Gas safety monitoring system based on Internet of Things

Abstract

The invention discloses a gas safety monitoring system based on the Internet of Things, and relates to the technical field of monitoring and alarming, the gas safety monitoring system comprises a monitoring and alarming platform, the monitoring and alarming platform is in communication connection with a sensor module, a communication module, an abnormity analysis module, an alarming and response module and a user interaction module, and the modules are in electric signal connection; the sensor module is used for collecting key parameters in a gas environment, including gas concentration, temperature and pressure. By collecting key parameters such as gas concentration, environment temperature, humidity, pressure and equipment state, and utilizing data analysis to quickly identify potential safety hazards, detect abnormities and trigger early warning by the system, an operator can take measures before an accident occurs, so that disastrous consequences are effectively avoided, and the safety of the operator is improved. Moreover, the time delay from detection to response is greatly reduced, and the response speed to gas safety accidents is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and alarming, and in particular to a gas safety monitoring system based on the Internet of Things. Background Art

[0002] With the popularization of gas use, safety accidents such as gas leakage and explosion occur frequently, seriously threatening people's lives and property. According to statistics, natural gas safety accidents have become the largest safety accidents after traffic accidents and industrial accidents. These accidents not only bring great harm to individuals and families, but also cause immeasurable losses to society. The rapid development of Internet of Things technology has provided new solutions for gas safety monitoring. The Internet of Things uses information sensing equipment to exchange and communicate information with any object according to the agreed protocol to realize intelligent identification, positioning, tracking, supervision and other functions. In the field of gas safety monitoring, Internet of Things technology can realize real-time, remote and automated monitoring and early warning, greatly improving the efficiency and accuracy of gas safety supervision.

[0003] In the existing technology, gas monitoring alarms are easily affected by environmental factors, resulting in false alarms and missed alarms, which in turn increases the risk of safety accidents. Therefore, how to accurately realize abnormal gas monitoring state alarms under the interference and influence of environmental factors is the problem we need to solve. To this end, a gas safety monitoring system based on the Internet of Things is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide a gas safety monitoring system based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The gas safety monitoring system based on the Internet of Things includes a monitoring and alarm platform, wherein the monitoring and alarm platform is communicatively connected with a sensor module, a communication module, an abnormality analysis module, an alarm and response module, and a user interaction module, wherein electrical signals are connected between the modules;

[0007] The sensor module is used to collect key parameters in the gas environment, including gas concentration, temperature, and pressure. Through the sensor module, the system can promptly detect abnormal conditions such as gas leakage, overpressure, and overtemperature, and provide basic data support for subsequent alarms and processing;

[0008] The communication module is used to transmit the data collected by the sensor module to the abnormal analysis module through the wireless network, ensuring real-time transmission and remote monitoring of the data, so that users can grasp the gas safety status anytime and anywhere. At the same time, the communication stability and reliability of the system are improved through the design of redundant communication links and backup servers;

[0009] The anomaly analysis module is used to mine and analyze the received data, obtain features related to gas safety, and apply machine learning to perform anomaly analysis on the data, identify and classify anomaly categories in the data, and more accurately judge the changing trend and potential risks of gas safety status, so as to provide strong support for subsequent decision-making and optimization. The gas safety-related features include gas concentration features, environmental parameter features, and equipment status features;

[0010] The abnormality analysis module includes a feature extraction unit, a data collection unit and an abnormality analysis unit; wherein the feature extraction unit is used to extract abnormal state features from the data collected by the sensor module; the data collection unit is used to classify the abnormal state feature data obtained by the feature extraction unit to obtain a data sequence table; the abnormality analysis unit is used to combine the data sequence table and the data collected by the sensor module to obtain an abnormal state warning coefficient, and perform a safety analysis of the gas system according to the abnormal state warning coefficient;

[0011] The alarm and response module is used to receive the abnormal analysis results of the abnormal analysis module, process the abnormal analysis results, classify the alarm levels, and trigger corresponding alarm signals according to the alarm levels to control relevant equipment for emergency processing, so as to realize intelligent monitoring and automatic response to the gas safety status, and can discover and deal with safety hazards in the first place, and effectively avoid the occurrence of gas accidents;

[0012] The user interaction module is used to provide users with a visual display of the system status, the reception and confirmation of alarm information, which improves the system's usability and user experience, allowing users to more conveniently understand the gas safety status and perform corresponding operations.

[0013] A further improvement of the technical solution of the present invention is that: in the sensor module, the process of collecting key parameters in the gas environment is:

[0014] Based on various types of sensors deployed in the gas use environment, the gas concentration, ambient temperature, and pipeline pressure are monitored and converted into electrical signals, which represent the actual status of the gas environment.

[0015] Perform pre-processing operations such as filtering and amplification on the raw data collected by the sensor to improve signal quality;

[0016] The processed data is transmitted to the communication module through the communication interface inside the sensor module.

[0017] A further improvement of the technical solution of the present invention is that: in the communication module, the process of transmitting data is:

[0018] The communication module receives pre-processed data from the sensor module and monitors the signal strength, bandwidth, and delay status of the current wireless network to ensure communication quality;

[0019] Design multiple communication links, divided into main communication links and backup links, and equip backup servers. When problems occur in the main communication link or main server, they can be switched to the backup link or backup server;

[0020] Encapsulate and encrypt the received pre-processed data, and send the encapsulated data to the abnormality analysis module via a wireless network;

[0021] After sending the data, obtain feedback and confirmation information from the exception analysis module to confirm whether the data is sent successfully. If the data fails to be sent, retransmit it until the data is successfully delivered or the retransmission limit is reached. If the data fails to be retransmitted multiple times, record the error information and notify the system administrator.

[0022] A further improvement of the technical solution of the present invention is that the process of extracting abnormal state features and obtaining a data sequence table is as follows:

[0023] Receive the encapsulated data sent by the communication module and parse the data packet to obtain the original monitoring value;

[0024] Clean the extracted data, remove invalid or erroneous data points, remove noise, missing values ​​or outliers, and ensure the accuracy and completeness of the data;

[0025] Features related to gas safety are extracted from the original data, namely gas concentration features, environmental parameter features, and equipment status features. Among them, gas concentration features include gas concentration value, concentration change trend, and number of concentration abnormality threshold breakthroughs, which are used to assess the risk of gas leakage; environmental parameter features include temperature, humidity, and pressure; equipment status features include sensor working status, detection accuracy, and fault records, which are used to assess the reliability and accuracy of data collection;

[0026] Analyze the extracted gas safety-related feature data and distinguish different abnormal status categories;

[0027] The classified abnormal status data are sorted in order of severity, and based on the sorted data, a data sequence table is constructed, which includes the occurrence time, location, abnormality type, and impact scope of the abnormal status.

[0028] A further improvement of the technical solution of the present invention is that: in the abnormality analysis unit, the process of performing safety analysis of the gas system according to the abnormal state warning coefficient is:

[0029] Integrate the abnormal state data in the data sequence table and the data collected by the sensor module, and perform feature matching between the data collected by the sensor module and the abnormal type in the data sequence table to identify the abnormal state;

[0030] Based on the extracted abnormal state data and the data collected by the sensor module, an abnormal state assessment model is constructed. The identified abnormal data is further classified and processed to distinguish different types of abnormalities, determine the severity of the abnormalities, analyze the changing trend of gas safety conditions, and predict potential risks.

[0031] Using the trained abnormal state assessment model, the data collected by the sensor module is processed and analyzed, the abnormal state warning coefficient is calculated, and the similarity between the current state and the abnormal state is analyzed;

[0032] Based on the abnormal state warning coefficient, the safety risk of the gas system is evaluated and the specific abnormal state existing in the current gas system is identified.

[0033] A further improvement of the technical solution of the present invention is that the process of obtaining the abnormal state warning coefficient is:

[0034] Collect gas concentration data at each time point in the monitoring period, determine the maximum and minimum gas concentrations in the monitoring period, and calculate the change in gas concentration at each time point. Based on the acquired data, calculate the sum of squares of the deviations between the gas concentration and the average value, take the square root, analyze the average deviation, and then calculate the sum of squares of the change in gas concentration, analyze the change trend, and combine the average deviation and the change trend to obtain the gas concentration anomaly index;

[0035] Collect the ambient temperature, humidity, and pressure data at each time point during the monitoring period, determine the standard values ​​of temperature, humidity, and pressure, calculate the comprehensive deviation of the three environmental parameters, square the comprehensive deviation, calculate the sum, and divide the sum of squares of the deviations by the total number of time points during the monitoring period to obtain the environmental parameter anomaly index;

[0036] Collect sensor working status, detection accuracy and equipment failure record data at each time point in the monitoring period, calculate the proportion of faulty sensors, and calculate the inverse of the detection accuracy at each time point, find the average, calculate the sum of equipment failure records, and divide it by the total number of time points in the monitoring period, combine the sensor failure rate, the inverse of the detection accuracy and the equipment failure rate to obtain the equipment status abnormality index;

[0037] According to the calculated gas concentration abnormality index, environmental parameter abnormality index, and equipment status abnormality index, corresponding weights are set, each abnormality index is multiplied by its weight, and then summed to obtain the abnormal status warning coefficient.

[0038] A further improvement of the technical solution of the present invention is that the calculation formula of the abnormal gas concentration index is:

[0039]

[0040] Among them, A C is the gas concentration abnormality index, C t is the gas concentration at time t, C max is the maximum value of gas concentration during the monitoring period, C min is the minimum value of gas concentration during the monitoring period, ΔC t is the change in gas concentration at time t, ΔC t =C t -C t-1 ,∈ is a small constant to prevent the denominator from being zero, and n is the total number of time points in the monitoring period;

[0041] The calculation formula of the environmental parameter abnormality index is:

[0042]

[0043] Among them, A E is the environmental parameter anomaly index, T t is the ambient temperature at time t, H t is the ambient humidity at time t, P t is the ambient pressure at time t, T th , H th , P th Respectively represent the standard values ​​of temperature, humidity and pressure;

[0044] The calculation formula of the device status abnormality index is:

[0045]

[0046] Among them, A D is the device status abnormality index, S t is the working state of the sensor at time point t, 0 indicates failure, 1 indicates normal, A cct is the sensor detection accuracy at time point t, F t is the equipment failure record at time point t.

[0047] A further improvement of the technical solution of the present invention is that the calculation formula of the abnormal state warning coefficient is:

[0048] R=w1·A C +w2·A E +w3·A D ;

[0049] Among them, R is the abnormal state warning coefficient, AC is the gas concentration abnormality index, A E is the environmental parameter anomaly index, A D is the equipment status abnormality index, w1 is the weight of the gas concentration abnormality index, w2 is the weight of the environmental parameter abnormality index, w3 is the weight of the equipment status abnormality index, w1+w2+w3=1.

[0050] A further improvement of the technical solution of the present invention is that in the alarm and response module, the emergency handling process is:

[0051] Receive abnormal analysis data on gas concentration, environmental parameters, and equipment status from the abnormal analysis module, and format and parse the received data to ensure data availability;

[0052] According to the preset risk assessment standards, abnormal situations are divided into different alarm levels, namely warning alarm level, serious alarm level, and emergency alarm level;

[0053] Set a corresponding alarm threshold for each alarm level. When the alarm threshold is exceeded, the corresponding level of alarm will be triggered;

[0054] Analyze the abnormal analysis results of the abnormal analysis module, combine the value of the abnormal state warning coefficient, determine the specific alarm level of the abnormal situation, and generate the corresponding alarm signal;

[0055] The alarm signal is transmitted to the user interaction module to notify relevant personnel, and according to the alarm level, the preset emergency response measures are automatically triggered, and relevant equipment is controlled through the interface, including automatically cutting off the gas supply and starting emergency exhaust.

[0056] A further improvement of the technical solution of the present invention is that: the multiple alarm levels correspond to the multiple alarm thresholds, wherein the alarm thresholds include an upper threshold and a lower threshold;

[0057] The multiple alarm levels and the multiple alarm thresholds satisfy the following relationship:

[0058] Warning alarm level 0 <R≤R T1 ; Trigger a yellow alarm, corresponding to a low-level warning, start enhanced monitoring mode, record a log, and notify technicians;

[0059] Severe alarm level R T1 <R≤R T2 ; Triggering an orange alert, corresponding to a medium-level warning, initiates some safety measures, such as reducing gas flow and activating backup ventilation systems;

[0060] Emergency alarm level R>R T2; Triggering a red alarm, corresponding to a high-level warning, immediately cuts off the gas supply and initiates full emergency procedures, such as evacuating personnel and notifying emergency services;

[0061] Among them, R is the abnormal state warning coefficient, R T1 is the lower threshold corresponding to the severe alarm level and the upper threshold corresponding to the warning alarm level, R T2 It is the lower threshold corresponding to the emergency alarm level and the upper threshold corresponding to the serious alarm level.

[0062] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0063] 1. The present invention provides a gas safety monitoring system based on the Internet of Things. By collecting key parameters such as gas concentration, ambient temperature, humidity, pressure and equipment status, the system uses data analysis to quickly identify potential safety hazards. When an abnormality is detected, the system triggers an early warning, allowing operators to take measures before an accident occurs, effectively avoiding catastrophic consequences, and greatly reducing the time delay from detection to response, thereby improving the response speed to gas safety accidents.

[0064] 2. The present invention provides a gas safety monitoring system based on the Internet of Things. By integrating multiple factors such as gas concentration, environmental parameters, and equipment status, the system generates an abnormal state warning coefficient, evaluates the gas safety status, and helps operators fully understand the safety level of the system. In addition, the system sets corresponding alarm levels according to different warning coefficients to achieve graded warnings, so that operators can take corresponding preventive and response measures according to the warning levels, thereby improving the scientificity and systematicness of gas safety management.

[0065] 3. The present invention provides a gas safety monitoring system based on the Internet of Things. When an abnormal state is detected, in addition to triggering an alarm signal, it can also automatically execute corresponding measures according to a preset emergency response strategy, such as cutting off the gas supply, starting emergency exhaust, etc. The automatic emergency handling mechanism greatly improves the efficiency of handling gas safety accidents and reduces the uncertainty and delay of manual operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0067] Figure 1 It is a module diagram of the present invention;

[0068] Figure 2This is a flow chart of the present invention for performing safety analysis on a gas system according to an abnormal state warning coefficient;

[0069] Figure 3 This is a flow chart for obtaining the abnormal state warning coefficient of the present invention;

[0070] Figure 4 It is the emergency treatment flow chart of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Embodiment 1, as Figure 1-3 As shown, the present invention provides a gas safety monitoring system based on the Internet of Things, including a monitoring alarm platform, the monitoring alarm platform is communicatively connected with a sensor module, a communication module, an abnormality analysis module, an alarm and response module, and a user interaction module, wherein the modules are connected by electrical signals;

[0073] The sensor module is used to collect key parameters in the gas environment, including gas concentration, temperature, and pressure. Through the sensor module, the system can promptly detect abnormal conditions such as gas leakage, overpressure, and overtemperature, and provide basic data support for subsequent alarms and processing. Based on various types of sensors deployed in the gas use environment, the system monitors and obtains gas concentration, ambient temperature, and pipeline pressure, and converts the physical quantities monitored by the sensor into electrical signals. The electrical signals represent the actual state in the gas environment. The raw data collected by the sensor is pre-processed by filtering and amplifying to improve the signal quality, and the processed data is transmitted to the communication module through the communication interface inside the sensor module;

[0074] The communication module is used to transmit the data collected by the sensor module to the abnormal analysis module through the wireless network, ensuring the real-time transmission and remote monitoring of the data, so that the user can grasp the gas safety status anytime and anywhere. At the same time, the communication stability and reliability of the system are improved through the design of redundant communication links and backup servers. The communication module receives the pre-processed data from the sensor module and monitors the signal strength, bandwidth and delay status of the current wireless network to ensure the communication quality. Multiple communication links are designed, which are divided into main communication links and backup links, and equipped with backup servers. When the main communication link or the main server has problems, it can switch to the backup link or backup server, encapsulate and encrypt the received pre-processed data, and send the encapsulated data to the abnormal analysis module through the wireless network. After sending the data, the feedback and confirmation information of the abnormal analysis module are obtained to confirm whether the data is successfully sent. If the data fails to be sent, it is retransmitted until the data is successfully delivered or the retransmission limit is reached. If the data fails to be retransmitted multiple times, the error information is recorded and the system administrator is notified;

[0075] The anomaly analysis module is used to mine and analyze the received data, obtain features related to gas safety, and apply machine learning to perform anomaly analysis on the data, identify and classify the anomaly categories in the data, and more accurately judge the changing trends and potential risks of gas safety conditions, providing strong support for subsequent decision-making and optimization. Gas safety-related features include gas concentration features, environmental parameter features, and equipment status features;

[0076] The abnormality analysis module includes a feature extraction unit, a data collection unit and an abnormality analysis unit; wherein the feature extraction unit is used to extract abnormal state features from the data collected by the sensor module; the data collection unit is used to classify the abnormal state feature data obtained by the feature extraction unit and obtain a data sequence table; the abnormality analysis unit is used to combine the data sequence table and the data collected by the sensor module to obtain the abnormal state warning coefficient, and perform safety analysis of the gas system according to the abnormal state warning coefficient, receive the encapsulated data sent by the communication module, parse the data packet to obtain the original monitoring value, clean the extracted data, eliminate invalid or erroneous data points, remove noise, missing values ​​or abnormal values, ensure the accuracy and integrity of the data, and Features related to gas safety are extracted from the data, namely gas concentration features, environmental parameter features, and equipment status features. Among them, gas concentration features include gas concentration value, concentration change trend, and the number of times the concentration abnormal threshold is exceeded, which are used to assess the risk of gas leakage; environmental parameter features include temperature, humidity, and pressure; equipment status features include the working status of the sensor, detection accuracy, and fault records, which are used to assess the reliability and accuracy of data collection, analyze the extracted gas safety-related feature data, distinguish different abnormal status categories, and sort the classified abnormal status data in order of severity. Based on the sorted data, a data sequence table is constructed, which includes the occurrence time, location, abnormality type, and impact range of the abnormal state;

[0077] Furthermore, the process of conducting gas system safety analysis based on the abnormal state warning coefficient is as follows:

[0078] Integrate the abnormal state data in the data sequence table and the data collected by the sensor module, and perform feature matching between the data collected by the sensor module and the abnormal type in the data sequence table to identify the abnormal state. Based on the extracted abnormal state data and the data collected by the sensor module, build an abnormal state assessment model. For the identified abnormal data, further classify and process it, distinguish different types of abnormalities, judge the severity of the abnormalities, analyze the changing trend of the gas safety status, predict potential risks, use the trained abnormal state assessment model to process and analyze the data collected by the sensor module, calculate the abnormal state warning coefficient, analyze the similarity between the current state and the abnormal state, and based on the abnormal state warning coefficient, assess the safety risk of the gas system and identify the specific abnormal state existing in the current gas system.

[0079] Furthermore, the process of obtaining the abnormal state warning coefficient is as follows:

[0080] Collect gas concentration data at each time point in the monitoring period, determine the maximum and minimum gas concentrations during the monitoring period, and calculate the change in gas concentration at each time point. Calculate the sum of squares of the deviations between the gas concentration and the average value based on the acquired data, take the square root, analyze the average deviation, and then calculate the sum of squares of the change in gas concentration. Analyze the change trend, combine the average deviation and the change trend to obtain the gas concentration anomaly index, collect ambient temperature, humidity, and pressure data at each time point in the monitoring period, determine the standard values ​​of temperature, humidity, and pressure, obtain the comprehensive deviation of the three environmental parameters, square the comprehensive deviation, calculate the sum, and compare the sum of squares of the deviation with the monitoring period. Divide the total number of time points in the monitoring period to obtain the environmental parameter abnormality index, collect the sensor working status, detection accuracy and equipment failure record data at each time point in the monitoring period, calculate the proportion of faulty sensors, and calculate the inverse of the detection accuracy at each time point, find the average, calculate the sum of the equipment failure records, and divide it by the total number of time points in the monitoring period, combine the sensor failure rate, the inverse of the detection accuracy and the equipment failure rate to obtain the equipment status abnormality index, set the corresponding weights according to the calculated gas concentration abnormality index, environmental parameter abnormality index and equipment status abnormality index, multiply each abnormality index by its weight, and then sum them to obtain the abnormal state warning coefficient;

[0081] Furthermore, the calculation formula of the gas concentration abnormality index is:

[0082]

[0083] Among them, A C is the gas concentration abnormality index, C t is the gas concentration at time t, C max is the maximum value of gas concentration during the monitoring period, C min is the minimum value of gas concentration during the monitoring period, ΔC t is the change in gas concentration at time t, ΔC t =C t -C t-1 ,∈ is a small constant to prevent the denominator from being zero, n is the total number of time points in the monitoring period, and it should be noted that the gas concentration abnormality A C Taking into account the changing trend and volatility of gas concentration, A C The value range is from 0 to positive infinity. When the gas concentration is close to its maximum or minimum value, A C The greater the square sum of the gas concentration changes, the greater the concentration fluctuation. C Also the larger it is, the higher the risk of abnormality is indicated;

[0084] The calculation formula of environmental parameter anomaly index is:

[0085]

[0086] Among them, A E is the environmental parameter anomaly index, T t is the ambient temperature at time t, H t is the ambient humidity at time t, P t is the ambient pressure at time t, T th , H th , P th Represent the standard values ​​of temperature, humidity and pressure respectively. It should be noted that the abnormality of environmental parameters A E Taking into account the deviations of temperature, humidity and pressure, A E The value range is from 0 to positive infinity. When any environmental parameter T t , H t , P t Deviation from its standard value T th , H th , P th When A E Increase, indicating that the abnormality of environmental parameters increases. If all environmental parameters are maintained near their standard values, A E Close to 0, indicating that environmental parameters are normal;

[0087] The calculation formula of the equipment status abnormality index is:

[0088]

[0089] Among them, A D is the device status abnormality index, S t is the working state of the sensor at time point t, 0 indicates failure, 1 indicates normal, A cct is the sensor detection accuracy at time point t, F t is the equipment failure record at time t. It should be noted that the equipment status abnormality A D Considering the working status, detection accuracy and fault record of the sensor, A D The value range is from 0 to positive infinity. t is 0, that is, when the sensor fails, A D Increase, indicating that the abnormality of the equipment status has increased, and the inverse of the sensor detection accuracy The larger the value, the lower the accuracy. D The larger the device failure record F t The higher the F t The higher it is, the more abnormal the device status is;

[0090] Furthermore, the calculation formula of the abnormal state warning coefficient is:

[0091] R=w1·AC +w2·A E +w3·A D ;

[0092] Among them, R is the abnormal state warning coefficient, A C is the gas concentration abnormality index, A E is the environmental parameter anomaly index, A D is the equipment status abnormality index, w1 is the weight of the gas concentration abnormality index, w2 is the weight of the environmental parameter abnormality index, and w3 is the weight of the equipment status abnormality index. w1+w2+w3=1. It should be noted that the three abnormality indexes A C , A E , A D and the comprehensive warning coefficient R are both non-negative values, and the larger the value, the higher the risk level of the system;

[0093] The alarm and response module is used to receive the abnormal analysis results of the abnormal analysis module, process the abnormal analysis results, classify the alarm levels, and trigger the corresponding alarm signals according to the alarm levels to control the relevant equipment for emergency processing, so as to realize intelligent monitoring and automatic response to the gas safety status, and be able to discover and deal with safety hazards in the first place, effectively avoiding the occurrence of gas accidents;

[0094] The user interaction module is used to provide users with a visual display of the system status, the reception and confirmation of alarm information, which improves the system's usability and user experience, allowing users to more easily understand the gas safety status and perform corresponding operations.

[0095] Embodiment 2, as Figure 4 As shown, based on Example 1, the present invention provides a technical solution: Preferably, in the alarm and response module, the emergency handling process is:

[0096] Receive abnormal analysis data on gas concentration, environmental parameters, and equipment status from the abnormal analysis module, and format and parse the received data to ensure data availability. According to the preset risk assessment standards, the abnormal situation is divided into different alarm levels, namely warning alarm level, serious alarm level, and emergency alarm level. A corresponding alarm threshold is set for each alarm level. When the alarm threshold is exceeded, an alarm of the corresponding level will be triggered. The abnormal analysis results of the abnormal analysis module are parsed, and the specific alarm level of the abnormal situation is determined in combination with the value of the abnormal state warning coefficient. The corresponding alarm signal is generated and transmitted to the user interaction module to notify relevant personnel. According to the alarm level, the preset emergency response measures are automatically triggered, and relevant equipment is controlled through the interface, including automatically cutting off the gas supply and starting emergency exhaust.

[0097] Multiple alarm levels correspond to multiple alarm thresholds, where the alarm threshold includes an upper threshold and a lower threshold;

[0098] Multiple alarm levels and multiple alarm thresholds satisfy the following relationship:

[0099] Warning alarm level 0 <R≤R T1 ; Trigger a yellow alarm, corresponding to a low-level warning, start enhanced monitoring mode, record a log, and notify technicians;

[0100] Severe alarm level R T1 <R≤R T2 ; Triggering an orange alert, corresponding to a medium-level warning, initiates some safety measures, such as reducing gas flow and activating backup ventilation systems;

[0101] Emergency alarm level R>R T2 ; Triggering a red alarm, corresponding to a high-level warning, immediately cuts off the gas supply and initiates full emergency procedures, such as evacuating personnel and notifying emergency services;

[0102] Among them, R is the abnormal state warning coefficient, R T1 is the lower threshold corresponding to the severe alarm level and the upper threshold corresponding to the warning alarm level, R T2 It is the lower threshold corresponding to the emergency alarm level and the upper threshold corresponding to the serious alarm level.

[0103] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A gas safety monitoring system based on the Internet of Things, including a monitoring and alarm platform, is characterized by: The monitoring and alarm platform is communicatively connected with a sensor module, a communication module, an abnormality analysis module, an alarm and response module, and a user interaction module, wherein electrical signals are connected between the modules; The sensor module is used to collect key parameters in the gas environment, including gas concentration, temperature, and pressure; The communication module is used to transmit the data collected by the sensor module to the abnormality analysis module through a wireless network; The anomaly analysis module is used to mine and analyze the received data, obtain features related to gas safety, and apply machine learning to perform anomaly analysis on the data to identify and classify anomaly categories in the data. The gas safety-related features include gas concentration features, environmental parameter features, and equipment status features; The abnormality analysis module includes a feature extraction unit, a data collection unit and an abnormality analysis unit; wherein the feature extraction unit is used to extract abnormal state features from the data collected by the sensor module; the data collection unit is used to classify the abnormal state feature data obtained by the feature extraction unit to obtain a data sequence table; the abnormality analysis unit is used to combine the data sequence table and the data collected by the sensor module to obtain an abnormal state warning coefficient, and perform a safety analysis of the gas system according to the abnormal state warning coefficient; The alarm and response module is used to receive the abnormal analysis results of the abnormal analysis module, process the abnormal analysis results, classify the alarm levels, and trigger corresponding alarm signals according to the alarm levels to control relevant equipment for emergency processing; The user interaction module is used to provide users with a visual display of system status.

2. The gas safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: In the sensor module, the process of collecting key parameters in the gas environment is as follows: Based on various types of sensors deployed in the gas use environment, the gas concentration, ambient temperature, and pipeline pressure are monitored and converted into electrical signals; Perform pre-processing operations such as filtering and amplification on the raw data collected by the sensor; The processed data is transmitted to the communication module through the communication interface inside the sensor module.

3. The gas safety monitoring system based on the Internet of Things according to claim 2 is characterized in that: In the communication module, the process of transmitting data is as follows: The communication module receives the pre-processed data from the sensor module and monitors the signal strength, bandwidth, and delay status of the current wireless network; Design multiple communication links, divided into main communication links and backup links, and equip backup servers. When problems occur in the main communication link or main server, they can be switched to the backup link or backup server; Encapsulate and encrypt the received pre-processed data, and send the encapsulated data to the abnormality analysis module via a wireless network; After sending the data, obtain feedback and confirmation information from the exception analysis module to confirm whether the data is sent successfully. If the data fails to be sent, retransmit it until the data is successfully delivered or the retransmission limit is reached. If the data fails to be retransmitted multiple times, record the error information and notify the system administrator.

4. The gas safety monitoring system based on the Internet of Things according to claim 3 is characterized in that: The process of extracting abnormal state features and obtaining a data sequence table is as follows: Receive the encapsulated data sent by the communication module and parse the data packet to obtain the original monitoring value; Clean the extracted data, remove invalid or erroneous data points, and remove noise, missing values, or outliers; Features related to gas safety are extracted from the original data, namely, gas concentration features, environmental parameter features, and equipment status features. Among them, gas concentration features include gas concentration value, concentration change trend, and number of concentration abnormality threshold breakthroughs; environmental parameter features include temperature, humidity, and pressure; equipment status features include sensor working status, detection accuracy, and fault records; Analyze the extracted gas safety-related feature data and distinguish different abnormal status categories; The classified abnormal status data are sorted in order of severity, and based on the sorted data, a data sequence table is constructed, which includes the occurrence time, location, abnormality type, and impact scope of the abnormal status.

5. The gas safety monitoring system based on the Internet of Things according to claim 4 is characterized in that: In the abnormality analysis unit, the process of performing safety analysis of the gas system according to the abnormal state warning coefficient is as follows: Integrate the abnormal state data in the data sequence table and the data collected by the sensor module, and perform feature matching between the data collected by the sensor module and the abnormal type in the data sequence table to identify the abnormal state; Based on the extracted abnormal state data and the data collected by the sensor module, an abnormal state assessment model is constructed. The identified abnormal data is further classified and processed to distinguish different types of abnormalities, determine the severity of the abnormalities, analyze the changing trend of gas safety conditions, and predict potential risks. Using the trained abnormal state assessment model, the data collected by the sensor module is processed and analyzed, the abnormal state warning coefficient is calculated, and the similarity between the current state and the abnormal state is analyzed; Based on the abnormal state warning coefficient, the safety risk of the gas system is evaluated and the specific abnormal state existing in the current gas system is identified.

6. The gas safety monitoring system based on the Internet of Things according to claim 5 is characterized in that: The process of obtaining the abnormal state warning coefficient is as follows: Collect gas concentration data at each time point in the monitoring period, determine the maximum and minimum gas concentrations in the monitoring period, and calculate the change in gas concentration at each time point. Based on the acquired data, calculate the sum of squares of the deviations between the gas concentration and the average value, take the square root, analyze the average deviation, and then calculate the sum of squares of the change in gas concentration, analyze the change trend, and combine the average deviation and the change trend to obtain the gas concentration anomaly index; Collect the ambient temperature, humidity, and pressure data at each time point during the monitoring period, determine the standard values ​​of temperature, humidity, and pressure, calculate the comprehensive deviation of the three environmental parameters, square the comprehensive deviation, calculate the sum, and divide the sum of squares of the deviations by the total number of time points during the monitoring period to obtain the environmental parameter anomaly index; Collect sensor working status, detection accuracy and equipment failure record data at each time point in the monitoring period, calculate the proportion of faulty sensors, and calculate the inverse of the detection accuracy at each time point, find the average, calculate the sum of equipment failure records, and divide it by the total number of time points in the monitoring period, combine the sensor failure rate, the inverse of the detection accuracy and the equipment failure rate to obtain the equipment status abnormality index; According to the calculated gas concentration abnormality index, environmental parameter abnormality index, and equipment status abnormality index, corresponding weights are set, each abnormality index is multiplied by its weight, and then summed to obtain the abnormal status warning coefficient.

7. The gas safety monitoring system based on the Internet of Things according to claim 6 is characterized in that: The calculation formula of the gas concentration abnormality index is: Among them, A C is the gas concentration abnormality index, C t is the gas concentration at time t, C max is the maximum value of gas concentration during the monitoring period, C min is the minimum value of gas concentration during the monitoring period, ΔC t is the change in gas concentration at time t, ΔC t =C t -C t-1 ,∈ is a small constant to prevent the denominator from being zero, and n is the total number of time points in the monitoring period; The calculation formula of the environmental parameter abnormality index is: Among them, A E is the environmental parameter anomaly index, T t is the ambient temperature at time t, H t is the ambient humidity at time t, P t is the ambient pressure at time t, T th , H th , P th Respectively represent the standard values ​​of temperature, humidity and pressure; The calculation formula of the device status abnormality index is: Among them, A D is the device status abnormality index, S t is the working state of the sensor at time point t, 0 indicates failure, 1 indicates normal, A cct is the sensor detection accuracy at time point t, F t is the equipment failure record at time point t.

8. The gas safety monitoring system based on the Internet of Things according to claim 7 is characterized in that: The calculation formula of the abnormal state warning coefficient is: R=w1·A C +w2·A E +w3·A DD ; Among them, R is the abnormal state warning coefficient, A C is the gas concentration abnormality index, A E is the environmental parameter anomaly index, A D is the equipment status abnormality index, w1 is the weight of the gas concentration abnormality index, w2 is the weight of the environmental parameter abnormality index, w3 is the weight of the equipment status abnormality index, w1+w2+w3=1.

9. The gas safety monitoring system based on the Internet of Things according to claim 8 is characterized in that: In the alarm and response module, the emergency handling process is as follows: Receive abnormal analysis data about gas concentration, environmental parameters, and equipment status from the abnormal analysis module, and format and parse the received data; According to the preset risk assessment standards, abnormal situations are divided into different alarm levels, namely warning alarm level, serious alarm level, and emergency alarm level; Set a corresponding alarm threshold for each alarm level. When the alarm threshold is exceeded, the corresponding level of alarm will be triggered; Analyze the abnormal analysis results of the abnormal analysis module, combine the value of the abnormal state warning coefficient, determine the specific alarm level of the abnormal situation, and generate the corresponding alarm signal; The alarm signal is transmitted to the user interaction module to notify relevant personnel, and according to the alarm level, the preset emergency response measures are automatically triggered, and relevant equipment is controlled through the interface, including automatically cutting off the gas supply and starting emergency exhaust.

10. The gas safety monitoring system based on the Internet of Things according to claim 9 is characterized in that: The plurality of alarm levels correspond to the plurality of alarm thresholds, wherein the alarm thresholds include an upper threshold and a lower threshold; The multiple alarm levels and the multiple alarm thresholds satisfy the following relationship: Warning alarm level 0 <R≤R T1 ; Severe alarm level R T1 <R≤R T2 ; Emergency alarm level R>R T2 ; Among them, R is the abnormal state warning coefficient, R T1 is the lower threshold corresponding to the severe alarm level and the upper threshold corresponding to the warning alarm level, R T2 It is the lower threshold corresponding to the emergency alarm level and the upper threshold corresponding to the serious alarm level.

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