Method and system for monitoring combustible gas in sewage pipeline based on Internet of Things

Abstract

The invention provides a sewage pipeline combustible gas monitoring method and system based on the Internet of Things, and the method comprises the steps: setting monitoring points in a monitoring region, collecting the gas concentration data of each monitoring point, extracting the gas concentration data of each monitoring point, substituting the gas concentration data into a gas concentration distribution prediction formula, and predicting the gas concentration value of each sub-segment between the monitoring points; substituting the obtained gas concentration value into a safety coefficient calculation formula, calculating a corresponding safety coefficient, carrying out risk comparison on the safety coefficient of each sub-section and a set safety coefficient threshold value, and if the obtained safety coefficient is smaller than or equal to the set safety coefficient threshold value, sending an instruction to a maintainer, carrying out maintenance early warning on a high-risk section, and if the obtained safety coefficient is not greater than the set safety coefficient threshold value, carrying out maintenance early warning on the high-risk section. And if the obtained safety coefficient is greater than the set safety coefficient threshold, an instruction is not sent to the maintenance personnel, so that the state of the combustible gas in the sewage pipeline can be monitored in real time, and the timeliness and accuracy of combustible gas monitoring are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of municipal engineering facilities, and specifically relates to a method and system for monitoring combustible gases in sewage pipes based on the Internet of Things. Background Art

[0002] During the process of sewage flowing in sewage pipes, the dissolved oxygen in the sewage is gradually consumed, and the pipes are often in an anaerobic environment. Under anaerobic conditions, organic matter is utilized by anaerobic microorganisms to produce combustible gases such as methane (CH4), hydrogen sulfide (H2S), and carbon monoxide (CO). The internal space of the sewage pipe is relatively closed, lacking a way to exchange gases with the outside world. The generated combustible gases cannot be discharged in time and accumulate continuously in the top space, and the concentration of combustible gases is easily in an over-standard state, posing a great threat to the safe operation and management of the sewage pipe network.

[0003] Regarding how to monitor combustible gases in sewage pipes, the patent with the authorized public number CN216900463U discloses a sewage pipe gas detection device. When staff detect the gas inside the pipe, the device can be moved above the pipe opening, and there is no need for staff to enter the pipe for detection, which is simple and practical. However, this device cannot monitor and give early warnings to combustible gases in the pipe in real time, and in order to improve the accuracy of data collection, a large number of such devices need to be added, resulting in a sharp increase in costs.

[0004] Therefore, the present invention proposes a method and system for monitoring combustible gases in sewage pipes based on the Internet of Things to solve the above problems. Summary of the Invention

[0005] Aiming at the defects existing in the prior art, the present invention provides a method and system for monitoring combustible gases in sewage pipes based on the Internet of Things, which can effectively solve the above problems.

[0006] The technical solution adopted by the present invention is as follows:

[0007] The present invention provides a method for monitoring combustible gases in sewage pipes based on the Internet of Things, including the following steps:

[0008] Step S1: Divide the sewage pipes to be monitored into several sub-regions according to the same pipe diameter and the same slope; set monitoring points at the same distance L along the sewage flow direction in each sub-region in the sewage pipe; evenly divide each two adjacent monitoring points into n sub-segments, and the length of each sub-segment is L j = L / n; where j = 1, 2,..., n; set gas monitoring instruments at each monitoring point to collect gas concentration data of the corresponding monitoring points;

[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 the 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 factors of each sub-segment with the set safety factor threshold for risk. If the safety factor is less than or equal to the set safety factor threshold, send a command to the maintenance personnel to give a maintenance warning for the high-risk segment; if the safety factor is greater than the set safety factor threshold, do not send a command to the maintenance personnel.

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

[0013] Step S11: Divide the sewage pipelines to be monitored into several sub-regions according to the same pipe diameter and the same slope; the sewage pipelines within each sub-region have the same pipe diameter and the same slope;

[0014] Step S12: Along the sewage flow direction within each sub-region, set monitoring points at the same distance L intervals in the sewage pipeline;

[0015] Step S13: Evenly divide the sewage pipeline into n sub-segments between every two adjacent monitoring points, and the length of the sewage pipeline of each sub-segment is L j = L / n, where j = 1, 2, …, n;

[0016] Step S14: Set gas monitoring instruments at each monitoring point to monitor the combustible gas concentration data of the said monitoring point; among them, the combustible gases include methane CH4, hydrogen sulfide H2S, and carbon monoxide CO.

[0017] Preferably, the said Step S2 includes the following specific content:

[0018] Suppose monitoring point a and monitoring point b are within the same sub-region, then the sewage pipelines between monitoring point a and monitoring point b have the same pipe diameter and the same slope;

[0019] The gas concentration distribution prediction formula for sub-segment j between monitoring point a and monitoring point b is:

[0020]

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

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

[0023]

[0024] Where:

[0025] M represents the gas safety factor of sub-segment j, C i Represents the concentration of the i-th gas in sub-segment j, which is directly monitored in step S1 or predicted by step S2;

[0026] C imax Represents the highest explosion limit concentration of the i-th gas, C imin Represents the lowest explosion limit concentration of the i-th gas, C imid Represents the median of the explosion limit range of the i-th gas, f i Represents the safety ratio coefficient of the i-th gas, N represents the number of gas types.

[0027] The present invention provides an Internet of Things-based sewage pipeline flammable gas monitoring system, which, based on the Internet of Things-based sewage pipeline flammable gas monitoring method, 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, and collects the gas concentration data of each monitoring point;

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

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

[0031] The safety factor comparison module is used to compare the safety factors of each sub - segment with a set safety factor threshold for risk comparison. If the safety factor is less than or equal to the set safety factor threshold, an instruction is sent to the maintenance personnel to give 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, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory; the processor executes a method for monitoring combustible gas in a sewage pipeline based on the Internet of Things by calling the computer program stored in the memory.

[0034] The present invention provides a computer - readable storage medium storing instructions, which when run on a computer, cause the computer to execute a method for monitoring combustible gas in a sewage pipeline based on the Internet of Things.

[0035] The method and system for monitoring combustible gas in a sewage pipeline based on the Internet of Things provided by the present invention have the following advantages:

[0036] 1. A method and system for monitoring combustible gas in a sewage pipeline based on the Internet of Things set monitoring points in the monitoring area, collect gas concentration data of each monitoring point, extract the gas concentration data of each monitoring point and substitute it into the gas concentration distribution prediction formula to predict the gas concentration values of each sub - segment between the monitoring points, substitute the obtained gas concentration into the safety factor calculation formula to calculate the corresponding safety factor, compare the safety factors of each sub - segment with a set safety factor threshold for risk comparison. 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 give a maintenance warning for the high - risk segment. If the obtained safety factor is greater than the set safety factor threshold, no instruction is sent to the maintenance personnel. It can monitor the state of combustible gas in the sewage pipeline in real - time, improving the timeliness and accuracy of combustible gas monitoring;

[0037] 2. Monitoring points are set at a certain distance in the monitoring area to collect gas concentration data, and then the gas concentration prediction and analysis module can predict the gas concentration data of the remaining sub - segments. There is no need to set a large number of monitoring points throughout the section, reducing the monitoring cost;

[0038] 3. When predicting the gas concentration data of the remaining sub - segments through the gas concentration prediction and analysis module, the gas concentration and safety factor data at the center point position of a certain sub - segment are used to represent the gas concentration and safety factor data of this sub - segment, greatly reducing the data processing volume and improving the processing speed. Description of the Drawings

[0039] Figure 1 It is a schematic flow chart of the method for monitoring combustible gas in sewage pipelines based on the Internet of Things provided by the present invention;

[0040] Figure 2 It is a schematic diagram of the framework of the system for monitoring combustible gas in sewage pipelines based on the Internet of Things provided by the present invention;

[0041] Figure 3 It is a structural diagram of the electronic device provided by the present invention. Specific Embodiments

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention; obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", "top / bottom end", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0044] In the description of the present invention, it should be noted that unless otherwise clearly defined and limited, the terms "installed", "provided with", "sheathed / connected", "connected", etc. should be understood in a broad sense. For example, "connected" 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 directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] Embodiment 1

[0046] Please refer to Figure 1 , an embodiment provided by the present invention: A method for monitoring combustible gas in sewage pipelines based on the Internet of Things, including the following steps:

[0047] Step S1: Divide the sewage pipes to be monitored into several sub - regions according to the same pipe diameter and the same slope; set monitoring points at the same distance L along the sewage flow direction in the sewage pipes within each sub - region; evenly divide each sewage pipe between every two adjacent monitoring points into n sub - segments, and the length of each sub - segment is L j = L / n; where j = 1, 2, …, n; set gas monitoring instruments at each monitoring point to collect gas concentration data corresponding to the monitoring point.

[0048] 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 the 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 for risk. If the safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to give a maintenance warning for the high - risk section; 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 pipes based on the Internet of Things, where S1 includes the following specific steps:

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

[0053] Step S12: Along the sewage flow direction within each sub - region, set monitoring points at the same distance L in the sewage pipes.

[0054] Step S13: Evenly divide the sewage pipes between every two adjacent monitoring points into n sub - segments, and the length of each sub - segment of the sewage pipe is L j = L / n, where j = 1, 2, …, n;

[0055] Step S14: Set gas monitoring instruments at each monitoring point to monitor the combustible gas concentration data of the monitoring point; where the combustible gases include various gases such as methane CH4, hydrogen sulfide H2S, and carbon monoxide CO.

[0056] The method for monitoring combustible gases in sewage pipes based on the Internet of Things, where S2 includes the following specific content:

[0057] Extract the gas concentration data of each monitoring point and substitute it into the gas concentration distribution prediction formula to predict the gas concentration values of each sub - segment between the monitoring points.

[0058] Suppose 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 pipe diameter and the same slope;

[0059] The prediction formula for the gas concentration distribution of sub-segment j between monitoring point a and monitoring point b is:

[0060]

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

[0062] The method for monitoring combustible gases in sewage pipes based on the Internet of Things, the 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 safety factor calculation formula is:

[0065]

[0066] Where:

[0067] M represents the gas safety factor of sub-segment j, C i represents the concentration of the i-th gas in sub-segment j, which is directly monitored in step S1 or predicted by step S2;

[0068] C imax represents the highest explosion limit concentration of the i-th gas, C imin represents the lowest explosion limit concentration of the i-th gas, C imid represents the median of the explosion limit range of the i-th gas, f i represents the safety ratio coefficient of the i-th gas, N represents the number of types of gases. The safety factors for the remaining sections can also be deduced by analogy from the formula.

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

[0070] Step S41: Compare the safety factor of each sub-section with the set safety factor threshold for risk, and judge the magnitude relationship between the safety factor of each sub-section and 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 give 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.

[0072] Through this embodiment, it can be achieved that monitoring points are set in the monitoring area, gas concentration data of each monitoring point are collected, the gas concentration data of each monitoring point are extracted and substituted into the gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitoring points, the obtained gas concentration is substituted into the safety factor calculation formula to calculate the corresponding safety factor, the safety factors of each sub-section are compared with the set safety factor threshold for risk. If the obtained safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to give 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, which can monitor the state of combustible gases in the sewage pipeline in real time and improve the timeliness and accuracy of combustible gas monitoring.

[0073] Embodiment 2

[0074] Please refer to Figure 2 , the combustible gas monitoring system for sewage pipelines based on the Internet of Things 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, and collects the gas concentration data of each monitoring point;

[0076] The gas concentration prediction and analysis module is used to substitute the gas concentration data of each monitored point extracted into the gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitored points;

[0077] The safety factor calculation module is used to substitute the obtained gas concentration values of each sub-section into the safety factor calculation formula to calculate the safety factors of each sub-section;

[0078] The safety factor comparison module is used to compare the safety factors of each sub - segment with the set safety factor threshold for risk comparison. If the safety factor is less than or equal to the set safety factor threshold, an instruction is sent to the maintenance personnel to give 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] Embodiment III

[0081] Please refer to Figure 3 , this embodiment provides an electronic device, including: a processor and a memory. Among them, a computer program that can be called by the processor is stored in the memory; the processor executes the above - mentioned Internet - of - Things - based monitoring method for flammable gases in sewage pipes by calling the computer program stored in the memory.

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

[0083] The memory can include various types of storage units, such as system memory, read - only memory (ROM), and permanent storage devices. Among them, the ROM can store static data or instructions required by other modules of the processor or the computer. The permanent storage device can be a read - write storage device, and the permanent storage device can be a non - volatile storage device that will not lose the stored instructions and data even after the computer is powered off. The system memory can be a read - write storage device or a volatile read - write storage device, such as dynamic random - access memory. The system memory can store some or all of the instructions and data required by the processor during operation. In addition, the memory can include any combination of computer - readable storage media, including various types of semiconductor storage chips, and disks or optical discs can also be used.

[0084] One or more computer programs are stored in the readable storage medium. When the computer program runs on the computer, the computer is made to execute the Internet - of - Things - based monitoring method for flammable gases in sewage pipes as described above.

[0085] The Internet - of - Things - based monitoring method and system for flammable gases in sewage pipes provided by the present invention have the following advantages:

[0086] 1. A method and system for monitoring combustible gas in sewage pipelines based on the Internet of Things. Monitoring points are set in the monitoring area to collect gas concentration data at each monitoring point. The gas concentration data at each monitoring point is extracted and substituted into the gas concentration distribution prediction formula to predict the gas concentration values of each sub-section between the monitoring points. The obtained gas concentration is substituted into the safety factor calculation formula to calculate the corresponding safety factor. The safety factors of each sub-section are compared with the set safety factor threshold for risk. 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 give 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. It can monitor the state of combustible gas in the sewage pipeline in real time, improving the timeliness and accuracy of combustible gas monitoring;

[0087] 2. Monitoring points are set at a certain distance in the monitoring area to collect gas concentration data. Then, through the gas concentration prediction and analysis module, the gas concentration data of the remaining sub-sections can be predicted, without setting a large number of monitoring points throughout the section, reducing the monitoring cost;

[0088] 3. When predicting the gas concentration data of the remaining sub-sections through the gas concentration prediction and analysis module, the gas concentration and safety factor data at the center point position of a certain sub-section are used to represent the gas concentration and safety factor data of this sub-section, greatly reducing the data processing volume and improving the processing speed.

[0089] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for monitoring combustible gas in sewage pipelines based on the Internet of Things, characterized in that, It includes the following steps: Step S1: Divide the sewage pipelines to be monitored into several sub-regions according to the same pipe diameter and the same slope; set monitoring points at the same interval distance L along the sewage flow direction in each of the sub-regions; evenly divide each of the sub-regions between every two adjacent monitoring points into n sub-segments, and the length of each sub-segment is L j = L / n; where j = 1, 2, …, n; set gas monitoring instruments at each monitoring point to collect gas concentration data corresponding to the monitoring point 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-section between the monitoring points; Step S3: Substitute the obtained gas concentration values of each sub-section into the safety factor calculation formula to calculate the safety factor of each sub-section; Step S4: Compare the safety factors of each sub-section with the set safety factor threshold for risk comparison. If the safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to give a maintenance warning for the high-risk section; if the safety factor is greater than the set safety factor threshold, do not send an instruction to the maintenance personnel.

2. The method for monitoring combustible gas in sewage pipelines based on the Internet of Things according to claim 1, wherein, The said Step S1 includes the following specific steps: Step S11: Divide the sewage pipelines to be monitored into several sub-regions according to the same pipe diameter and the same slope; the sewage pipelines within each sub-region have the same pipe diameter and the same slope; Step S12: Along the sewage flow direction within each sub-region, set monitoring points at the same distance L intervals in the sewage pipeline; Step S13: The sewage pipeline is evenly divided into n sub-segments between every two adjacent monitoring points, and the length of the sewage pipeline for each sub-segment is L j = L / n, where j = 1, 2, …, n; Step S14: Set gas monitoring instruments at each monitoring point to monitor the combustible gas concentration data of the said monitoring point; wherein, the combustible gas includes methane CH4, hydrogen sulfide H2S and carbon monoxide CO.

3. The method for monitoring combustible gas in a sewage pipeline based on the Internet of Things according to claim 1, characterized in that, The said Step S2 includes the following specific contents: Suppose monitoring point a and monitoring point b are in the same sub-region, then the sewage pipelines between monitoring point a and monitoring point b have the same pipe diameter and the same slope; The gas concentration distribution prediction formula for sub-section j between monitoring point a and monitoring point b is: Among them: C iLj represents the concentration of the i-th gas in the sub-segment j between the monitoring point a and the monitoring point b, C ia represents the concentration of the i-th gas at the monitoring point a, C ib represents the concentration of the i-th gas at the monitoring point b, L m represents the distance between the monitoring point a and the monitoring point b, L m = m·L, where m represents the number of intervals of the distance L between the monitoring point a and the monitoring point b; L x represents the distance from the center point of the sub-segment j between the monitoring point a and the monitoring point b to the monitoring point a, L x < L m ; D represents the gas diffusion coefficient, E represents the pipe hydraulic coefficient, and both D and E are constants related to the sewage pipe diameter and the sewage pipe slope, and D and E remain unchanged within the same sub-region.

4. The method for monitoring combustible gas in a sewage pipeline based on the Internet of Things according to claim 1, characterized in that, In the said Step S3, the safety factor calculation formula is: Wherein: M represents the gas safety factor of sub - segment j, C i represents the concentration of the i - th gas in sub - segment j, which is directly monitored in step S1 or predicted by step S2; C imax represents the upper explosion limit concentration of the i-th gas, C imin represents the lower explosion limit concentration of the i-th gas, C imid represents the median value of the explosion limit range of the i-th gas, f i represents the safety proportion coefficient of the i-th gas, N represents the number of types of gases.

5. The combustible gas monitoring system for sewage pipelines based on the Internet of Things is characterized in that Based on the method for monitoring combustible gas in sewage pipelines based on the Internet of Things according to any one of claims 1-4, it is characterized in that it 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; The said data acquisition module is a gas monitoring instrument set at each monitoring point in the area to be monitored, and collects the gas concentration data of each monitoring point; The gas concentration prediction and analysis module is used to substitute the extracted gas concentration data of each monitoring point into the 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 used to substitute the obtained gas concentration values of each sub-section into the safety factor calculation formula to calculate the safety factor of each sub-section; The safety factor comparison module is used to compare the safety factors of each sub-section with the set safety factor threshold for risk comparison. If the safety factor is less than or equal to the set safety factor threshold, send an instruction to the maintenance personnel to give a maintenance warning for the high-risk section; if the safety factor is greater than the set safety factor threshold, do not send an instruction to the maintenance personnel; The said 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.

6. An electronic device, characterized in that, It includes: A processor and a memory, wherein a computer program callable by the processor is stored in the memory; the processor executes the Internet of Things-based combustible gas monitoring method according to any one of claims 1-4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that, Instructions are stored, which, when run on a computer, cause the computer to execute the Internet of Things-based combustible gas monitoring method according to any one of claims 1-4.

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