perform feature fusion analysis on the gas interference data and the gas state data to obtain the state feature vector of each gas application environment;

[0135] The gas analysis module 25 is used to input the state feature vector into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain the gas meter monitoring result;

[0136] The error warning module 26 is used to perform error warning management on the current NB remote ultrasonic gas meter according to the gas meter monitoring result to obtain an error warning strategy.

[0137] In a possible implementation of the embodiment of the present application, the flow detection model 23 performs gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment, specifically for:

[0138] The gas application data sequence of each gas application environment is input into the preset flow analysis model according to the order of gas distribution records, and the preset feature center of the flow analysis model is called to calculate the feature clustering points of the gas application data sequence of each gas application environment to obtain the feature clustering points of each gas application environment;

[0139] The distance mean is calculated between the gas application data sequence and the characteristic clustering points of each gas application environment to obtain the average point distance;

[0140] Adjust the center displacement of the preset feature center based on the average point distance to obtain displacement deviation data;

[0141] Determine whether the displacement deviation data exceeds the preset deviation data, and if so, determine the replacement center displacement based on the displacement deviation data and the preset feature center, and replace the center displacement of the preset feature center with the replacement center displacement to obtain the replaced preset feature center;

[0142] According to the replaced preset feature center, gas flow clustering calculation is performed on the gas application data sequence of each gas application environment to obtain the gas flow data of each gas application environment.

[0143] In another possible implementation of the embodiment of the present application, when the state analysis module 24 calculates the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, it is specifically used to:

[0144] According to the gas interference information, the harmonic distortion and fluctuation of the gas flow data are calculated to obtain the distortion data and application fluctuation data of each gas application environment;

[0145] Perform time series matching on distortion data and application fluctuation data respectively to obtain gas interference data of each gas application environment;

[0146] The transmission stability of the gas flow data is calculated based on the gas interference information to obtain the stability data of each gas application environment;

[0147] The stability data of each gas application environment is time-series matched respectively to obtain the gas status data of each gas application environment.

[0148] In another possible implementation of the embodiment of the present application, the state analysis module 24 performs feature fusion analysis on the gas interference data and the gas state data to obtain the state feature vector of each gas application environment, specifically for:

[0149] Feature extraction is performed on the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment;

[0150] The correlation coefficient between the gas interference data and the gas state data is calculated, and the interference feature set and the state feature set are feature encoded and vector concatenated according to the correlation coefficient to obtain the state feature vector of each gas application environment.

[0151] In another possible implementation of the embodiment of the present application, the state analysis module 24 extracts features from the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment, specifically for:

[0152] Construct ultrasonic interference coordinates, and import gas interference data into the ultrasonic interference coordinates to obtain ultrasonic interference curves. The X-axis of the ultrasonic interference coordinates is different unit time, and the Y-axis of the ultrasonic interference coordinates is different unit interference data.

[0153] Extracting curve feature points from the ultrasonic interference curve to obtain at least two interference feature points;

[0154] Performing feature clustering analysis on at least two interference feature points to obtain an interference clustering result for each interference feature point, and generating an interference feature set for each gas application environment according to the interference clustering result;

[0155] Construct an ultrasonic state coordinate, and import the gas state data into the ultrasonic state coordinate to obtain an ultrasonic state curve, wherein the X-axis of the ultrasonic state coordinate is different unit time, and the Y-axis of the ultrasonic state coordinate is different unit gas state data;

[0156] Identify the characteristic points of the ultrasonic state curve to obtain at least two characteristic points to be selected;

[0157] Calculating standard deviation data of the ultrasonic state curve, and screening curve characteristic points for at least two candidate characteristic points according to the standard deviation data to obtain characteristic points of the state curve;

[0158] A state feature set for each gas application environment is generated based on the characteristic points of the state curve.

[0159] In another possible implementation of the embodiment of the present application, the state analysis module 24 calculates the correlation coefficient between the gas interference data and the gas state data, and performs feature encoding and vector concatenation on the interference feature set and the state feature set according to the correlation coefficient to obtain the state feature vector of each gas application environment, specifically for:

[0160] Calculate the first mean and the first standard deviation of the gas interference data;

[0161] Calculate the second mean and the second standard deviation of the gas state data;

[0162] Calculating a correlation coefficient between the gas interference data and the gas state data according to the first mean and the first standard deviation, the second mean and the second standard deviation;

[0163] Perform feature encoding and vector conversion on the interference feature set to obtain the ultrasonic interference feature vector;

[0164] Perform feature encoding and vector conversion on the state feature set to obtain the ultrasonic state feature vector;

[0165] The ultrasonic interference feature vector and the ultrasonic state feature vector are fused according to the correlation coefficient to obtain the state feature vector of each gas application environment.

[0166] In another possible implementation of the embodiment of the present application, the system 20 further includes: an information collection module, a time determination module, an abnormality determination module and an abnormality handling module, wherein:

[0167] The information collection module is used to collect the management personnel information and the gas meter location, and determine the target terminal device based on the management personnel information. The gas meter location information is the gas meter geographical location of the current NB remote ultrasonic gas meter;

[0168] A time determination module, used to determine a maintenance time range corresponding to the gas meter monitoring result according to the gas meter monitoring result and a preset monitoring standard;

[0169] The abnormality determination module is used to record the arrival time of the manager at the gas meter location after the error warning strategy is detected and sent to the target terminal device, and match the arrival time with the maintenance time range to determine whether there is an abnormality in maintenance timeliness;

[0170] The exception handling module is used to mark the management personnel information when there is an abnormality in maintenance timeliness, and send the marked management personnel information to the terminal device of the management personnel's upper level management personnel

[0171] The present application embodiment provides a NB remote ultrasonic gas meter, such as Figure 3 As shown, Figure 3 A schematic diagram of the structure of a NB remote ultrasonic gas meter provided in an embodiment of the present application. Figure 3 The NB remote ultrasonic gas meter 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the NB remote ultrasonic gas meter 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the NB remote ultrasonic gas meter 300 does not constitute a limitation on the embodiments of the present application.

[0172] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0173] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0174] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0175] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0176] Among them, NB remote ultrasonic gas meters include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The NB remote ultrasonic gas meter shown is merely an example and should not impose any limitation on the functions and scope of use of the embodiments of the present application.

[0177] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0178] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned NB remote ultrasonic gas meter supervision method are implemented.

[0179] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0180] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0181] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A NB remote ultrasonic gas meter monitoring method, characterized in that: include: Obtaining gas application information and gas interference information, wherein the gas application information is data information of gas distribution recorded by the current NB remote ultrasonic gas meter for different gas application environments, and the gas interference information is interference factor information affecting ultrasonic signal transmission of the current NB remote ultrasonic gas meter and gas interference degree standards corresponding to the interference factor information; Performing data monitoring on different gas application environments in the gas application information to obtain a gas application data sequence for each gas application environment; Performing gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain gas flow data of each gas application environment; The gas flow detection is performed on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment, including: Inputting the gas application data sequence of each gas application environment into a preset flow analysis model according to the order of gas distribution records, and calling the preset feature center of the flow analysis model to calculate the feature clustering points of the gas application data sequence of each gas application environment to obtain the feature clustering points of each gas application environment; Calculating the distance mean between the gas application data sequence of each gas application environment and the characteristic clustering points to obtain an average point distance; Adjusting the center displacement of the preset feature center based on the average point distance to obtain displacement deviation data; Determine whether the displacement deviation data exceeds the preset deviation data, and if so, determine a replacement center displacement based on the displacement deviation data and the preset feature center, and replace the center displacement of the preset feature center with the replacement center displacement to obtain a replaced preset feature center; Performing gas flow clustering calculation on the gas application data sequence of each gas application environment according to the replaced preset feature center, to obtain gas flow data of each gas application environment; Calculating the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, and performing feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment; The state characteristic vector is input into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain a gas meter monitoring result; According to the monitoring result of the gas meter, error warning management is performed on the current NB remote ultrasonic gas meter to obtain an error warning strategy.

2. According to claim 1, a NB remote ultrasonic gas meter monitoring method is characterized in that: The calculating the gas interference data and the gas status data of each gas application environment according to the gas interference information and the gas flow data includes: Perform harmonic distortion and fluctuation calculation on the gas flow data according to the gas interference information to obtain distortion data and application fluctuation data of each gas application environment; Performing time series matching on the distortion data and the application fluctuation data respectively to obtain gas interference data of each gas application environment; Performing transmission stability calculation on the gas flow data according to the gas interference information to obtain stability data for each gas application environment; The stability data of each gas application environment is time-series matched respectively to obtain the gas status data of each gas application environment.

3. The NB remote ultrasonic gas meter monitoring method according to claim 1 is characterized in that: The performing feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment includes: Extracting features from the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set for each gas application environment; The correlation coefficient between the gas interference data and the gas state data is calculated, and the interference feature set and the state feature set are feature encoded and vector concatenated according to the correlation coefficient to obtain a state feature vector of each gas application environment.

4. A NB remote ultrasonic gas meter monitoring method according to claim 3, characterized in that: The feature extraction is performed on the gas interference data and the gas state data respectively to obtain an interference feature set and a state feature set of each gas application environment, including: Constructing ultrasonic interference coordinates, and importing the gas interference data into the ultrasonic interference coordinates to obtain an ultrasonic interference curve, wherein the X-axis of the ultrasonic interference coordinates is different unit time, and the Y-axis of the ultrasonic interference coordinates is different unit interference data; Extracting curve feature points from the ultrasonic interference curve to obtain at least two interference feature points; Performing feature clustering analysis on the at least two interference feature points to obtain an interference clustering result for each interference feature point, and generating an interference feature set for each gas application environment according to the interference clustering result; Constructing an ultrasonic state coordinate, and importing the gas state data into the ultrasonic state coordinate to obtain an ultrasonic state curve, wherein the X-axis of the ultrasonic state coordinate is different unit time, and the Y-axis of the ultrasonic state coordinate is different unit gas state data; Performing curve feature point identification on the ultrasonic state curve to obtain at least two feature points to be selected; Calculating standard deviation data of the ultrasonic state curve, and screening the at least two candidate feature points for curve feature points according to the standard deviation data to obtain state curve feature points; A state feature set for each gas application environment is generated according to the state curve feature points.

5. A NB remote ultrasonic gas meter monitoring method according to claim 4, characterized in that: The calculating of the correlation coefficient between the gas interference data and the gas state data, and performing feature encoding and vector concatenation on the interference feature set and the state feature set according to the correlation coefficient to obtain the state feature vector of each gas application environment includes: Calculating a first mean and a first standard deviation of the gas interference data; Calculate a second mean and a second standard deviation of the gas state data; Calculating a correlation coefficient between the gas interference data and the gas state data according to the first mean value and the first standard deviation, the second mean value and the second standard deviation; Performing feature coding and vector conversion on the interference feature set to obtain an ultrasonic interference feature vector; Performing feature encoding and vector conversion on the state feature set to obtain an ultrasonic state feature vector; The ultrasonic interference feature vector and the ultrasonic state feature vector are fused according to the correlation coefficient to obtain a state feature vector of each gas application environment.

6. The NB remote ultrasonic gas meter monitoring method according to claim 1 is characterized in that: The error warning management of the current NB remote ultrasonic gas meter is performed according to the gas meter monitoring result to obtain an error warning strategy, and then further includes: Collecting management personnel information and gas meter location, and determining the target terminal device based on the management personnel information, wherein the gas meter location information is the gas meter geographical location of the current NB remote ultrasonic gas meter; Determine a maintenance time range corresponding to the gas meter monitoring result according to the gas meter monitoring result and the preset monitoring standard; When the error warning strategy is detected and sent to the target terminal device, the arrival time of the manager at the gas meter location is recorded, and the arrival time is matched with the maintenance time range to determine whether there is an abnormality in maintenance timeliness; If there is an abnormality in the timeliness of maintenance, the manager information is marked, and the marked manager information and abnormal information are saved in the local history record of the gas meter, and sent to the terminal device of the manager's upper-level manager at the same time.

7. A NB remote ultrasonic gas meter monitoring system, characterized in that: include: An information acquisition module, used to acquire gas application information and gas interference information, wherein the gas application information is data information of gas distribution records of the current NB remote ultrasonic gas meter for different gas application environments, and the gas interference information is interference factor information affecting ultrasonic signal transmission of the current NB remote ultrasonic gas meter and a gas interference degree standard corresponding to the interference factor information; A data monitoring module, used to perform data monitoring on different gas application environments in the gas application information to obtain a gas application data sequence for each gas application environment; A flow detection module, used to perform gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order, to obtain the gas flow data of each gas application environment; The flow detection module performs gas flow detection on the gas application data sequence of each gas application environment one by one according to the gas distribution record order to obtain the gas flow data of each gas application environment, specifically for: Inputting the gas application data sequence of each gas application environment into a preset flow analysis model according to the order of gas distribution records, and calling the preset feature center of the flow analysis model to calculate the feature clustering points of the gas application data sequence of each gas application environment to obtain the feature clustering points of each gas application environment; Calculating the distance mean between the gas application data sequence of each gas application environment and the characteristic clustering points to obtain an average point distance; Adjusting the center displacement of the preset feature center based on the average point distance to obtain displacement deviation data; Determine whether the displacement deviation data exceeds the preset deviation data, and if so, determine a replacement center displacement based on the displacement deviation data and the preset feature center, and replace the center displacement of the preset feature center with the replacement center displacement to obtain a replaced preset feature center; Performing gas flow clustering calculation on the gas application data sequence of each gas application environment according to the replaced preset feature center, to obtain gas flow data of each gas application environment; a state analysis module, used to calculate the gas interference data and the gas state data of each gas application environment according to the gas interference information and the gas flow data, and perform feature fusion analysis on the gas interference data and the gas state data to obtain a state feature vector of each gas application environment; A gas analysis module, used for inputting the state characteristic vector into a preset gas meter calibration model to perform NB remote ultrasonic gas meter calibration analysis to obtain a gas meter monitoring result; The error warning module is used to perform error warning management on the current NB remote ultrasonic gas meter according to the monitoring result of the gas meter to obtain an error warning strategy.

8. A NB remote ultrasonic gas meter, characterized in that: The NB remote ultrasonic gas meter includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a NB remote ultrasonic gas meter supervision method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a NB remote ultrasonic gas meter monitoring method as described in any one of claims 1-6.

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