Sewage well combustible gas monitoring pumping drainage method and system

Abstract

The invention discloses a sewage well combustible gas monitoring pumping and draining method and system, and relates to the technical field of combustible gas monitoring, and the method carries out the dynamic compensation of a pumping and draining threshold value based on monitoring data, separates an interference signal, starts a pumping and draining mechanism in time, and carries out the dynamic adjustment for a high-humidity environment, thereby reducing the sensor false alarm caused by the environment change, and improving the reliability of the pumping and draining system. The system comprehensively solves the problem of combustible gas monitoring pain points in high-temperature, high-humidity and multi-gas interference environments, achieves the purposes of precise monitoring and active prevention and control, promotes the intelligent management of data, can efficiently analyze the data and provide decision support, and improves the overall level and efficiency of urban sewage management.

Description

Technical Field

[0001] The invention belongs to the technical field of combustible gas monitoring, and in particular relates to a method and system for monitoring and draining combustible gas in a sewage well. Background Art

[0002] Closed spaces such as urban sewers and septic tanks will continue to produce methane due to the decay of organic matter, accompanied by toxic and harmful combustible gases such as hydrogen sulfide and ammonia. Among them, hydrogen sulfide gas is not only highly toxic, but also has a wider explosion limit than methane. Once it encounters an open flame, it can easily cause a deflagration. The current mainstream monitoring technology for combustible gases such as hydrogen sulfide still has significant defects: the humidity in the sewage well is relatively high, often reaching 80% or above. High humidity and cross-sensitivity to multiple gases may cause false alarms from sensors. In addition, hydrogen sulfide is easily soluble in water in such a high humidity environment to form hydrosulfuric acid, which corrodes the detection conductors and circuit contacts of the gas monitoring equipment. If the original fixed threshold is maintained, the actual dangerous concentration may be missed, or more false alarms may occur when the humidity is high.

[0003] In summary, there is an urgent need to develop an intelligent monitoring system that is resistant to high humidity, high precision and adaptable to complex environments, and to build an all-weather prevention and control system for combustible gas risks in sewage wells in combination with active pumping technology. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a method and system for monitoring and extracting combustible gas in a sewage well to solve the above-mentioned technical problems.

[0005] On the one hand, a method for monitoring and draining combustible gas from a sewage well is provided, the method comprising the steps of: Acquiring linkage data inside the sewage well, wherein the linkage data includes combustible gas concentration data and environmental monitoring data; In a low-corrosive environment, according to a pre-established compensation and adjustment model, an extraction threshold is calculated based on the linkage data, and the combustible gas in the sewage well is extracted according to the extraction threshold. In the low-corrosive environment, the combustible gas concentration data and the environmental monitoring data are both lower than the critical point.

[0006] This extraction method dynamically compensates the extraction threshold based on monitoring data, separates interference signals, promptly starts the extraction mechanism, and dynamically adjusts to high humidity environments, thereby reducing sensor false alarms caused by environmental changes.

[0007] Preferably, the combustible gas concentration data specifically includes: chlorine gas concentration data, hydrogen sulfide gas concentration data and sulfur dioxide gas concentration data; The environmental monitoring data specifically includes: environmental temperature data and air humidity data.

[0008] By obtaining the concentrations of multiple combustible gases, the algorithm can accurately calculate the mutual influence of various gas concentrations, thereby providing more accurate data. Here, it is considered that in a high humidity environment, hydrogen sulfide gas dissolves in water to produce hydrogen sulfuric acid to corrode the metal electrode of the sensor, causing an error between the monitored hydrogen sulfide gas concentration and the actual hydrogen sulfide gas concentration. The higher the ambient temperature and air humidity, the faster the reaction rate. Sulfur dioxide gas and hydrogen sulfide gas generate polysulfides, which will accelerate the sulfidation corrosion of the metal electrode of the sensor. At the same time, the chloride ions in the chlorine gas will damage the metal passivation film.

[0009] As a preference, The compensation adjustment model is specifically: ; in, To compensate for the adjusted extraction threshold, To monitor the concentration of hydrogen sulfide gas, is the ambient temperature data, is the standard ambient temperature, is the reference concentration of chlorine gas, is the reference concentration of sulfur dioxide gas, Air humidity data, is the chlorine gas concentration data, is the sulfur dioxide gas concentration data, To monitor the corrosion coefficient of the conductor, is the attention score; Among them, the attention score is specifically obtained by the following formula: ; in, is the air humidity data weight coefficient, is the weight coefficient of ambient temperature data, is the weight coefficient of chlorine gas concentration data, is the weight coefficient of sulfur dioxide gas concentration data.

[0010] Preferably, the establishment process of the compensation adjustment model is specifically as follows: Preprocessing the linkage data; The random forest algorithm is used to calculate the corresponding weight coefficients of the chlorine gas concentration data, the sulfur dioxide gas concentration data, the ambient temperature data and the air humidity data in the linkage data, and the optimal feature data is generated in combination with the recursive feature elimination strategy, and part of the optimal feature data is taken as a training set; Integrate the convolutional neural network and graph neural network models to obtain a multi-dimensional network model; The training set is input into the multi-dimensional network model for training, and the training result is obtained as a compensation adjustment model.

[0011] By preprocessing the linkage data, the system can clean and screen out valid data, filter out noise and interference, and use the random forest algorithm to calculate the weight coefficients of chlorine, sulfur dioxide concentration, ambient temperature and air humidity, which can effectively identify the influence of different gases and environmental factors on the monitoring results. Then, the recursive feature elimination strategy is used to generate the optimal feature data, which can further simplify the complexity of the model and ensure that only the feature data most relevant to the monitoring target is used. At the same time, the convolutional neural network and the graph neural network are integrated to form a multi-dimensional network model. By utilizing the advantages of each model and processing information from multiple dimensions, the compensation adjustment model can be quickly obtained to optimize the real-time response capability of the gas monitoring system.

[0012] Preferably, the preprocessing of the linkage data specifically includes the following steps: Identifying and removing abnormal data in the linkage data to eliminate random errors; Using cubic spline interpolation method to estimate the removed data and missing data in the linkage data, and filling the calculated interpolation results into the linkage data; The filled linkage data are subjected to Min-Max normalization processing to eliminate the influence caused by the differences in dimensions and magnitudes between different variables.

[0013] By identifying and eliminating abnormal data in the linkage data, random errors and noise can be effectively eliminated. Then, the cubic spline interpolation method is used to estimate the eliminated data and missing data, and data filling can be performed to generate a smooth and continuous data sequence. After data filling, Min-Max normalization processing is used to scale the data values ​​to a uniform range. The processed data is more stable and balanced, and is not affected by abnormal values ​​and missing values.

[0014] Preferably, the combustible gas concentration data is acquired by a sensor. Performing fault monitoring on the sensor in a highly corrosive environment; When the sensor operates normally, the extraction power is calculated based on the combustible gas concentration data according to a preset concentration-power correlation model, and the combustible gas in the sewage well is extracted according to the extraction power. In the highly corrosive environment, the combustible gas concentration data and one or more of the environmental monitoring data are higher than the critical point.

[0015] By distinguishing between highly corrosive environments and low-corrosive environments and adopting differentiated monitoring and extraction strategies to meet various operational needs, it is not only possible to improve overall operational efficiency, but also reduce operating costs and overall improve the response speed to combustible gas risks.

[0016] As a preference, The concentration-power correlation model is specifically: ; in, is the reference power, is the benchmark efficiency, is the temperature efficiency attenuation coefficient, is the humidity efficiency attenuation coefficient, is the standard air humidity data, The extraction power.

[0017] Preferably, the fault monitoring of the sensor specifically includes the following steps: Obtain voltage data and current data of the hydrogen sulfide sensor; Calculating a voltage data residual term and a current data residual term according to the voltage data and the current data, wherein the voltage data residual term is composed of a plurality of residual points; based on the voltage data residual term, identifying and extracting outlier residual points of the voltage data residual term, and evaluating the mutation degree and difference of each outlier residual point; The fault of the sensor is judged according to the mutation degree and difference analysis of the outlier residual point.

[0018] According to the mutation degree and difference analysis of outlier residual points, the fault type and severity of the sensor can be judged, providing clearer fault information for the maintenance process, facilitating the implementation of differentiated monitoring strategies based on the monitored sensor fault type and level, reducing system blind spots and ensuring timely response.

[0019] Preferably, the step of evaluating the mutation degree and difference of each outlier residual point specifically includes the following steps: Obtain the slope and corresponding residual value of each residual point in the voltage residual term, and obtain the mutation degree of each outlier residual point; A corresponding chain code sequence is generated according to the slope of each outlier residual point, and the difference of each outlier residual point is obtained based on the chain code sequence of the outlier residual point, the residual value and the residual term of the current data.

[0020] When evaluating each outlier residual point, by obtaining the slope and corresponding residual value of each residual point in the voltage residual term, the rate and amplitude of change of each outlier point can be deeply analyzed, and the degree of mutation of each outlier residual point can be further obtained, so that the system can clearly identify the severity of the fault, and then generate the corresponding chain code sequence according to the slope of each outlier residual point, which can effectively track and record the change pattern during the fault development process.

[0021] On the other hand, a sewage well combustible gas monitoring and extraction system is provided, including the following contents: Negative pressure extraction device: installed in the sewage well, used to extract and release the combustible gas in the sewage well; Linkage data acquisition module: used to collect linkage data in the sewage well, the linkage data includes combustible gas concentration data and environmental monitoring data, wherein the combustible gas concentration data is acquired by sensors; Sensor monitoring module: used for fault monitoring of the sensor; Linkage data processing module: used for calculating the extraction threshold and extraction power according to the linkage data; Control end: used to control the operation of the negative pressure extraction device according to the calculation results of the linkage data processing module.

[0022] The beneficial effects of the present invention are as follows: the extraction method dynamically compensates for the extraction threshold based on the monitoring data, separates the interference signal, promptly starts the extraction mechanism, and dynamically adjusts the high humidity environment, thereby reducing the sensor false alarms caused by environmental changes, and comprehensively solves the pain points of combustible gas monitoring in high temperature, high humidity and multi-gas interference environments, achieving the purpose of accurate monitoring and active prevention and control, promoting the intelligent management of data, being able to efficiently analyze data and provide decision-making support, and improving the overall level and efficiency of urban sewage management. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A step diagram of the method for monitoring and draining combustible gas from a sewage well provided by the present invention; Figure 2 This is a schematic structural diagram of the sewage well combustible gas monitoring and extraction system provided by the present invention. DETAILED DESCRIPTION

[0025] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0026] The disclosure below provides many different embodiments or examples to realize different structures of the present invention. In order to simplify the disclosure of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present invention.

[0027] The embodiments of the invention are described in detail below with reference to the accompanying drawings.

[0028] like Figure 1 As shown, a method for monitoring and draining combustible gas from a sewage well comprises the following steps: Obtain linkage data inside the sewage well, including combustible gas concentration data and environmental monitoring data; In a low-corrosive environment, according to the pre-established compensation and adjustment model, the extraction threshold is calculated based on the linkage data, and the combustible gas in the sewage well is extracted according to the extraction threshold. In a low-corrosive environment, the combustible gas concentration data and environmental monitoring data are both below the critical point.

[0029] It comprehensively solves the pain points of combustible gas monitoring in high temperature, high humidity and multi-gas interference environments, avoids underreporting due to reduced sensitivity of monitoring equipment, achieves the purpose of accurate monitoring and active prevention and control, promotes intelligent data management, can efficiently analyze data and provide decision-making support, and improve the overall level and efficiency of urban sewage management.

[0030] More specifically, the combustible gas concentration data specifically include: chlorine gas concentration data, hydrogen sulfide gas concentration data and sulfur dioxide gas concentration data; Environmental monitoring data specifically include: ambient temperature data and air humidity data.

[0031] Among them, the critical points are: hydrogen sulfide gas concentration is 10 ppm, ambient temperature is 35°C, and air humidity is 95%RH.

[0032] Taking into account the impact of the chemical reactions of sulfur dioxide and chlorine in high temperature and high humidity environments on hydrogen sulfide sensors, the concentrations of sulfur dioxide gas and chlorine are further monitored here to eliminate the cross-influence of sulfur dioxide, chlorine and hydrogen sulfide, achieving a leapfrog upgrade from single gas monitoring to multi-parameter linkage. It not only improves the accuracy and reliability of combustible gas monitoring in sewage wells, but also has strong adaptability and easy maintenance in actual environments.

[0033] More specifically, the compensation adjustment model is as follows: ; in, To compensate for the adjusted extraction threshold, To monitor the concentration of hydrogen sulfide gas, is the ambient temperature data, is the standard ambient temperature, is the reference concentration of chlorine gas, is the reference concentration of sulfur dioxide gas, Air humidity data, is the chlorine gas concentration data, is the sulfur dioxide gas concentration data, To monitor the corrosion coefficient of the conductor, is the attention score; Among them, the attention score is specifically obtained by the following formula: ; in, is the air humidity data weight coefficient, is the weight coefficient of ambient temperature data, is the weight coefficient of chlorine gas concentration data, is the weight coefficient of sulfur dioxide gas concentration data.

[0034] In the above compensation adjustment model, the corrosion coefficient of the monitoring conductor is specifically obtained by the following formula: ; in, is the material property fitting coefficient, is the material corrosion rate, is the radius of the reaction zone, is the diffusion coefficient, is the reaction rate constant.

[0035] More specifically, the process of establishing the compensation adjustment model is as follows: Preprocess linkage data; The random forest algorithm is used to calculate the corresponding weight coefficients of the chlorine gas concentration data, sulfur dioxide gas concentration data, ambient temperature data and air humidity data in the linkage data, and the optimal feature data is generated by combining the recursive feature elimination strategy, and some of the optimal feature data are taken as the training set; Integrate the convolutional neural network and graph neural network models to obtain a multi-dimensional network model; The training set is input into the multi-dimensional network model for training, and the training result is obtained as a compensation adjustment model.

[0036] The process of preprocessing the data improves the reliability of subsequent data analysis, making the data reflection of chlorine, sulfur dioxide and environmental conditions more accurate. In addition, the use of intelligent weight calculation methods such as the random forest algorithm makes the model more scientific and reasonable when dealing with multiple gas monitoring, which facilitates better efficiency in the subsequent machine learning model training process, helps the model focus on important data, enhances the system's analysis capabilities under multi-parameter linkage changes, and optimizes the overall management and response process.

[0037] More specifically, when preprocessing linkage data, the following steps are specifically included: Identify and remove abnormal data in linkage data to eliminate random errors; The cubic spline interpolation method is used to estimate the removed data and missing data in the linkage data, and the calculated interpolation results are filled into the linkage data; The filled linkage data are subjected to Min-Max normalization processing to eliminate the impact of the differences in dimensions and magnitudes between different variables.

[0038] The linkage data after abnormal data identification and elimination can ensure the reliability and accuracy of data analysis and reduce the decision-making risks caused by erroneous data. The cubic spline interpolation method used for data filling takes into account the changing trend and local characteristics of the data when estimating, which can help the system obtain a complete linkage data set and provide sufficient information basis for subsequent model training. After the filled data is processed by Min-Max normalization, it can ensure that each feature has a consistent weight during model training, which helps to improve the generalization ability and robustness of the model.

[0039] More specifically, the combustible gas concentration data is collected by sensors. Fault monitoring of sensors in highly corrosive environments; When the sensor operates normally, the extraction power is calculated based on the combustible gas concentration data according to the preset concentration-power correlation model, and the combustible gas in the sewage well is extracted according to the extraction power. In a highly corrosive environment, one or more of the combustible gas concentration data and the environmental monitoring data are higher than the critical point.

[0040] Based on the combustible gas concentration data, the system uses a preset concentration-power correlation model to calculate the extraction power. This scientific calculation method can reasonably adjust the extraction power according to the real-time combustible gas concentration, making the extraction process more efficient, thereby quickly controlling the combustible gas concentration in the sewage well, and pre-diagnosing faults for sensors, increasing the ability to prevent and control potential hazards, and being able to promptly detect abnormal equipment conditions and prevent data errors caused by sensor failures.

[0041] More specifically, the concentration-power correlation model is: ; in, is the reference power, is the benchmark efficiency, is the temperature efficiency attenuation coefficient, is the humidity efficiency attenuation coefficient, is the standard air humidity data, The extraction power.

[0042] Generally, in a closed environment such as a sewage well, the temperature efficiency attenuation coefficient is taken as 0.005 / °C, and the humidity efficiency attenuation coefficient is taken as 0.002 / %RH.

[0043] More specifically, when performing fault monitoring on a sensor, the following steps are specifically included: Obtain voltage data and current data of the hydrogen sulfide sensor; A voltage data residual term and a current data residual term are calculated according to the voltage data and the current data, wherein the voltage data residual term is composed of a plurality of residual points; based on the voltage data residual term, outlier residual points of the voltage data residual term are identified and extracted, and the mutation degree and difference of each outlier residual point are evaluated; The sensor fault is judged based on the mutation degree and difference analysis of the outlier residual points.

[0044] By analyzing the data, the residual term of the data is obtained, and the abnormality is analyzed in the residual term of the data, so as to reduce the interference of noise fluctuations in the original data on the abnormality analysis, so as to make the sensor fault diagnosis more accurate. The slope and residual value of the residual point in the residual term of the voltage data are used to obtain the mutation degree of each outlier residual point in the residual term of the voltage data. When obtaining the mutation degree, the continuous change of the slope of the residual point is analyzed to make the mutation degree more accurate, which can better reflect the mutation characteristics of the voltage data collected by the sensor. The fault analysis results not only help to determine the key areas that need regular inspection, but also guide equipment upgrades and replacements, and improve the performance and effectiveness of the overall system.

[0045] More specifically, the following steps are included to evaluate the mutation degree and difference of each outlier residual point: Obtain the slope and corresponding residual value of each residual point in the voltage residual term, and obtain the mutation degree of each outlier residual point; The corresponding chain code sequence is generated according to the slope of each outlier residual point, and the difference of each outlier residual point is obtained based on the chain code sequence of the outlier residual point, the residual value and the residual term of the current data.

[0046] The slope data of each outlier residual point can provide a preliminary indication of the nature of the fault, which helps to quickly identify the possible cause of the fault. At the same time, it provides a priority basis for subsequent fault response, making subsequent fault analysis more intuitive and convenient for system learning and optimization. It is expected that similar faults will be identified in future monitoring. Analysis based on multiple data related to outlier residual points can make fault judgment more accurate and reflect more complex fault phenomena, thereby improving the accuracy of fault diagnosis and greatly enhancing the fault identification, analysis and response capabilities of the monitoring system.

[0047] like Figure 2 As shown, a sewage well combustible gas monitoring and extraction system includes the following contents: a negative pressure extraction device: installed in the sewage well, used to extract and release the combustible gas in the sewage well; Linkage data acquisition module: used to collect linkage data in sewage wells. The linkage data includes combustible gas concentration data and environmental monitoring data, among which the combustible gas concentration data is acquired by sensors. Sensor monitoring module: used to monitor sensor faults; Linkage data processing module: used to calculate the extraction threshold and extraction power according to the linkage data; Control end: used to control the operation of the negative pressure extraction device according to the calculation results of the linkage data processing module.

[0048] The negative pressure extraction device here generally uses a vacuum pump, which can create a negative pressure environment to extract the gas in the manhole. This type of pump is usually corrosion-resistant, can adapt to the complex environment in the sewage well, and has efficient extraction capabilities. It is suitable for large-area gas extraction needs and ensures that the gas is safely extracted.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for monitoring and draining combustible gas from a sewage well, characterized in that: The steps of the method include: Acquiring linkage data inside the sewage well, wherein the linkage data includes combustible gas concentration data and environmental monitoring data; In a low-corrosive environment, according to a pre-established compensation and adjustment model, an extraction threshold is calculated based on the linkage data, and the combustible gas in the sewage well is extracted according to the extraction threshold. In the low-corrosive environment, the combustible gas concentration data and the environmental monitoring data are both lower than the critical point.

2. The method for monitoring and draining combustible gas from a sewage well according to claim 1, characterized in that: The combustible gas concentration data specifically include: chlorine gas concentration data, hydrogen sulfide gas concentration data and sulfur dioxide gas concentration data; The environmental monitoring data specifically includes: environmental temperature data and air humidity data.

3. The method for monitoring and draining combustible gas from a sewage well according to claim 2, characterized in that: The compensation adjustment model is specifically: ; in, To compensate for the adjusted extraction threshold, To monitor the concentration of hydrogen sulfide gas, is the ambient temperature data, is the standard ambient temperature, is the reference concentration of chlorine gas, is the reference concentration of sulfur dioxide gas, Air humidity data, is the chlorine gas concentration data, is the sulfur dioxide gas concentration data, To monitor the corrosion coefficient of the conductor, is the attention score; Among them, the attention score is specifically obtained by the following formula: ; in, is the air humidity data weight coefficient, is the weight coefficient of ambient temperature data, is the weight coefficient of chlorine gas concentration data, is the weight coefficient of sulfur dioxide gas concentration data.

4. The method for monitoring and draining combustible gas from a sewage well according to claim 3 is characterized in that: The establishment process of the compensation adjustment model is specifically as follows: Preprocessing the linkage data; The random forest algorithm is used to calculate the corresponding weight coefficients of the chlorine gas concentration data, the sulfur dioxide gas concentration data, the ambient temperature data and the air humidity data in the linkage data, and the optimal feature data is generated in combination with the recursive feature elimination strategy, and part of the optimal feature data is taken as a training set; Integrate the convolutional neural network and graph neural network models to obtain a multi-dimensional network model; The training set is input into the multi-dimensional network model for training, and the training result is obtained as a compensation adjustment model.

5. The method for monitoring and draining combustible gas from a sewage well according to claim 4 is characterized in that: The preprocessing of the linkage data specifically includes the following steps: Identifying and removing abnormal data in the linkage data to eliminate random errors; Using cubic spline interpolation method to estimate the removed data and missing data in the linkage data, and filling the calculated interpolation results into the linkage data; The filled linkage data are subjected to Min-Max normalization processing to eliminate the influence caused by the differences in dimensions and magnitudes between different variables.

6. The method for monitoring and draining combustible gas from a sewage well according to claim 2, characterized in that: The combustible gas concentration data is collected and acquired by the sensor. Performing fault monitoring on the sensor in a highly corrosive environment; When the sensor operates normally, the extraction power is calculated based on the combustible gas concentration data according to a preset concentration-power correlation model, and the combustible gas in the sewage well is extracted according to the extraction power. In the highly corrosive environment, the combustible gas concentration data and one or more of the environmental monitoring data are higher than the critical point.

7. The method for monitoring and draining combustible gas from a sewage well according to claim 6, characterized in that: The concentration-power correlation model is specifically: ; in, is the reference power, is the benchmark efficiency, is the temperature efficiency attenuation coefficient, is the humidity efficiency attenuation coefficient, is the standard air humidity data, The extraction power.

8. The method for monitoring and draining combustible gas from a sewage well according to claim 6, characterized in that: When the sensor is subjected to fault monitoring, the following steps are specifically included: Obtain voltage data and current data of the hydrogen sulfide sensor; Calculating a voltage data residual term and a current data residual term according to the voltage data and the current data, wherein the voltage data residual term is composed of a plurality of residual points; based on the voltage data residual term, identifying and extracting outlier residual points of the voltage data residual term, and evaluating the mutation degree and difference of each outlier residual point; The fault of the sensor is judged according to the mutation degree and difference analysis of the outlier residual point.

9. The method for monitoring and draining combustible gas from a sewage well according to claim 8, characterized in that: When evaluating the mutation degree and difference of each outlier residual point, the following steps are specifically included: Obtain the slope and corresponding residual value of each residual point in the voltage residual term, and obtain the mutation degree of each outlier residual point; A corresponding chain code sequence is generated according to the slope of each outlier residual point, and the difference of each outlier residual point is obtained based on the chain code sequence of the outlier residual point, the residual value and the residual term of the current data.

10. A sewage well combustible gas monitoring and extraction system, characterized in that: It includes the following: Negative pressure extraction device: installed in the sewage well, used to extract and release the combustible gas in the sewage well; Linkage data acquisition module: used to collect linkage data in the sewage well, the linkage data includes combustible gas concentration data and environmental monitoring data, wherein the combustible gas concentration data is acquired by sensors; Sensor monitoring module: used for fault monitoring of the sensor; Linkage data processing module: used for calculating the extraction threshold and extraction power according to the linkage data; Control end: used to control the operation of the negative pressure extraction device according to the calculation results of the linkage data processing module.

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