NB-IoT-based fuel gas intelligent equipment anomaly analysis system and storage medium

Abstract

The invention relates to the technical field of fuel gas intelligent equipment, and discloses an NB-IoT-based fuel gas intelligent equipment abnormity analysis system and a storage medium, the NB-IoT-based fuel gas intelligent equipment abnormity analysis system comprises a data sensing module used for collecting fuel gas related data and equipment operation conditions in real time, and performing data transmission through an NB-IoT network; the data processing module is used for storing each piece of corresponding data, preprocessing the data acquired by the data sensing module and analyzing and processing the preprocessed data; and the anomaly detection module is used for setting an anomaly detection threshold value and extracting key features of the analyzed and processed data. According to the invention, the prediction subunit generates the prediction data, the comparison subunit compares the prediction data with the data collected in real time to obtain the comparison data, the analysis subunit determines the abnormal data, the processing efficiency is improved, and the abnormal rectification and solving efficiency is improved through the cooperative operation of the modules.

Description

Technical Field

[0001] The present invention relates to the technical field of gas intelligent devices, and particularly to an NB-IoT-based gas intelligent device anomaly analysis system and a storage medium. Background Art

[0002] Gas intelligent devices refer to devices that monitor, control, and manage gas through intelligent technologies (such as the Internet of Things, artificial intelligence, etc.). They are usually used in household, commercial, and industrial environments to improve the safety, efficiency, and convenience of gas, and also improve the gas usage efficiency and reduce the risk of human operation errors.

[0003] When implementing traditional solutions, they detect abnormal situations during gas usage, but cannot specify the type of anomaly and the influencing factors causing the anomaly. When the gas consumption is abnormal, it may be caused by problems such as equipment aging and inaccurate selection. It is impossible to timely understand the type of anomaly and the reason for the anomaly, and the rectification and solution efficiency is relatively low. Therefore, an NB-IoT-based gas intelligent device anomaly analysis system and a storage medium are proposed to solve the above problems. Summary of the Invention

[0004] To make up for the above deficiencies, the present invention provides an NB-IoT-based gas intelligent device anomaly analysis system and a storage medium, aiming to improve the problem that when implementing traditional solutions, it is impossible to timely understand the type of anomaly and the reason for the anomaly, and the rectification and solution efficiency is relatively low.

[0005] In a first aspect, the present invention provides the following technical solution. The NB-IoT-based gas intelligent device anomaly analysis system includes: A data sensing module: used to collect gas-related data and equipment operation conditions in real time, and transmit the data through the NB-IoT network; A data processing module: used to store various corresponding data, preprocess the data collected by the data sensing module, and analyze and process the preprocessed data; An anomaly detection module: used to set the threshold for detecting anomalies, extract the key features of the data after analysis and processing, update them to the data processing module, build a prediction model, and optimize the prediction model according to the key features; An alarm module: used to generate corresponding alarm information, and notify users and maintenance personnel of the alarm information through multimodal communication; An emergency handling module: used to set multiple handling solutions according to different abnormal situations and optimize them, control the corresponding equipment according to the handling solutions, display the gas usage situation and equipment status, and monitor the handling effect of the handling solutions in real time and give feedback.

[0006] Preferably, the data sensing module includes a gas monitoring unit, a data transmission unit, and an equipment monitoring unit. The gas monitoring unit is used to monitor gas data in each gas pipeline. The gas data includes flow rate, pressure, and temperature. The data transmission unit is used to transmit each data through the NB-IoT network. The equipment monitoring unit is used to monitor data of each gas equipment. The data of the gas equipment includes equipment temperature and battery power.

[0007] Preferably, the data processing module includes a database unit, a data preprocessing unit, and a data analysis unit. The database unit is used to store each corresponding data. The data preprocessing unit is used to preprocess the data collected by the data sensing module. The preprocessing includes removing noise and outliers. The data analysis unit is used to analyze and process the preprocessed data.

[0008] Preferably, the data analysis unit includes a prediction subunit, a comparison subunit, and an analysis subunit. The prediction subunit is used to train a prediction model based on each data collected by the data sensing module to generate prediction data. The prediction model includes a time series model and a Prophet model. The comparison subunit is used to compare the prediction data with each data collected in real time by the data sensing module to obtain comparison data. The analysis subunit is used to analyze the comparison data according to a set threshold to determine abnormal data.

[0009] Preferably, the anomaly detection module includes a threshold setting unit, a feature extraction unit, and a model generation unit. The threshold setting unit is used to set a threshold for detecting anomalies. The feature extraction unit is used to extract key features of abnormal data and update them to the data processing module. The model generation unit is used to construct a prediction model and optimize the prediction model according to the key features of abnormal data.

[0010] Preferably, the alarm module includes an alarm generation unit and an alarm notification unit. The alarm generation unit is used to generate corresponding alarm information according to abnormal data. The alarm information includes gas anomaly information and gas equipment anomaly information. The alarm notification unit is used to notify users and maintenance personnel of the alarm information through multimodal communication.

[0011] Preferably, the emergency handling module includes a solution library unit, a control unit, and a monitoring and feedback unit. The solution library unit is used to set multiple handling solutions according to different abnormal situations and optimize them. The control unit is used to control corresponding equipment according to the handling solutions. The equipment includes gas valves and fire-fighting equipment. The monitoring and feedback unit is used to display the gas usage situation and equipment status, and monitor the handling effect of the handling solutions in real time and give feedback.

[0012] Second aspect, an NB-IoT gas intelligent device anomaly analysis method, including the following steps: S1. Data collection: Collect gas-related data and equipment operation conditions to obtain basic data, then collect it in real time and transmit the data through the NB-IoT network; S2. Data preprocessing: Store the basic data, and then perform preprocessing, including removing noise and outliers, constructing a prediction model based on the basic data and setting a threshold for detecting anomalies, and then presetting various processing schemes for different abnormal situations according to the basic data; S3. Anomaly detection: Then train the prediction model according to the real-time collected data to generate prediction data, then compare the prediction data with the real-time collected data to obtain comparison data, then analyze the comparison data through the threshold for detecting anomalies to determine abnormal data, then extract the key features of the abnormal data, update them to the data processing module, and optimize the prediction model; S4. Alarm response: After determining the abnormal data, generate corresponding alarm information according to the abnormal data, including gas anomaly information and gas equipment anomaly information, and notify users and maintenance personnel of the alarm information through multimodal communication; S5. Emergency handling: After generating the alarm information, perform emergency handling according to the preset processing schemes for different abnormal situations, and monitor the handling effect in real time for feedback.

[0013] Third aspect, the present invention provides the following technical solution, a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the above-mentioned NB-IoT gas intelligent device anomaly analysis method.

[0014] Fourth aspect, the present invention provides the following technical solution, a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the above-mentioned NB-IoT gas intelligent device anomaly analysis method.

[0015] The present invention has the following beneficial effects: 1. In the present invention, prediction data is generated by the prediction subunit, the prediction data is compared with each real-time collected data by the comparison subunit to obtain comparison data, abnormal data is determined by the analysis subunit, which helps subsequent anomaly handling and improves the handling efficiency. The feature extraction unit is used to extract the key features of the abnormal data, thereby enhancing the accuracy of anomaly detection. The model generation unit is used to construct and optimize the prediction model, thereby improving the precision of anomaly detection. The cooperation and operation of each module improve the efficiency of anomaly rectification and solution.

[0016] 2. In the present invention, the corresponding alarm information is generated by the alarm generation unit, so as to facilitate understanding of the corresponding abnormal situation, enabling the corresponding personnel to perform targeted maintenance. The alarm information is notified to the corresponding personnel through the alarm notification unit, thereby enhancing the user experience and response speed. The emergency response process is optimized by the solution library unit to improve the processing efficiency. The device status is adjusted in real time by the control unit to ensure safety and quickly respond to abnormalities. The monitoring feedback unit provides comprehensive status feedback to further enhance the user experience, thus enhancing the application prospect and the safety of gas use. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 FIG. is the system architecture diagram of the NB-IoT gas intelligent device anomaly analysis system proposed by the present invention; Figure 2 FIG. is the data sensing module architecture diagram of an image recognition system proposed by the present invention; Figure 3 FIG. is the data processing module architecture diagram of an image recognition system proposed by the present invention; Figure 4 FIG. is the data analysis unit architecture diagram of an image recognition system proposed by the present invention; Figure 5 FIG. is the anomaly detection module architecture diagram of an image recognition system proposed by the present invention; Figure 6 FIG. is the alarm module architecture diagram of an image recognition system proposed by the present invention; Figure 7 FIG. is the emergency processing module architecture diagram of an image recognition system proposed by the present invention; Figure 8 FIG. is the method flow chart of the NB-IoT gas intelligent device anomaly analysis method proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1 Referring to Figure 1 , in the first embodiment of the present invention, the present invention provides an NB-IoT gas intelligent device anomaly analysis system, including: Data sensing module: used for real-time collection of gas-related data and equipment operation conditions, and data transmission through the NB-IoT network; Data processing module: used to store various corresponding data, preprocess the data collected by the data sensing module, and analyze and process the preprocessed data; Abnormal detection module: used to set the threshold for detecting abnormalities, extract the key features of the data after analysis and processing, and update them to the data processing module, build a prediction model and optimize the prediction model according to the key features; Alarm module: used to generate corresponding alarm information and notify users and maintenance personnel of the alarm information through multimodal communication; Emergency handling module: used to set multiple handling schemes according to different abnormal situations and optimize them, control the corresponding equipment according to the handling schemes, display the gas usage situation and equipment status, and monitor the handling effect of the handling schemes in real time and give feedback.

[0020] Specifically, the data sensing module collects gas-related data and equipment operation conditions in real time, and transmits the data through the NB-IoT network to provide real-time monitoring and help detect potential problems in a timely manner; the data processing module stores various corresponding data, preprocesses the data collected by the data sensing module, and analyzes and processes the preprocessed data to improve the quality and reliability of the data and provide an accurate basis for subsequent analysis; the abnormal detection module sets the threshold for detecting abnormalities, extracts the key features of the data after analysis and processing, and updates them to the data processing module, builds a prediction model and optimizes the prediction model according to the key features to accurately identify abnormal data and give early warnings in a timely manner; the alarm module generates corresponding alarm information and notifies users and maintenance personnel of the alarm information through multimodal communication to ensure a timely response to abnormal situations and reduce potential losses; the emergency handling module sets multiple handling schemes according to different abnormal situations and optimizes them, controls the corresponding equipment according to the handling schemes, displays the gas usage situation and equipment status, and monitors the handling effect of the handling schemes in real time and gives feedback to quickly handle abnormal situations; the coordinated operation of each module improves the rectification and solution efficiency of abnormalities, thus improving the problem that when the traditional scheme is implemented, it is impossible to timely understand the type of abnormalities and the reasons for the occurrence of abnormalities, and the rectification and solution efficiency is relatively low.

[0021] Refer to Figure 2 , the data sensing module includes a gas monitoring unit, a data transmission unit, and an equipment monitoring unit. The gas monitoring unit is used to monitor the gas data in each gas pipeline. The gas data includes flow rate, pressure, and temperature. The data transmission unit is used to transmit each data through the NB-IoT network. The equipment monitoring unit is used to monitor the data of each gas equipment. The data of the gas equipment includes equipment temperature and battery power.

[0022] Specifically, the gas monitoring unit monitors the gas data in each gas pipeline to provide accurate gas usage data, which helps to evaluate the operating conditions. The data transmission unit transmits each data through the NB-IoT network to ensure real-time data update for subsequent processing. The equipment monitoring unit monitors the data of each gas equipment to timely understand the equipment operation, prevent failures, and correspondingly extend the equipment life and optimize the maintenance cycle.

[0023] Refer to Figure 3 , the data processing module includes a database unit, a data preprocessing unit, and a data analysis unit. The database unit is used to store each corresponding data. The data preprocessing unit is used to preprocess the data collected by the data sensing module. The preprocessing includes removing noise and outliers. The data analysis unit is used to analyze and process the preprocessed data.

[0024] Specifically, the database unit stores each corresponding data for centralized management, which is convenient for subsequent analysis and provides long-term storage and access to the data. The data preprocessing unit preprocesses the data collected by the data sensing module to improve the accuracy of subsequent analysis. The data analysis unit analyzes and processes the preprocessed data to extract valuable information and trends and enhance the decision-making support ability.

[0025] Refer to Figure 4 , the data analysis unit includes a prediction subunit, a comparison subunit, and an analysis subunit. The prediction subunit is used to train the prediction model based on each data collected by the data sensing module to generate prediction data. The prediction model includes a time series model and a Prophet model. The comparison subunit is used to compare the prediction data with each data collected by the data sensing module in real time to obtain comparison data. The analysis subunit is used to analyze the comparison data according to the set threshold to determine the abnormal data.

[0026] Specifically, the prediction subunit trains the prediction model based on each data collected by the data sensing module to generate prediction data, thereby generating predictions of future data, which helps to identify potential problems in advance. The comparison subunit compares the prediction data with each data collected by the data sensing module in real time to obtain comparison data, thereby identifying the situation of gas data and equipment data. The analysis subunit analyzes the comparison data according to the set threshold to determine the abnormal data, thereby determining the nature of the abnormal data, which helps with subsequent abnormal processing and improves the processing efficiency.

[0027] Refer to Figure 5The anomaly detection module includes a threshold setting unit, a feature extraction unit, and a model generation unit. The threshold setting unit is used to set the threshold for detecting anomalies. The feature extraction unit is used to extract the key features of the abnormal data and update them to the data processing module. The model generation unit is used to build a prediction model and optimize the prediction model according to the key features of the abnormal data.

[0028] Specifically, a threshold value for detecting anomalies is set by a threshold setting unit, thereby providing a basis for anomaly detection, key features of abnormal data are extracted by a feature extraction unit and updated to a data processing module, thereby enhancing the accuracy of anomaly detection, a prediction model is constructed by a model generation unit, and the prediction model is optimized according to the key features of the abnormal data, thereby improving the accuracy of anomaly detection and thus improving the efficiency of anomaly rectification and resolution.

[0029] Reference Figure 6 The alarm module includes an alarm generation unit and an alarm notification unit. The alarm generation unit is used to generate corresponding alarm information according to abnormal data. The alarm information includes gas abnormality information and gas equipment abnormality information. The alarm notification unit is used to notify users and maintenance personnel of the alarm information through multimodal communication.

[0030] Specifically, the alarm generation unit generates corresponding alarm information based on the abnormal data, which makes it easier to understand the corresponding abnormal situation so that the corresponding personnel can perform targeted maintenance. The alarm notification unit notifies the user and maintenance personnel of the alarm information through multimodal communication, thereby notifying the user and maintenance personnel in time, enhancing the user experience and response speed.

[0031] Reference Figure 7 The emergency handling module includes a solution library unit, a control unit, and a monitoring feedback unit. The solution library unit is used to set and optimize multiple handling solutions according to different abnormal situations. The control unit is used to control the corresponding equipment according to the handling solution. The equipment includes gas valves and fire-fighting equipment. The monitoring feedback unit is used to display the gas usage and equipment status, and monitor the handling effect of the handling solution in real time and provide feedback.

[0032] Specifically, the solution library unit sets and optimizes a variety of processing solutions according to different abnormal situations, thereby optimizing the emergency response process, improving processing efficiency, and reducing emergency processing time. The control unit controls the corresponding equipment according to the processing solution, thereby adjusting the equipment status in real time to ensure safety, reduce the accident rate, and respond to abnormalities quickly. The monitoring feedback unit displays the gas usage and equipment status, monitors the processing effect of the processing solution in real time, and provides feedback, thereby providing comprehensive status feedback and further improving the user experience.

[0033] Embodiment 2: ReferenceFigure 8 , in the second embodiment of the present invention, the present invention provides an NB-IoT gas intelligent device anomaly analysis method, including the following steps: S1. Data collection: Collect gas-related data and the operating conditions of the device to obtain basic data, and then collect it in real time and transmit the data through the NB-IoT network; S2. Data preprocessing: Store the basic data and then perform preprocessing, including removing noise and outliers, constructing a prediction model based on the basic data and setting a threshold for detecting anomalies, and then presetting various processing schemes for different abnormal situations according to the basic data; S3. Anomaly detection: Then train the prediction model according to the real-time collected data to generate prediction data, then compare the prediction data with the real-time collected data to obtain comparison data, then analyze the comparison data through the threshold for detecting anomalies to determine the abnormal data, then extract the key features of the abnormal data, update them to the data processing module, and optimize the prediction model; S4. Alarm response: After determining the abnormal data, generate corresponding alarm information according to the abnormal data, including gas anomaly information and gas device anomaly information, and notify users and maintenance personnel of the alarm information through multimodal communication; S5. Emergency handling: After the alarm information is generated, perform emergency handling according to the preset processing schemes for different abnormal situations and monitor the processing effect in real time for feedback.

[0034] Embodiment Three In the third embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the steps of the NB-IoT gas intelligent device anomaly analysis method in the above embodiment.

[0035] Embodiment Four In the fourth embodiment of the present invention, based on the same inventive concept, the present invention proposes a computer device, including: a processor and a memory; the processor and the memory communicate with each other; the memory is used to store instructions; the processor is used to execute the instructions in the memory to execute the NB-IoT gas intelligent device anomaly analysis method in the above embodiment.

[0036] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0037] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Based on NB-IoT gas intelligent equipment abnormality analysis system, it is characterized by: include: Data sensor module: used to collect gas-related data and equipment operation status in real time, and transmit data through the NB-IoT network; Data processing module: used to store various corresponding data, pre-process the data collected by the data sensing module, and analyze and process the pre-processed data; Anomaly detection module: used to set the threshold for detecting anomalies, extract the key features of the analyzed and processed data, and update them to the data processing module, build a prediction model and optimize the prediction model based on the key features; Alarm module: used to generate corresponding alarm information and notify users and maintenance personnel of the alarm information through multimodal communication; Emergency processing module: used to set and optimize multiple processing plans according to different abnormal situations, control the corresponding equipment according to the processing plan, display the gas usage and equipment status, and monitor the processing effect of the processing plan in real time and provide feedback.

2. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 1 is characterized in that: The data sensing module includes a gas monitoring unit, a data transmission unit, and an equipment monitoring unit. The gas monitoring unit is used to monitor the gas data in each gas pipeline, and the gas data includes flow, pressure, and temperature. The data transmission unit is used to transmit various data through the NB-IoT network. The equipment monitoring unit is used to monitor the data of each gas equipment, and the data of the gas equipment includes equipment temperature and battery power.

3. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 1 is characterized in that: The data processing module includes a database unit, a data preprocessing unit, and a data analysis unit. The database unit is used to store various corresponding data. The data preprocessing unit is used to preprocess the data collected by the data sensing module, and the preprocessing includes removing noise and outliers. The data analysis unit is used to analyze and process the preprocessed data.

4. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 3 is characterized in that: The data analysis unit includes a prediction subunit, a comparison subunit, and an analysis subunit. The prediction subunit is used to train the prediction model according to the various data collected by the data sensing module to generate prediction data. The prediction model includes a time series model and a prophet model. The comparison subunit is used to compare the prediction data with the various data collected in real time by the data sensing module to obtain comparison data. The analysis subunit is used to analyze the comparison data according to a set threshold and determine abnormal data.

5. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 1 is characterized in that: The anomaly detection module includes a threshold setting unit, a feature extraction unit, and a model generation unit. The threshold setting unit is used to set a threshold for detecting anomalies. The feature extraction unit is used to extract key features of abnormal data and update them to the data processing module. The model generation unit is used to construct a prediction model and optimize the prediction model according to the key features of the abnormal data.

6. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 1 is characterized in that: The alarm module includes an alarm generation unit and an alarm notification unit. The alarm generation unit is used to generate corresponding alarm information based on abnormal data. The alarm information includes gas abnormality information and gas equipment abnormality information. The alarm notification unit is used to notify users and maintenance personnel of the alarm information through multimodal communication.

7. The abnormality analysis system based on NB-IoT gas intelligent equipment according to claim 1 is characterized in that: The emergency processing module includes a solution library unit, a control unit, and a monitoring feedback unit. The solution library unit is used to set and optimize multiple processing solutions according to different abnormal situations. The control unit is used to control corresponding equipment according to the processing solutions. The equipment includes gas valves and fire-fighting equipment. The monitoring feedback unit is used to display gas usage and equipment status, monitor the processing effect of the processing solution in real time, and provide feedback.

8. The abnormality analysis method of gas intelligent equipment based on NB-IoT is characterized by: The NB-IoT-based gas smart device abnormality analysis system applied to any one of claims 1 to 7 comprises the following steps: S1. Data collection: Collect gas-related data and equipment operation status to obtain basic data, which is then collected in real time and transmitted through the NB-IoT network; S2. Data preprocessing: The basic data is stored and then preprocessed, including removing noise and outliers, building a prediction model based on the basic data and setting thresholds for detecting anomalies, and then presetting multiple processing solutions for different abnormal situations based on the basic data; S3, anomaly detection: then train the prediction model according to the real-time collected data to generate prediction data, then compare the prediction data with the real-time collected data to obtain comparison data, then analyze the comparison data through the threshold of detecting anomalies to determine the abnormal data, then extract the key features of the abnormal data, update to the data processing module, and optimize the prediction model; S4, alarm response: after determining the abnormal data, generate corresponding alarm information according to the abnormal data, including gas abnormal information and gas equipment abnormal information, and notify the user and maintenance personnel of the alarm information through multimodal communication; S5. Emergency processing: After the alarm information is generated, emergency processing is carried out according to the preset processing plans for different abnormal situations, and the processing effect is monitored in real time for feedback.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the NB-IoT-based gas smart device abnormality analysis method according to claim 8 is implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by the processor, the NB-IoT-based gas smart device abnormality analysis method according to claim 8 is implemented.

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