Method and system for monitoring flammable gas in sewage pipeline based on internet of things

Abstract

The present application provides a sewage pipeline flammable gas monitoring method and system based on the Internet of Things, monitoring points are arranged in the monitoring area, gas concentration data of each monitoring point is collected, the gas concentration data of each monitoring point is extracted and brought into a gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitoring points, the obtained gas concentration values are brought into a safety factor calculation formula to calculate the corresponding safety factors, the safety factors of each sub-section are compared with the set safety factor threshold value in terms of risk, if the obtained safety factor is less than or equal to the set safety factor threshold value, instructions are sent to the maintenance personnel, and the high-risk section is maintained and warned, if the obtained safety factor is greater than the set safety factor threshold value, no instructions are sent to the maintenance personnel, the state of the flammable gas in the sewage pipeline can be monitored in real time, and the timeliness and accuracy of the flammable gas monitoring are improved.

Description

Technical Field

[0001] This invention belongs to the field of municipal engineering facilities technology, specifically relating to a method and system for monitoring combustible gases in sewage pipelines based on the Internet of Things. Background Technology

[0002] As sewage flows through pipes, dissolved oxygen is gradually consumed, creating an anaerobic environment within the pipes. Under anaerobic conditions, organic matter is utilized by anaerobic microorganisms, producing flammable gases such as methane (CH4), hydrogen sulfide (H2S), and carbon monoxide (CO). The relatively enclosed space within sewage pipes lacks pathways for gas exchange with the outside environment. These flammable gases cannot be promptly discharged and accumulate in the overhead space, easily exceeding safe concentrations and posing a significant threat to the safe operation and management of the sewage network.

[0003] Regarding the monitoring of flammable gases in sewage pipes, a patent authorized by the public account CN216900463U discloses a sewage pipe gas detection device. When workers test the gas inside the pipe, the device can be moved above the pipe opening, eliminating the need for workers to enter the pipe for testing. The operation is simple and practical. However, this device cannot provide real-time monitoring and early warning of flammable gases in the pipe. Moreover, in order to improve the accuracy of data collection, a large number of these devices need to be added, resulting in a sharp increase in costs.

[0004] Therefore, this invention proposes a method and system for monitoring combustible gases in sewage pipelines based on the Internet of Things (IoT) to solve the above-mentioned problems. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the present invention provides a method and system for monitoring combustible gases in sewage pipelines based on the Internet of Things, which can effectively solve the above problems.

[0006] The technical solution adopted in this invention is as follows:

[0007] This invention provides a method for monitoring flammable gases in sewage pipelines based on the Internet of Things, comprising the following steps:

[0008] Step S1: Divide the sewage pipe to be monitored into several sub-regions with the same pipe diameter and slope; within each sub-region, set monitoring points at equal intervals L along the sewage flow direction within the sewage pipe; evenly divide each pair of adjacent monitoring points into n segments, each segment having a length of L. j =L / n; where j = 1,2,…,n; a gas monitoring instrument is set up at each monitoring point to collect the gas concentration data of the corresponding monitoring point;

[0009] Step S2: Extract the gas concentration data of each monitoring point, substitute it into the gas concentration distribution prediction formula, and predict the gas concentration values ​​of each sub-segment between monitoring points.

[0010] Step S3: Substitute the obtained gas concentration values ​​of each sub-segment into the safety factor calculation formula to calculate the safety factor of each sub-segment;

[0011] Step S4: Compare the safety factor of each sub-segment with the set safety factor threshold. If the safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to provide a maintenance warning for the high-risk segment; if the safety factor is greater than the set safety factor threshold, do not send an instruction to the maintenance personnel.

[0012] Preferably, step S1 includes the following specific steps:

[0013] Step S11: Divide the sewage pipes to be monitored into several sub-areas according to the same pipe diameter and slope; the sewage pipes in each sub-area have the same pipe diameter and slope;

[0014] Step S12: In each sub-region, monitoring points are set at equal intervals L along the direction of sewage flow within the sewage pipe;

[0015] Step S13: Divide the sewage pipe into n segments evenly between every two adjacent monitoring points, with each segment having a length of L. j = L / n, where j = 1, 2, ..., n;

[0016] Step S14: Install a gas monitoring instrument at each monitoring point to monitor the concentration data of combustible gases at the monitoring point; wherein, combustible gases include methane (CH4), hydrogen sulfide (H2S), and carbon monoxide (CO).

[0017] Preferably, step S2 includes the following specific contents:

[0018] If monitoring point a and monitoring point b are located in the same sub-region, then the sewage pipes between monitoring point a and monitoring point b have the same diameter and slope.

[0019] The formula for predicting the gas concentration distribution in segment j between monitoring point a and monitoring point b is:

[0020]

[0021] Where: C iLj C represents the concentration of the i-th gas in segment j between monitoring points a and b. ia C represents the concentration of the i-th gas at monitoring point a. ib L represents the concentration of the i-th gas at monitoring point b. mL represents the distance between monitoring point a and monitoring point b. m = m·L, where m represents the number of intervals L between monitoring points a and b; L x L represents the distance from the center point of segment j between monitoring point a and monitoring point b to monitoring point a. x <L m D represents the gas diffusion coefficient, and E represents the pipeline hydraulic coefficient. Both D and E are constants related to the diameter and slope of the sewage pipeline. Within the same sub-region, D and E remain unchanged.

[0022] Preferably, in step S3, the formula for calculating the safety factor is:

[0023]

[0024] in:

[0025] M represents the gas safety factor for segment j, C i The concentration of the i-th gas in sub-segment j is obtained directly from monitoring in step S1 or predicted in step S2.

[0026] C imax C represents the highest explosive limit concentration of the i-th gas. imin C represents the minimum explosive limit concentration of the i-th gas. imid This represents the median of the explosive limits of the i-th gas. f i This represents the safety percentage coefficient for the i-th gas. N represents the number of different types of gases.

[0027] This invention provides an IoT-based system for monitoring flammable gases in sewage pipelines. The IoT-based method for monitoring flammable gases in sewage pipelines specifically includes: a data acquisition module, a gas concentration prediction and analysis module, a safety factor calculation module, a safety factor comparison module, and a management module.

[0028] The data acquisition module is a gas monitoring instrument set at each monitoring point in the area to be monitored, which collects gas concentration data at each monitoring point.

[0029] The gas concentration prediction and analysis module is used to input the extracted gas concentration data from each monitoring point into the gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between monitoring points.

[0030] The safety factor calculation module is used to input the obtained gas concentration values ​​of each sub-segment into the safety factor calculation formula to calculate the safety factor of each sub-segment.

[0031] The safety factor comparison module is used to compare the safety factor of each sub-segment with the set safety factor threshold. If the safety factor is less than or equal to the set safety factor threshold, an instruction is sent to the maintenance personnel to provide a maintenance warning for the high-risk segment; if the safety factor is greater than the set safety factor threshold, no instruction is sent to the maintenance personnel.

[0032] The management module is used to manage the operation of the data acquisition module, the gas concentration prediction and analysis module, the safety factor calculation module, and the safety factor comparison module.

[0033] The present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes an Internet of Things-based method for monitoring combustible gases in sewage pipes by calling the computer program stored in the memory.

[0034] The present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform an Internet of Things-based method for monitoring combustible gases in sewage pipes.

[0035] The IoT-based method and system for monitoring combustible gases in sewage pipelines provided by this invention have the following advantages:

[0036] 1. A method and system for monitoring combustible gases in sewage pipelines based on the Internet of Things (IoT) involves setting up monitoring points within a monitoring area, collecting gas concentration data from each monitoring point, extracting the gas concentration data from each monitoring point and inputting it into a gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between monitoring points, inputting the obtained gas concentrations into a safety factor calculation formula to calculate the corresponding safety factor, comparing the safety factor of each sub-segment with a set safety factor threshold for risk assessment, and sending an instruction to maintenance personnel to provide a maintenance warning for high-risk sections if the obtained safety factor is less than or equal to the set safety factor threshold, and not sending an instruction to maintenance personnel if the obtained safety factor is greater than the set safety factor threshold. This method can monitor the state of combustible gases in sewage pipelines in real time, improving the timeliness and accuracy of combustible gas monitoring.

[0037] 2. By setting up monitoring points at certain intervals within the monitoring area to collect gas concentration data, the gas concentration data of the remaining segments can be predicted through the gas concentration prediction and analysis module, eliminating the need to set up a large number of monitoring points throughout the entire segment and reducing monitoring costs.

[0038] 3. When predicting the gas concentration data of other sub-segments through the gas concentration prediction and analysis module, the gas concentration and safety factor data at the center point of a certain sub-segment are used to represent the gas concentration and safety factor data of that sub-segment, which greatly reduces the amount of data processing and improves the processing speed. Attached Figure Description

[0039] Figure 1 A schematic flowchart of the Internet of Things-based method for monitoring combustible gases in sewage pipelines provided by the present invention;

[0040] Figure 2 A schematic diagram of the framework of the Internet of Things-based combustible gas monitoring system for sewage pipelines provided by the present invention;

[0041] Figure 3 This is a structural diagram of the electronic device provided by the present invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," "top / bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0044] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "sleeved / connected," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0045] Example 1

[0046] Please see Figure 1 The present invention provides an embodiment of a method for monitoring flammable gases in sewage pipelines based on the Internet of Things, comprising the following steps:

[0047] Step S1: Divide the sewage pipe to be monitored into several sub-regions with the same pipe diameter and slope; within each sub-region, set monitoring points at equal intervals L along the sewage flow direction within the sewage pipe; evenly divide each pair of adjacent monitoring points into n segments, each segment having a length of L. j =L / n; where j = 1, 2, ..., n; gas monitoring instruments are set up at each monitoring point to collect gas concentration data at the corresponding monitoring point.

[0048] Step S2: Extract the gas concentration data from each monitoring point, input it into the gas concentration distribution prediction formula, and predict the gas concentration values ​​of each sub-segment between monitoring points.

[0049] Step S3: Substitute the obtained gas concentration values ​​of each sub-segment into the safety factor calculation formula to calculate the safety factor of each sub-segment.

[0050] Step S4: Compare the safety factor of each sub-segment with the set safety factor threshold. If the safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to provide a maintenance warning for the high-risk segment; if the safety factor is greater than the set safety factor threshold, do not send an instruction to the maintenance personnel.

[0051] The method for monitoring combustible gases in sewage pipelines based on the Internet of Things includes the following specific steps in step S1:

[0052] Step S11: Divide the sewage pipes to be monitored into several sub-areas according to the same pipe diameter and slope; the sewage pipes in each sub-area have the same pipe diameter and slope;

[0053] Step S12: In each sub-region, monitoring points are set at equal intervals L along the direction of sewage flow within the sewage pipe;

[0054] Step S13: Divide the sewage pipe into n segments evenly between every two adjacent monitoring points, with each segment having a length of L. j = L / n, where j = 1, 2, ..., n;

[0055] Step S14: Install gas monitoring instruments at each monitoring point to monitor the concentration data of combustible gases at the monitoring point; wherein, combustible gases include methane (CH4), hydrogen sulfide (H2S), carbon monoxide (CO), and other gases.

[0056] The method for monitoring combustible gases in sewage pipelines based on the Internet of Things, wherein step S2 includes the following specific contents:

[0057] Gas concentration data from each monitoring point are extracted and input into the gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between monitoring points.

[0058] If monitoring point a and monitoring point b are located in the same sub-region, then the sewage pipes between monitoring point a and monitoring point b have the same diameter and slope.

[0059] The formula for predicting the gas concentration distribution in segment j between monitoring point a and monitoring point b is:

[0060]

[0061] Where: C iLj C represents the concentration of the i-th gas in segment j between monitoring points a and b. ia C represents the concentration of the i-th gas at monitoring point a. ib L represents the concentration of the i-th gas at monitoring point b. m L represents the distance between monitoring point a and monitoring point b. m = m·L, where m represents the number of intervals L between monitoring points a and b; L x L represents the distance from the center point of segment j between monitoring point a and monitoring point b to monitoring point a. x <L m D represents the gas diffusion coefficient, and E represents the pipeline hydraulic coefficient. Both D and E are constants related to the diameter and slope of the sewage pipeline, and remain constant within the same sub-region. The gas concentrations between other monitoring points can also be derived by analogy from the formula.

[0062] The method for monitoring combustible gases in sewage pipelines based on the Internet of Things, wherein step S3 includes the following specific contents:

[0063] Substitute the obtained gas concentration into the safety factor calculation formula to calculate the corresponding safety factor.

[0064] The formula for calculating the safety factor is as follows:

[0065]

[0066] in:

[0067] M represents the gas safety factor for segment j, C i The concentration of the i-th gas in sub-segment j is obtained directly from monitoring in step S1 or predicted in step S2.

[0068] C imax C represents the highest explosive limit concentration of the i-th gas. imin C represents the minimum explosive limit concentration of the i-th gas. imid This represents the median of the explosive limits of the i-th gas. f i This represents the safety percentage coefficient for the i-th gas. N represents the number of different types of gas. The safety factors for the remaining sections can also be derived by analogy from the formula.

[0069] The method for monitoring combustible gases in sewage pipelines based on the Internet of Things includes the following specific steps in step S4:

[0070] Step S41: Compare the safety factor of each sub-segment with the set safety factor threshold to determine the magnitude of the safety factor of each sub-segment compared with the set safety factor threshold.

[0071] Step S42: If the obtained safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to provide a maintenance warning for the high-risk section; if the obtained safety factor is greater than the set safety factor threshold, do not send an instruction to the maintenance personnel.

[0072] This embodiment enables the following: monitoring points are set up within the monitoring area, gas concentration data is collected from each monitoring point, the gas concentration data from each monitoring point is extracted and input into a gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between the monitoring points, the obtained gas concentrations are input into a safety factor calculation formula to calculate the corresponding safety factor, and the safety factor of each sub-segment is compared with a set safety factor threshold. If the obtained safety factor is less than or equal to the set safety factor threshold, an instruction is sent to the maintenance personnel to provide a maintenance warning for the high-risk section. If the obtained safety factor is greater than the set safety factor threshold, no instruction is sent to the maintenance personnel. This allows for real-time monitoring of the state of combustible gases in sewage pipes, improving the timeliness and accuracy of combustible gas monitoring.

[0073] Example 2

[0074] Please see Figure 2 The IoT-based flammable gas monitoring system for sewage pipelines specifically includes: a data acquisition module, a gas concentration prediction and analysis module, a safety factor calculation module, a safety factor comparison module, and a management module.

[0075] The data acquisition module is a gas monitoring instrument set at each monitoring point in the area to be monitored, which collects gas concentration data at each monitoring point.

[0076] The gas concentration prediction and analysis module is used to input the extracted gas concentration data from each monitoring point into the gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between monitoring points.

[0077] The safety factor calculation module is used to input the obtained gas concentration values ​​of each sub-segment into the safety factor calculation formula to calculate the safety factor of each sub-segment.

[0078] The safety factor comparison module is used to compare the safety factor of each sub-segment with the set safety factor threshold. If the safety factor is less than or equal to the set safety factor threshold, an instruction is sent to the maintenance personnel to provide a maintenance warning for the high-risk segment; if the safety factor is greater than the set safety factor threshold, no instruction is sent to the maintenance personnel.

[0079] The management module is used to manage the operation of the data acquisition module, the gas concentration prediction and analysis module, the safety factor calculation module, and the safety factor comparison module.

[0080] Example 3

[0081] Please see Figure 3 This embodiment provides an electronic device, including a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the above-described IoT-based method for monitoring combustible gases in sewage pipes by calling the computer program stored in the memory.

[0082] The processor can be a central processing unit, or it can be other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0083] Memory can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM can store static data or instructions required by the processor or other modules of the computer. Permanent storage devices can be read-write devices, and can be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. System memory can be a read-write device or a volatile read-write device, such as dynamic random access memory. System memory can store some or all of the instructions and data required by the processor during operation. Furthermore, memory can include any combination of computer-readable storage media, including various types of semiconductor memory chips; hard disks or optical discs can also be used.

[0084] The readable storage medium stores one or more computer programs that, when run on a computer, cause the computer to perform the aforementioned Internet of Things-based method for monitoring combustible gases in sewage pipes.

[0085] The IoT-based method and system for monitoring combustible gases in sewage pipelines provided by this invention have the following advantages:

[0086] 1. A method and system for monitoring combustible gases in sewage pipelines based on the Internet of Things (IoT) involves setting up monitoring points within a monitoring area, collecting gas concentration data from each monitoring point, extracting the gas concentration data from each monitoring point and inputting it into a gas concentration distribution prediction formula to predict the gas concentration values ​​of each sub-segment between monitoring points, inputting the obtained gas concentrations into a safety factor calculation formula to calculate the corresponding safety factor, comparing the safety factor of each sub-segment with a set safety factor threshold for risk assessment, and sending an instruction to maintenance personnel to provide a maintenance warning for high-risk sections if the obtained safety factor is less than or equal to the set safety factor threshold, and not sending an instruction to maintenance personnel if the obtained safety factor is greater than the set safety factor threshold. This method can monitor the state of combustible gases in sewage pipelines in real time, improving the timeliness and accuracy of combustible gas monitoring.

[0087] 2. By setting up monitoring points at certain intervals within the monitoring area to collect gas concentration data, the gas concentration data of the remaining segments can be predicted through the gas concentration prediction and analysis module, eliminating the need to set up a large number of monitoring points throughout the entire segment and reducing monitoring costs.

[0088] 3. When predicting the gas concentration data of other sub-segments through the gas concentration prediction and analysis module, the gas concentration and safety factor data at the center point of a certain sub-segment are used to represent the gas concentration and safety factor data of that sub-segment, which greatly reduces the amount of data processing and improves the processing speed.

[0089] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring flammable gas in a sewage pipeline based on the Internet of Things, characterized in that, The method comprises the following steps: Step S1: the sewage pipeline to be monitored is divided into several sub-regions according to the same pipe diameter and the same slope; monitoring points are arranged in the sewage pipeline at the same distance L along the sewage flow direction in each sub-region; each two adjacent monitoring points are evenly divided into n sub-sections, and the length of each sub-section is L j =L / n; wherein, j=1, 2, …, n; a gas monitoring instrument is arranged at each monitoring point to collect gas concentration data of the corresponding monitoring point; Step S2: Extracting the gas concentration data of each monitoring point, and bringing the data into a gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitoring points; Step S3: Bringing the obtained gas concentration values of each sub-section into a safety factor calculation formula to calculate the safety factors of each sub-section; Step S4: Comparing the safety factors of each sub-section with a set safety factor threshold value, and if the safety factor is less than or equal to the set safety factor threshold value, sending an instruction to the maintenance personnel to perform maintenance warning on the high-risk section; if the safety factor is greater than the set safety factor threshold value, not sending an instruction to the maintenance personnel; The step S2 comprises the following specific contents: Supposing that the monitoring point a and the monitoring point b are located in the same sub-region, the sewage pipeline between the monitoring point a and the monitoring point b has the same pipe diameter and the same slope; The gas concentration distribution prediction formula of the sub-section j between the monitoring point a and the monitoring point b is: ； wherein: represents the concentration of the i-th gas between the monitoring point a and the monitoring point b in the sub-section j, represents the concentration of the i-th gas at the monitoring point a, represents the concentration of the i-th gas at the monitoring point b, represents the distance between the monitoring point a and the monitoring point b, , represents the number of intervals with the distance L between the monitoring point a and the monitoring point b; represents the distance between the center point of the sub-section j and the monitoring point a between the monitoring point a and the monitoring point b, ; D represents a gas diffusion coefficient, E represents a pipe hydraulic coefficient, and D and E are both constants related to the diameter of the sewage pipe and the slope of the sewage pipe, and D and E remain unchanged in the same sub-region; In the step S3, the safety factor calculation formula is: ； Wherein: M represents the gas safety factor of the sub-section j, Ci,j represents the concentration of the i-th gas of the sub-section j, which is directly monitored in step S1 or predicted by step S2; Xi represents the highest explosive limit concentration of the i-th gas, Xi represents the lowest explosive limit of the i-th gas concentration, a median value representing an explosion limit range of the i-th gas, , a safety occupancy coefficient representing the i-th gas, , N represents the number of gas types.

2. The Internet of Things based sewer pipe flammable gas monitoring method according to claim 1, characterized in that, The step S1 comprises the following specific steps: Step S11: Dividing the sewage pipeline to be monitored into a plurality of sub-regions according to the same pipe diameter and the same slope; the sewage pipelines in each sub-region have the same pipe diameter and the same slope; Step S12: Setting monitoring points in the sewage pipeline at intervals of the same distance L along the sewage flow direction in each sub-region; Step S13: evenly divide the sewage pipeline between every two adjacent monitoring points into n sub-sections, and the length of each sub-section of the sewage pipeline is L j = L / n, where j = 1, 2, …, n; Step S14: Setting a gas monitoring instrument at each monitoring point to monitor the combustible gas concentration data of the monitoring point; wherein the combustible gas includes methane CH4, hydrogen sulfide H2S and carbon monoxide CO.

3. The system for monitoring flammable gas in sewage pipeline based on Internet of Things, characterized in that, The sewage pipeline combustible gas monitoring method based on the Internet of Things according to any one of claims 1-2, characterized in that it specifically comprises: a data acquisition module, a gas concentration prediction analysis module, a safety factor calculation module, a safety factor comparison module and a management module; The data acquisition module is a gas monitoring instrument set at each monitoring point in the region to be monitored, which acquires the gas concentration data of each monitoring point; The gas concentration prediction analysis module is configured to bring the extracted gas concentration data of each monitoring point into a gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitoring points; The safety factor calculation module is configured to bring the obtained gas concentration values of each sub-section into a safety factor calculation formula to calculate the safety factors of each sub-section; The safety factor comparison module is configured to compare the safety factors of each sub-section with a set safety factor threshold value, and if the safety factor is less than or equal to the set safety factor threshold value, send an instruction to the maintenance personnel to perform maintenance warning on the high-risk section; if the safety factor is greater than the set safety factor threshold value, not send an instruction to the maintenance personnel; The management module is configured to manage the operation of the data acquisition module, the gas concentration prediction analysis module, the safety factor calculation module and the safety factor comparison module.

4. An electronic device, comprising: It comprises: A processor and a memory, wherein the memory has stored a computer program invokable by the processor; the processor, by invoking the computer program stored in the memory, executes the method for monitoring combustible gas in sewage pipeline based on Internet of Things according to any one of claims 1-2.

5. A computer readable storage medium, characterized in that, A computer program product having stored instructions which, when executed on a computer, cause the computer to perform the method for monitoring combustible gas in sewage pipeline based on Internet of Things according to any one of claims 1-2.

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