Real-time pH monitoring system for peritoneal drainage liquid

Abstract

The invention belongs to the technical field of medical instruments, and particularly relates to a real-time pH monitoring system for peritoneal drainage fluid, which comprises an independent power supply system and a power supply control module, and creatively combines key physiological parameters such as a pH abnormal coefficient, a physiological parameter abnormal coefficient and the like through a stability and recovery effect comprehensive evaluation module, so as to realize the real-time pH monitoring of the peritoneal drainage fluid. The method comprises the following steps: calculating to obtain a stability index, then calculating and evaluating an overall monitoring effect based on the stability index, finally comparing a numerical value of the overall monitoring effect with an early warning threshold set on a man-machine interaction terminal with an alarm function, triggering an early warning signal according to an evaluation result, giving a solution according to a specific monitoring condition, and fusing multi-source information. The stability and recovery trend of the intraperitoneal environment and the whole body state of a patient can be more sensitively and comprehensively evaluated, medical staff can find out signs of complications such as abdominal bleeding, infection and anastomotic fistula in the early stage, and an intervention window is moved forwards.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of medical devices, and particularly relates to a real-time pH monitoring system for abdominal cavity drainage fluid. BACKGROUND

[0002] If anastomotic leakage occurs after partial gastrectomy for gastric tumor, gastrointestinal contents containing gastric acid, bile, pancreatic juice and other digestive fluids leak into the abdominal cavity, which can significantly change the acid-base balance of the abdominal cavity environment. Gastric juice is strongly acidic, and pancreatic juice and bile are weakly alkaline. The mixed leakage of these digestive fluids can cause characteristic changes in the pH value of the abdominal cavity drainage fluid. Compared with normal serous drainage fluid, the pH value of fistula early drainage fluid may appear sharp decrease, abnormal increase or dynamic fluctuation, and the patient is prone to symptoms such as fever, abdominal pain and abnormal drainage fluid.

[0003] The existing drainage system only has storage and metering functions, and has no biochemical parameter monitoring capability. Although some studies have proposed integrating sensors in drainage fluid, they mainly focus on pressure, flow or color monitoring, and lack a special system for effectively capturing, analyzing and warning the dynamic change pattern of the pH value of drainage fluid in the specific scenario of post-gastric anastomotic fistula.

[0004] To solve the above problems, the application designs a real-time pH monitoring system for abdominal cavity drainage fluid. SUMMARY

[0005] To solve the problems in the prior art mentioned in the background, the application provides a real-time pH monitoring system for abdominal cavity drainage fluid, which can realize the fusion and intelligent evaluation of multi-parameter data through advanced software algorithms, and can provide a complete and effective solution for the fine management of postoperative patients and the early warning of complications, so as to solve the problems in the background.

[0006] To achieve the above purpose, the application provides a real-time pH monitoring system for abdominal cavity drainage fluid, which comprises a drainage tube placed in the abdominal cavity of a patient, and a sensor integrated cabin body integrated on the drainage tube. The sensor integrated cabin body is internally provided with a fluid channel for accessing the drainage tube. The fluid channel is provided with a data acquisition module. The data acquisition module is used to acquire the pH value and temperature data of the abdominal cavity drainage fluid in real time. The system further comprises a temperature compensation module, a signal processing and transmission module, a stability and recovery effect comprehensive evaluation module and a human-computer interaction terminal with alarm function. The temperature compensation module is used to compensate the pH value of the drainage fluid based on the acquired temperature data to obtain the corrected pH value. The stability and recovery effect comprehensive evaluation module is used to evaluate the overall monitoring effect. The method comprises the following steps: S1. Calculate the pH anomaly coefficient based on the fluctuation and standard deviation of the corrected pH value. ; S2. Collect the patient's body temperature and blood pressure data, and calculate the body temperature deviation based on the temperature and blood pressure data. and blood pressure variability ; S3, Based on body temperature deviation and blood pressure variability Calculate the abnormality coefficient of physiological parameters Physiological parameter abnormality coefficient The calculation formula is: in, , These are the weighting coefficients. This serves as a baseline value for blood pressure fluctuations. S4, Based on pH Anomaly Coefficient With physiological parameter abnormality coefficient Calculate the stability index Stability Index The calculation formula is: in, , pH anomaly coefficient With physiological parameter abnormality coefficient The weighting coefficients, and , As a regulating factor, It is a smoothing constant. It is a natural constant. It is the hyperbolic tangent function; S5, Based on stability index Calculate and evaluate the overall monitoring effect Overall monitoring effect The calculation formula is: in, , The sum of the weighting coefficients is 1. To stabilize the duration of the period, Total monitoring duration; S6. Overall monitoring results The numerical value and the warning threshold set on the human-computer interaction terminal with alarm function If a comparison is made, This triggers an early warning signal. The human-machine interface terminal with alarm function will issue an alarm, indicating that the drainage fluid status is abnormal, and will provide a solution based on the specific monitoring situation.

[0007] In a preferred embodiment based on the above scheme, the signal processing and transmission module is electrically connected to the data acquisition module, and is used to process the sensor signals acquired by the data acquisition module and perform wireless transmission.

[0008] Based on the above scheme, the preferred data acquisition module includes a pH sensing module and a temperature detection module. The pH sensing module includes an electrochemical sensing unit with a working electrode, a counter electrode, and a reference electrode. The sensitive surface of the working electrode is modified with a polydopamine pH-responsive membrane. The outer surface of the polydopamine pH-responsive membrane is covered with a biocompatible hydrogel coating to protect the sensitive membrane from non-specific adsorption and contamination by macromolecules such as proteins and lipids in the peritoneal drainage fluid, thereby significantly reducing signal drift.

[0009] Based on the above scheme, the preferred formula for calculating the corrected pH value is: in, The corrected pH value. To measure the pH value, This is the temperature compensation coefficient. For actual measured temperature, This is the standard temperature.

[0010] Based on the above scheme, the preferred method is to calculate the pH anomaly coefficient based on the fluctuation and standard deviation of the corrected pH value. This includes the following steps: S11. Obtain the corrected pH value sequence for n consecutive time points; S12. Calculate the standard deviation σ and mean value of the corrected pH value sequence. ; S13, Based on standard deviation ,average value Calculate the pH anomaly coefficient based on the preset fluctuation threshold. pH anomaly coefficient The calculation formula is: in, To preset the standard deviation benchmark value, For fluctuation weighting coefficients, The maximum deviation value in the sequence. This represents the reasonable range of pH variation.

[0011] Based on the above scheme, the preferred method involves collecting the patient's body temperature and blood pressure data, and calculating the body temperature deviation based on the body temperature and blood pressure data respectively. and blood pressure variability This includes the following steps: S21, at fixed time intervals Collect patient body temperature data to form a time series. ; S22. Perform noise reduction processing on the collected body temperature data and apply it within a time window. Within this process, a linear fit is performed on the body temperature data to obtain the slope of the fitted line. The slope of the fitted line Indicates time window The slope of the trend in internal body temperature; S23, Based on the slope of the fitted line With sliding time window body temperature deviation Calculate the body temperature deviation. The calculation formula is: in, For reference temperature change range; S24. Collect the patient's systolic blood pressure at fixed time intervals. and diastolic blood pressure Data, calculate mean arterial pressure and form mean arterial pressure sequence; S25. Calculate mean arterial pressure Blood pressure stability index of the sequence ; S26. Set a safe blood pressure range and calculate the mean arterial pressure. Number of data points outside the safe range in the sequence Calculate the percentage of time with abnormal blood pressure. ; S27, Based on Blood Pressure Stability Index Percentage of time with abnormal blood pressure Calculate blood pressure variability .

[0012] Based on the above scheme, the preferred option is the mean arterial pressure. Blood pressure stability index Percentage of time with abnormal blood pressure With blood pressure variability The calculation formulas are as follows: in, Mean arterial pressure Standard deviation of the sequence Mean arterial pressure The average value of the sequence. The statistical mean arterial pressure The number of data points in the sequence that exceed the safe range. Mean arterial pressure The total number of data points in the sequence. , These are the weighting coefficients.

[0013] Based on the above scheme, the preferred embodiment is that the sensor integrated cabin is connected in series to the drainage pipeline through the pipe joints at both ends, so that all the drainage fluid flows through its fluid channel.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates the data acquisition module into the sensor integration chamber, which is then directly connected in series to the drainage tube. This allows all drainage fluid to flow through the sensing area, enabling continuous, in-situ monitoring of the pH and temperature of the drainage fluid. This avoids the lag and sample error associated with intermittent manual sampling, and can instantly reflect minute changes in the intra-abdominal environment, providing real-time data support for clinical decision-making. By incorporating a temperature compensation module, the influence of temperature on pH measurement can be automatically corrected, ensuring the accuracy of pH readings at different body or ambient temperatures. By setting a polydopamine pH-responsive membrane on the sensitive surface of the working electrode and combining it with an external biocompatible hydrogel coating, the non-specific adsorption and contamination of macromolecules such as proteins and lipids in the intra-abdominal drainage fluid on the electrode surface can be effectively reduced, significantly reducing sensor signal drift and ensuring the stability of long-term implantation monitoring. Through a comprehensive evaluation module for stability and recovery effects, the pH anomaly coefficient is creatively incorporated. Physiological parameter abnormality coefficient The stability index was calculated by combining key physiological parameters. Then based on the stability index Calculate and evaluate the overall monitoring effect Ultimately, the overall monitoring effect The numerical value and the warning threshold set on the human-computer interaction terminal with alarm function By comparing and contrasting the results, triggering early warning signals based on the assessment, and providing solutions based on specific monitoring conditions, the integration of multi-source information can more sensitively and comprehensively assess the stability and recovery trend of the patient's intra-abdominal environment and overall condition. This helps medical staff to detect signs of complications such as intra-abdominal bleeding, infection, and anastomotic leakage at an early stage, thus advancing the intervention window. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Fig. 1 This is a system block diagram of a real-time pH monitoring system for abdominal drainage fluid according to the present invention; Fig. 2 For the overall monitoring effect in this invention The evaluation calculation flowchart. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0017] Example: To address the technical problems raised in the background art, this application provides a preferred embodiment: such as Figs. 1-2 As shown, a real-time pH monitoring system for peritoneal drainage fluid includes a drainage tube placed in the patient's abdominal cavity and a sensor integrated chamber integrated on the drainage tube. The sensor integrated chamber has a fluid channel for connecting to the drainage tube, and a data acquisition module is installed in the fluid channel. The data acquisition module is used to collect the pH value and temperature data of the peritoneal drainage fluid in real time. It also includes a temperature compensation module, a signal processing and transmission module, a stability and recovery effect comprehensive evaluation module, and a human-computer interaction terminal with alarm function. The temperature compensation module is used to compensate for the pH value of the drainage fluid based on the collected temperature data, and obtain the corrected pH value. The stability and recovery effect comprehensive evaluation module is used to evaluate the overall monitoring effect. This includes the following steps: S1. Calculate the pH anomaly coefficient based on the fluctuation and standard deviation of the corrected pH value. ; S2. Collect the patient's body temperature and blood pressure data, and calculate the body temperature deviation based on the temperature and blood pressure data. and blood pressure variability ; S3, Based on body temperature deviation and blood pressure variability Calculate the abnormality coefficient of physiological parameters Physiological parameter abnormality coefficient The calculation formula is: in, , These are the weighting coefficients. This serves as a baseline value for blood pressure fluctuations. S4, Based on pH Anomaly Coefficient With physiological parameter abnormality coefficient Calculate the stability index Stability Index The calculation formula is: in, , pH anomaly coefficient With physiological parameter abnormality coefficient The weighting coefficients, and , As a regulating factor, It is a smoothing constant. It is a natural constant. It is the hyperbolic tangent function; S5, Based on stability index Calculate and evaluate the overall monitoring effect Overall monitoring effect The calculation formula is: in, , The sum of the weighting coefficients is 1. To stabilize the duration of the period, Total monitoring duration; S6. Overall monitoring results The numerical value and the warning threshold set on the human-computer interaction terminal with alarm function If a comparison is made, This triggers an early warning signal. The human-machine interface terminal with alarm function will issue an alarm, indicating that the drainage fluid status is abnormal, and will provide a solution based on the specific monitoring situation.

[0018] It should be noted that the abnormality coefficient of physiological parameters Logarithmic function in the calculation formula The ratio structure is approximately linear at smaller values ​​and slows down at larger values, consistent with the buffering effect of body temperature on physiological states. Standardizing blood pressure fluctuations facilitates cross-sectional comparisons between different patients or devices. Body temperature and blood pressure are sensitive indicators reflecting complications such as infection and bleeding. This formula integrates the two into a single physiological abnormality signal, which can be input in parallel with pH abnormality signals to form multimodal monitoring. When the anomaly coefficient increases, the stability index In the calculation formula A value rapidly approaching 0 reflects a sharp decline in stability, consistent with the nonlinear characteristics of system collapse caused by the accumulation of abnormalities in clinical practice. This stability index... In the calculation formula It is a smooth, bounded function, and its output is... This can enhance stability and output smoothness, by adding the denominator. Preventing division by zero enhances numerical stability.

[0019] The advantages of the above scheme are: through a comprehensive evaluation module of stability and recovery effect, it creatively incorporates the pH anomaly coefficient. Physiological parameter abnormality coefficient The stability index was calculated by combining key physiological parameters. Then based on the stability index Calculate and evaluate the overall monitoring effect Ultimately, the overall monitoring effect The numerical value and the warning threshold set on the human-computer interaction terminal with alarm function By comparing and contrasting the results, triggering early warning signals based on the assessment, and providing solutions based on specific monitoring conditions, the integration of multi-source information can more sensitively and comprehensively assess the stability and recovery trend of the patient's intra-abdominal environment and overall condition. This helps medical staff to detect signs of complications such as intra-abdominal bleeding, infection, and anastomotic leakage at an early stage, thus advancing the intervention window.

[0020] Furthermore: In an optional embodiment, the signal processing and transmission module is electrically connected to the data acquisition module and is used to process the sensor signals acquired by the data acquisition module and transmit them wirelessly.

[0021] In an optional embodiment, the data acquisition module includes a pH sensing module and a temperature detection module. The pH sensing module includes an electrochemical sensing unit with a working electrode, a counter electrode, and a reference electrode. The sensitive surface of the working electrode is modified with a polydopamine pH-responsive membrane. The outer surface of the polydopamine pH-responsive membrane is covered with a biocompatible hydrogel coating to protect the sensitive membrane from non-specific adsorption and contamination by macromolecules such as proteins and lipids in the peritoneal drainage fluid, thereby significantly reducing signal drift.

[0022] It should be noted that by setting a polydopamine pH-responsive membrane on the sensitive surface of the working electrode and combining it with an external biocompatible hydrogel coating, the non-specific adsorption and contamination of macromolecules such as proteins and lipids in the peritoneal drainage fluid on the electrode surface can be effectively reduced, which can significantly reduce the signal drift of the sensor and ensure the stability of long-term implantation monitoring.

[0023] In an optional embodiment, the formula for calculating the corrected pH value is: in, The corrected pH value. To measure the pH value, This is the temperature compensation coefficient. For actual measured temperature, This is the standard temperature.

[0024] Furthermore: In an optional embodiment, a pH anomaly coefficient is calculated based on the fluctuation and standard deviation of the corrected pH value. This includes the following steps: S11. Obtain the corrected pH value sequence for n consecutive time points; S12. Calculate the standard deviation σ and mean value of the corrected pH value sequence. ; S13, Based on standard deviation ,average value Calculate the pH anomaly coefficient based on the preset fluctuation threshold. pH anomaly coefficient The calculation formula is: in, To preset the standard deviation benchmark value, For fluctuation weighting coefficients, The maximum deviation value in the sequence. This represents the reasonable range of pH variation.

[0025] In one optional embodiment, the patient's body temperature and blood pressure data are collected, and the body temperature deviation is calculated based on the body temperature and blood pressure data respectively. and blood pressure variability This includes the following steps: S21, at fixed time intervals Collect patient body temperature data to form a time series. ; S22. Perform noise reduction processing on the collected body temperature data and apply it within a time window. Within this process, a linear fit is performed on the body temperature data to obtain the slope of the fitted line. The slope of the fitted line Indicates time window The slope of the trend in internal body temperature; S23, Based on the slope of the fitted line With sliding time window body temperature deviation Calculate the body temperature deviation. The calculation formula is: in, For reference to the range of temperature changes, it should be noted that this formula captures the trend deviation of body temperature within a specific time window, rather than instantaneous fluctuations, which is more consistent with the gradual changes in body temperature caused by infection or inflammation. S24. Collect the patient's systolic blood pressure at fixed time intervals. and diastolic blood pressure Data, calculate mean arterial pressure and form mean arterial pressure sequence; S25. Calculate mean arterial pressure Blood pressure stability index of the sequence ; S26. Set a safe blood pressure range and calculate the mean arterial pressure. Number of data points outside the safe range in the sequence Calculate the percentage of time with abnormal blood pressure. ; S27, Based on Blood Pressure Stability Index Percentage of time with abnormal blood pressure Calculate blood pressure variability .

[0026] In an optional embodiment, mean arterial pressure Blood pressure stability index Percentage of time with abnormal blood pressure With blood pressure variability The calculation formulas are as follows: in, Mean arterial pressure Standard deviation of the sequence Mean arterial pressure The average value of the sequence. The statistical mean arterial pressure The number of data points in the sequence that exceed the safe range. Mean arterial pressure The total number of data points in the sequence. , These are the weighting coefficients.

[0027] It should be noted that: Blood pressure stability index Reflects the intrinsic stability of blood pressure fluctuations; a higher value indicates more stable blood pressure, and the percentage of time with abnormal blood pressure is also considered. It reflects the proportion of time that blood pressure is out of control, and visually displays the degree of abnormality.

[0028] In an optional embodiment, the sensor integration chamber is connected in series to the drainage pipeline through pipe joints at both ends, so that all drainage fluid flows through its fluid channel.

[0029] It should be noted that by integrating the data acquisition module into the sensor integration chamber and then directly connecting the sensor integration chamber in series with the drainage tube, all drainage fluid can flow through the sensing area. This enables continuous, in-situ monitoring of the pH and temperature of the drainage fluid, avoiding the lag and sample error of manual intermittent sampling. It can also reflect minute changes in the intra-abdominal environment in real time, providing real-time data support for clinical decision-making.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time pH monitoring system for peritoneal drainage fluid, comprising a drainage tube placed in the patient's peritoneal cavity, and a sensor integrated chamber integrated on the drainage tube, characterized in that, The sensor integration chamber has a fluid channel for connecting the drainage tube. The fluid channel is equipped with a data acquisition module, which is used to collect the pH value and temperature data of the abdominal drainage fluid in real time. It also includes a temperature compensation module, a signal processing and transmission module, a stability and recovery effect comprehensive evaluation module, and a human-machine interaction terminal with alarm function. The temperature compensation module is used to compensate for the pH value of the drainage fluid based on the collected temperature data, and obtain the corrected pH value. The stability and recovery effect comprehensive evaluation module is used to evaluate the overall monitoring effect. This includes the following steps: S1. Calculate the pH anomaly coefficient based on the fluctuation and standard deviation of the corrected pH value. ; S2. Collect the patient's body temperature and blood pressure data, and calculate the body temperature deviation based on the temperature and blood pressure data. and blood pressure variability ; S3, Based on body temperature deviation and blood pressure variability Calculate the abnormality coefficient of physiological parameters Physiological parameter abnormality coefficient The calculation formula is: in, , These are the weighting coefficients. This serves as a baseline value for blood pressure fluctuations. S4, Based on pH Anomaly Coefficient With physiological parameter abnormality coefficient Calculate the stability index Stability Index The calculation formula is: in, , pH anomaly coefficient With physiological parameter abnormality coefficient The weighting coefficients, and , As a regulating factor, It is a smoothing constant. It is a natural constant. It is the hyperbolic tangent function; S5, Based on stability index Calculate and evaluate the overall monitoring effect Overall monitoring effect The calculation formula is: in, , The sum of the weighting coefficients is 1. To stabilize the duration of the period, Total monitoring duration; S6. Overall monitoring results The numerical value and the warning threshold set on the human-computer interaction terminal with alarm function If a comparison is made, This triggers an early warning signal. The human-machine interface terminal with alarm function will issue an alarm, indicating that the drainage fluid status is abnormal, and will provide a solution based on the specific monitoring situation.

2. The real-time pH monitoring system for peritoneal drainage fluid according to claim 1, characterized in that: The signal processing and transmission module is electrically connected to the data acquisition module and is used to process the sensor signals acquired by the data acquisition module and transmit them wirelessly.

3. A real-time pH monitoring system for peritoneal drainage fluid according to claim 2, characterized in that: The data acquisition module includes a pH sensing module and a temperature detection module. The pH sensing module includes an electrochemical sensing unit with a working electrode, a counter electrode, and a reference electrode. The sensitive surface of the working electrode is modified with a polydopamine pH-responsive membrane. The outer surface of the polydopamine pH-responsive membrane is covered with a biocompatible hydrogel coating to protect the sensitive membrane from non-specific adsorption and contamination by macromolecules such as proteins and lipids in the peritoneal drainage fluid, thereby significantly reducing signal drift.

4. A real-time pH monitoring system for peritoneal drainage fluid according to claim 3, characterized in that: The formula for calculating the corrected pH value is: in, The corrected pH value. To measure the pH value, This is the temperature compensation coefficient. For actual measured temperature, This is the standard temperature.

5. A real-time pH monitoring system for peritoneal drainage fluid according to claim 4, characterized in that: The pH anomaly coefficient was calculated based on the fluctuation and standard deviation of the corrected pH value. This includes the following steps: S11. Obtain the corrected pH value sequence for n consecutive time points; S12. Calculate the standard deviation σ and mean value of the corrected pH value sequence. ; S13, Based on standard deviation ,average value Calculate the pH anomaly coefficient based on the preset fluctuation threshold. pH anomaly coefficient The calculation formula is: in, To preset the standard deviation benchmark value, For fluctuation weighting coefficients, The maximum deviation value in the sequence. This represents the reasonable range of pH variation.

6. A real-time pH monitoring system for peritoneal drainage fluid according to claim 5, characterized in that: Collect the patient's body temperature and blood pressure data, and calculate the body temperature deviation based on the data. and blood pressure variability This includes the following steps: S21, at fixed time intervals Collect patient body temperature data to form a time series. ; S22. Perform noise reduction processing on the collected body temperature data and apply it within a time window. Within this process, a linear fit is performed on the body temperature data to obtain the slope of the fitted line. The slope of the fitted line Indicates time window The slope of the trend in internal body temperature; S23, Based on the slope of the fitted line With sliding time window body temperature deviation Calculate the body temperature deviation. The calculation formula is: in, For reference temperature change range; S24. Collect the patient's systolic blood pressure at fixed time intervals. and diastolic blood pressure Data, calculate mean arterial pressure and form mean arterial pressure sequence; S25. Calculate mean arterial pressure Blood pressure stability index of the sequence ; S26. Set a safe blood pressure range and calculate the mean arterial pressure. Number of data points outside the safe range in the sequence Calculate the percentage of time with abnormal blood pressure. ; S27, Based on Blood Pressure Stability Index Percentage of time with abnormal blood pressure Calculate blood pressure variability .

7. A real-time pH monitoring system for peritoneal drainage fluid according to claim 6, characterized in that: Mean arterial pressure Blood pressure stability index Percentage of time with abnormal blood pressure With blood pressure variability The calculation formulas are as follows: in, Mean arterial pressure Standard deviation of the sequence Mean arterial pressure The average value of the sequence. The statistical mean arterial pressure The number of data points in the sequence that exceed the safe range. Mean arterial pressure The total number of data points in the sequence. , These are the weighting coefficients.

8. A real-time pH monitoring system for peritoneal drainage fluid according to claim 7, characterized in that: The sensor integration chamber is connected in series to the drainage pipeline through pipe joints at both ends, so that all the drainage fluid flows through its fluid channel.

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