Operation and maintenance intelligent control system of wastewater online detection equipment

Abstract

The invention relates to the technical field of waste water monitoring, in particular to an operation and maintenance intelligent control system of waste water online detection equipment, which comprises the following steps: calculating the propagation time delay of pressure waves in a pipeline through a pressure sensor, and judging whether an abnormal condition exists in the pipeline according to the magnitude and trend of the amplitude change rate of the pressure waves at adjacent time points; evaluating the abnormal condition of the pipeline by introducing a comprehensive evaluation index, and judging possible faults of the equipment; when the abnormal signal continuously exceeds a first preset threshold value, a pressure sensor is automatically started, cross check is carried out, and the position of a fault sensor is positioned through comparative analysis; comprehensively evaluating the grade of the equipment fault according to a fault positioning result in combination with the strength and the duration of the abnormal signal; and carrying out coupling analysis on the equipment fault level signal and the wastewater flow parameter, and dynamically generating an operation and maintenance instruction. The pressure waves are analyzed from different angles, and the condition in the pipeline can be monitored more comprehensively and accurately.

Description

Technical Field

[0001] This invention relates to the field of wastewater monitoring technology, specifically to an intelligent control system for the operation and maintenance of online wastewater monitoring equipment. Background Technology

[0002] With the acceleration of industrialization, continuous population growth, and ongoing urbanization, water pollution has become an increasingly serious problem and one of the major environmental challenges facing the world. Wastewater, as the main carrier of various pollution sources, poses a serious threat to natural water bodies, the ecological environment, and human health when discharged directly without effective treatment. Against this backdrop, real-time and accurate monitoring of wastewater is particularly important. It is not only a key basis for assessing the degree of environmental pollution, but also an important prerequisite for formulating scientific and reasonable pollution control strategies and ensuring water environment safety.

[0003] In recent years, online wastewater monitoring equipment has emerged and been widely used. These devices can continuously and in real time monitor various pollutant indicators in wastewater. By being installed at key nodes such as sewage outlets, the monitoring data is transmitted to regulatory authorities in real time, realizing all-weather, uninterrupted monitoring of wastewater discharge sites. This greatly improves the timeliness and accuracy of monitoring and provides strong data support for environmental management. However, single sensors are susceptible to environmental interference and have difficulty capturing the spatial distribution characteristics of pressure waves, resulting in insufficient accuracy in anomaly location. They also lack effective identification and filtering mechanisms for environmental interference, which can easily lead to false alarms. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an intelligent control system for the operation and maintenance of online wastewater monitoring equipment.

[0005] The technical solution of this invention to solve the above-mentioned technical problems is as follows: An intelligent control system for the operation and maintenance of an online wastewater monitoring device, comprising: a feature parameter calculation module: acquiring the time difference of pressure waves arriving at different sensors through pressure sensors, calculating the propagation delay of pressure waves in the pipeline, and determining whether there is an abnormality in the pipeline based on the magnitude and trend of the pressure wave amplitude change rate at adjacent time points; a fault diagnosis module: evaluating the abnormality of the pipeline by introducing comprehensive evaluation indicators to determine the possible faults of the equipment; a redundancy verification module: automatically activating the pressure sensor when the abnormal signal continuously exceeds a first preset threshold, cross-verifying the data obtained by the redundant sensor array with the original sensor data, and locating the faulty sensor location through comparative analysis; a fault level assessment module: comprehensively assessing the level of equipment fault based on the fault location result, combined with the intensity and duration of the abnormal signal; and an operation and maintenance instruction generation and execution module: coupling and analyzing the equipment fault level signal with wastewater flow parameters, and dynamically generating operation and maintenance instructions based on the coupling analysis results.

[0006] In a preferred embodiment, the feature parameter calculation module sets up several pressure sensors at different locations in the wastewater pipeline, assigns a unique identification identifier to each pressure sensor, and configures a sampling frequency. The pressure sensors record the dynamic changes of pressure waves. When a pressure wave propagates in the pipeline, the module records the time it takes for the pressure wave to reach each sensor. For any two adjacent pressure sensors, the module calculates the time difference between the pressure wave reaching sensor B and the time it reaches sensor A. Simultaneously measure the actual distance between sensor A and sensor B. Through formula Calculate the propagation speed of the pressure wave in the pipe. This allows us to obtain the propagation time delay of the pressure wave in the pipeline, calculated using the following formula: ,in, This represents the propagation time delay of the pressure wave in this section of the pipeline, and sets the lower limit of the propagation time delay of the pressure wave when the pipeline is operating normally. and upper limit The actual transmission delay Compared with the range of propagation delay, when and If an abnormal situation is detected in the pipeline, pressure data is collected from each pressure sensor, and the pressure wave amplitude data at adjacent time points is extracted. The pressure amplitude of sensor i at time t and t+1 is recorded as follows: and For each pressure sensor, the rate of change of pressure wave amplitude is calculated based on the following formula, the specific calculation formula is as follows: Where A represents the rate of change of pressure wave amplitude, we analyze the magnitude of the rate of change of pressure wave amplitude. A large rate of change of amplitude indicates that the pressure wave fluctuates more violently during that period. We observe the trend of the rate of change of amplitude, plot the curve of the rate of change of amplitude over time, and analyze its rising, falling and stabilizing trends.

[0007] In a preferred embodiment, the fault diagnosis module combines the analysis results of propagation delay and pressure wave amplitude change rate to comprehensively evaluate the pipeline's operating status, introducing a comprehensive evaluation index Z, the calculation formula of which is as follows: ,in, This represents the average value within the normal propagation delay range. This represents the normal average rate of change of pressure wave amplitude. and The weighting coefficients represent the propagation delay and the rate of change of pressure wave amplitude, respectively. The comprehensive evaluation index Z reflects the degree to which the pipeline's operating state deviates from the normal state. A large value of Z indicates a higher risk of pipeline abnormalities.

[0008] In a preferred embodiment, the redundancy verification module monitors the intensity and duration of abnormal signals of the comprehensive evaluation index in real time. When an abnormal signal of the comprehensive evaluation index first appears, a timing mechanism is activated to record the duration of the abnormal signal. The intensity of abnormal signals in the comprehensive evaluation index monitored in real time is compared with a first preset threshold. When the intensity of abnormal signals in the comprehensive evaluation index continues to be greater than the first preset threshold for an extended period of time, the abnormal signal is detected. When the preset time length is reached, the redundant sensor array activation command is triggered. If, during the timing process, the abnormal signal strength of the comprehensive evaluation index drops below the first preset threshold, the timing stops and monitoring restarts. The redundant sensor array is activated by sending a start signal to the distributed fiber optic pressure sensors distributed along the pipeline axis. After activation, the redundant sensor array begins to acquire three-dimensional pressure field distribution data within the pipeline at a set frequency and transmits the data to the data processing center. The three-dimensional pressure field distribution data acquired by the redundant sensor array is integrated with the data acquired by the original pressure sensors to ensure data timestamp consistency. The integrated data is preprocessed, including noise removal and data normalization. Data from the redundant sensors at the same location is compared with the original sensor data, and the difference in pressure values ​​at the same time is calculated. The specific calculation formula is as follows: ,in, This represents the difference in pressure values ​​at the same time. This indicates redundant sensor data. This represents the existing sensor data, and a permissible error range is set. When | |> If the sensor data at that location is inconsistent, it is considered that there may be a sensor malfunction. Based on the equipment topology, the connection and position relationships between the sensors with inconsistent data are analyzed. If the data of multiple adjacent redundant sensors on a section of pipeline are significantly different from the original sensor data, while the data in other areas are normal, it can be preliminarily determined that the fault is in the sensor on that pipeline.

[0009] In a preferred embodiment, the fault level assessment module considers the comprehensive assessment index of abnormal signal strength. Duration Fault sensor location We assign weights to each evaluation indicator, quantify the above indicators, and let the comprehensive evaluation indicator, the abnormal signal strength, be... The preset threshold is The intensity quantification score can be expressed as: ,in, , This represents the score for the corresponding interval. , Thresholds representing different intervals are used to quantize the duration, where the duration is... Duration quantification score It can be represented as: ,in, , This represents the score for the corresponding interval. , The threshold values ​​representing different intervals are used to quantify the location of fault sensors. The pipeline is divided into n regions, and their importance weights are defined as follows: When the fault sensor is located in region j, the quantitative evaluation index of the fault sensor location is... It can be represented as: ,in, This indicates an indicator function, when i=j, , indicating that the faulty sensor is located in region j, and based on the intensity quantification score, the specific formula for calculating the fault level score S is as follows: Set the fault level classification threshold as and The fault level is determined as follows: Based on the calculated fault level score, the fault level of the equipment is determined, and the corresponding equipment fault level signal is generated.

[0010] In a preferred embodiment, the operation and maintenance instruction generation and execution module obtains the equipment fault level from the fault level assessment module and wastewater flow parameters from the wastewater flow monitoring system, including real-time flow and flow change trend data. It then establishes an assessment model of the impact of different fault types on equipment operation and wastewater treatment under different wastewater flow conditions, considering the impact of wastewater flow changes on fault development. Based on the assessment model, it determines the degree of influence of different fault levels under different wastewater flow parameters. To quantify the degree of impact of faults on equipment operation and wastewater treatment under different flow conditions, a fault impact factor F is defined. Let the wastewater flow be Q and the equipment fault level be S. The fault impact factor F is calculated using the following formula: ,in, A function representing the flow rate Q, used to describe the weight of the flow rate's impact on faults. This function represents the fault level S, used to describe the basic impact of different fault levels. Based on the impact level, operation and maintenance strategies are formulated for different fault levels and operating conditions.

[0011] The beneficial effects of this invention are as follows: By calculating multiple characteristic parameters such as the rate of change of pressure wave amplitude and propagation delay, this invention analyzes pressure waves from different angles, enabling more comprehensive and accurate monitoring of the pipeline's condition and timely detection of anomalies. The redundant sensor array automatically activates when the abnormal pressure wave signal continuously exceeds the threshold, acquiring three-dimensional pressure field distribution data within the pipeline and cross-validating it with the original sensor data. This not only allows for more accurate location of faulty sensors but also a more comprehensive assessment of the type and severity of the fault, effectively improving the system's reliability and stability. Furthermore, by coupling and analyzing equipment fault level signals with wastewater flow parameters, this invention dynamically generates maintenance instructions based on different fault levels and operating conditions, making maintenance strategies and operational steps more targeted. This allows for the adoption of the most effective treatment measures based on actual conditions, improving equipment maintenance efficiency and wastewater treatment effectiveness. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention; Figure 2 This is a system block diagram of the present invention. Detailed Implementation

[0013] like Figure 1 This embodiment provides an intelligent control system for the operation and maintenance of an online wastewater monitoring device, including: a feature parameter calculation module: through a pressure sensor, it obtains the time difference of pressure waves arriving at different sensors, calculates the propagation delay of pressure waves in the pipeline, and determines whether there is an abnormality in the pipeline based on the magnitude and trend of the pressure wave amplitude change rate at adjacent time points.

[0014] In this embodiment, the feature parameter calculation module needs to be specifically described. This module sets up several pressure sensors at different locations in the wastewater pipeline, assigns a unique identification identifier to each pressure sensor, and configures a sampling frequency. The pressure sensors record the dynamic changes in pressure waves. When the pressure wave propagates in the pipeline, the module records the time it takes for the pressure wave to reach each sensor. For any two adjacent pressure sensors, the module calculates the time difference between the pressure wave reaching sensor B and the time it reaches sensor A. Simultaneously measure the actual distance between sensor A and sensor B. Through formula Calculate the propagation speed of the pressure wave in the pipe. This allows us to obtain the propagation time delay of the pressure wave in the pipeline, calculated using the following formula: ,in, This represents the propagation time delay of the pressure wave in this section of the pipeline, and sets the lower limit of the propagation time delay of the pressure wave when the pipeline is operating normally. and upper limit The actual transmission delay Compared with the range of propagation delay, when and If an abnormal situation is detected in the pipeline, pressure data is collected from each pressure sensor, and the pressure wave amplitude data at adjacent time points is extracted. The pressure amplitude of sensor i at time t and t+1 is recorded as follows: and For each pressure sensor, the rate of change of pressure wave amplitude is calculated based on the following formula, the specific calculation formula is as follows: Where A represents the rate of change of pressure wave amplitude. Analyze the magnitude of the rate of change of pressure wave amplitude. A large rate of change of amplitude indicates that the pressure wave fluctuates more violently during that period. Observe the trend of the rate of change of amplitude, draw the curve of the rate of change of amplitude over time, and analyze its rising, falling and stabilizing trends. For example, when the rate of change of amplitude continues to rise, it may mean that the pressure fluctuation in the pipeline is gradually intensifying.

[0015] It should be noted that pressure waves are mainly generated when wastewater flows through pipes. Changes in the flow velocity and direction disturb the fluid, causing pressure fluctuations that propagate as pressure waves propagate through the pipes. Simultaneously, the process of fluid intake and discharge generates periodic mechanical vibrations, which, when transmitted to the fluid, form pressure waves. Pressure sensors should be installed at critical nodes in the pipeline, such as elbows, tees, and diameter changes, where fluid conditions change significantly, pressure fluctuations are obvious, and anomalies are prone to occur. Installing sensors at these locations allows for timely detection of these changes. Secondly, pressure sensors should be installed evenly at regular intervals based on the pipeline length. The distance should be determined by the pipe material, diameter, and wastewater characteristics. Generally, one sensor should be installed every 50-100 meters on long straight pipelines to comprehensively monitor the pressure conditions of different pipe sections. Sensors should also be installed at high and low points in the pipeline. High points are prone to gas accumulation affecting pressure, while low points are subject to greater fluid impact; these installations help detect problems such as air blockage and blockages.

[0016] The intelligent operation and maintenance control system also includes a fault diagnosis module: by introducing comprehensive evaluation indicators, it assesses abnormal conditions of the pipeline and determines possible faults in the equipment.

[0017] In this embodiment, it is necessary to explain the fault diagnosis module. The fault diagnosis module combines the analysis results of propagation delay and pressure wave amplitude change rate to comprehensively evaluate the pipeline's operating status, and introduces a comprehensive evaluation index Z, the calculation formula of which is as follows: ,in, This represents the average value within the normal propagation delay range. This represents the normal average rate of change of pressure wave amplitude. and The weighting coefficients represent the propagation delay and the rate of change of pressure wave amplitude, respectively. The comprehensive evaluation index Z reflects the degree to which the pipeline's operating state deviates from the normal state. A large value of Z indicates a higher risk of pipeline abnormalities.

[0018] The intelligent operation and maintenance control system also includes a redundancy verification module: when an abnormal signal continuously exceeds the first preset threshold, the pressure sensor is automatically activated, and the data obtained by the redundant sensor array is cross-verified with the original sensor data. Through comparative analysis, the location of the faulty sensor is located.

[0019] In this embodiment, the redundancy verification module needs to be specifically described. This module monitors the intensity and duration of abnormal signals of the comprehensive evaluation index in real time. When an abnormal signal of the comprehensive evaluation index first appears, a timing mechanism is activated to record the duration of the abnormal signal. The intensity of abnormal signals in the comprehensive evaluation index monitored in real time is compared with a first preset threshold. When the intensity of abnormal signals in the comprehensive evaluation index continues to be greater than the first preset threshold for an extended period of time, the abnormal signal is detected. When the preset time length is reached, the redundant sensor array activation command is triggered. If, during the timing process, the abnormal signal strength of the comprehensive evaluation index drops below the first preset threshold, the timing stops and monitoring restarts. The redundant sensor array is activated by sending a start signal to the distributed fiber optic pressure sensors distributed along the pipeline axis. After activation, the redundant sensor array begins to acquire three-dimensional pressure field distribution data within the pipeline at a set frequency and transmits the data to the data processing center. The three-dimensional pressure field distribution data acquired by the redundant sensor array is integrated with the data acquired by the original pressure sensors to ensure data timestamp consistency. The integrated data is preprocessed, including noise removal and data normalization. Data from the redundant sensors at the same location is compared with the original sensor data, and the difference in pressure values ​​at the same time is calculated. The specific calculation formula is as follows: ,in, This represents the difference in pressure values ​​at the same time. This indicates redundant sensor data. This represents the existing sensor data, and a permissible error range is set. When | |> If the sensor data at that location is inconsistent, it is considered that there may be a sensor malfunction. Based on the equipment topology, the connection and position relationships between the sensors with inconsistent data are analyzed. If the data of multiple adjacent redundant sensors on a section of pipeline differs significantly from the original sensor data, while the data in other areas is normal, it can be preliminarily determined that the fault lies with the sensor on that pipeline. It should be noted that the first preset threshold is obtained through historical data statistical analysis. A large amount of comprehensive evaluation index data of the equipment under normal operating conditions is collected, including the values ​​of characteristic parameters such as pressure wave amplitude change rate, waveform distortion index, and propagation delay. By statistically analyzing these data, such as calculating the average value and standard deviation, a reasonable threshold range is determined. Generally, a certain quantile of normal data (such as the 95th or 99th quantile) can be used as the first preset threshold. This can ensure that the probability of the signal strength exceeding the threshold is low under normal operating conditions, thereby reducing false alarms.

[0020] It should be noted that redundant sensor arrays are installed upstream and downstream of valves. Valve opening and closing causes transient pressure changes, and redundant sensors can help determine the impact of valve failures on the pipeline pressure field. For pipe sections with long service life, aging materials, or frequent historical failures, redundant arrays can serve as key monitoring units, comparing data from the main sensor in real time to provide early warnings of potential leaks or structural damage. In long pipelines with large spacing between main sensors, redundant arrays can fill monitoring blind spots, shorten the error range for calculating pressure wave propagation time, and improve positioning accuracy. In cross-regional or concealed pipelines, redundant arrays can help determine internal anomalies, avoiding misjudgments due to single data from the main sensor. Pressure pulses from pump operation may interfere with the main sensor data. Redundant arrays can simultaneously monitor the three-dimensional distribution of the pressure field, separating pressure changes caused by equipment vibration and pipeline anomalies. During installation, redundant sensor arrays should be staggered from the main sensors to avoid overlapping monitoring ranges and prioritize coverage of blind spots in the main sensor's field of view, such as the middle area between the main sensors. Redundant sensor arrays are in a dormant state by default and are only automatically activated when the main sensor triggers an abnormal signal to reduce energy consumption and data processing load.

[0021] The intelligent operation and maintenance control system also includes a fault level assessment module: based on the fault location results, combined with the strength and duration of abnormal signals, it comprehensively assesses the level of equipment faults.

[0022] In this embodiment, the fault level assessment module needs to be specifically explained. This module considers the comprehensive evaluation index of abnormal signal strength. Duration Fault sensor location We assign weights to each evaluation indicator, quantify the above indicators, and let the comprehensive evaluation indicator, the abnormal signal strength, be... The preset threshold is The intensity quantification score can be expressed as: ,in, , This represents the score for the corresponding interval. , Thresholds representing different intervals are used to quantize the duration, where the duration is... Duration quantification score It can be represented as: ,in, , This represents the score for the corresponding interval. , The threshold values ​​representing different intervals are used to quantify the location of fault sensors. The pipeline is divided into n regions, and their importance weights are defined as follows: When the fault sensor is located in region j, the quantitative evaluation index of the fault sensor location is... It can be represented as: ,in, This indicates an indicator function, when i=j, , indicating that the faulty sensor is located in region j, and based on the intensity quantification score, the specific formula for calculating the fault level score S is as follows: Set the fault level classification threshold as and The fault level is determined as follows: Based on the calculated fault level score, the fault level of the equipment is determined, and the corresponding equipment fault level signal is generated.

[0023] The intelligent operation and maintenance control system also includes an operation and maintenance instruction generation and execution module: it couples and analyzes equipment fault level signals with wastewater flow parameters, and dynamically generates operation and maintenance instructions based on the results of the coupling analysis.

[0024] In this embodiment, the operation and maintenance instruction generation and execution module needs to be specifically described. This module obtains the equipment fault level from the fault level assessment module and wastewater flow parameters from the wastewater flow monitoring system, including real-time flow and flow change trend data. It establishes an assessment model of the impact of different fault types on equipment operation and wastewater treatment under different wastewater flow conditions, considering the impact of wastewater flow changes on fault development. Based on the assessment model, it determines the degree of influence of different fault levels under different wastewater flow parameters. To quantify the degree of impact of faults on equipment operation and wastewater treatment under different flow conditions, a fault impact factor F is defined. Let the wastewater flow be Q and the equipment fault level be S. The fault impact factor F is calculated using the following formula: ,in, A function representing the flow rate Q, used to describe the weight of the flow rate's impact on faults. This function represents the fault level S, used to describe the basic impact of different fault levels. Based on the impact level, different maintenance strategies are formulated for different fault levels and operating conditions. For example, for high-impact faults, emergency repair measures are taken, and professional technicians are given priority to carry out emergency repairs. For medium-impact faults, a regular maintenance plan is formulated to carry out equipment maintenance at appropriate time periods. For low-impact faults, maintenance can be arranged during equipment downtime in conjunction with the equipment operation plan. According to the maintenance strategy, the maintenance instructions should include instructions number, equipment name, fault description, maintenance strategy, detailed maintenance instructions, operation steps, estimated execution time, and required resources (such as maintenance personnel, maintenance materials, tools, etc.).

[0025] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0026] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0027] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0028] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0029] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0030] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0031] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent control system for the operation and maintenance of an online wastewater monitoring device, characterized in that, include: Feature parameter calculation module: By using pressure sensors, the time difference of pressure waves arriving at different sensors is obtained, the propagation delay of pressure waves in the pipeline is calculated, and the magnitude and trend of the pressure wave amplitude change rate at adjacent time points are used to determine whether there are any abnormalities in the pipeline. Fault diagnosis module: By introducing comprehensive evaluation indicators, the abnormal conditions of the pipeline are assessed to determine the possible faults of the equipment; Redundancy verification module: When the abnormal signal continues to exceed the first preset threshold, the pressure sensor is automatically activated. The data obtained by the redundant sensor array is cross-verified with the original sensor data. Through comparative analysis, the location of the faulty sensor is located. Fault Level Assessment Module: Based on the fault location results, combined with the intensity and duration of abnormal signals, the module comprehensively assesses the level of equipment fault. Operation and maintenance instruction generation and execution module: Couples and analyzes equipment fault level signals with wastewater flow parameters, and dynamically generates operation and maintenance instructions based on the results of the coupling analysis.

2. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 1, characterized in that, The characteristic parameter calculation module sets up several pressure sensors at different locations in the wastewater pipeline, assigning a unique identification identifier to each pressure sensor and configuring a sampling frequency. The pressure sensors record the dynamic changes in pressure waves. As the pressure wave propagates in the pipeline, the time it takes for the pressure wave to reach each sensor is recorded. For any two adjacent pressure sensors, the time difference between the arrival time of the pressure wave at sensor B and the arrival time at sensor A is calculated. Simultaneously measure the actual distance between sensor A and sensor B. Through formula Calculate the propagation speed of the pressure wave in the pipe. This allows us to obtain the propagation time delay of the pressure wave in the pipeline, calculated using the following formula: ,in, This indicates the propagation delay of the pressure wave in that section of the pipe.

3. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 2, characterized in that, Set a lower limit for the propagation time delay of pressure waves during normal pipeline operation. and upper limit The actual transmission delay Compared with the range of propagation delay, when and If an abnormal situation is detected in the pipeline, pressure data is collected from each pressure sensor, and the pressure wave amplitude data at adjacent time points is extracted. The pressure amplitude of sensor i at time t and t+1 is recorded as follows: and For each pressure sensor, the rate of change of pressure wave amplitude is calculated based on the following formula, the specific calculation formula is as follows: Where A represents the rate of change of pressure wave amplitude, we analyze the magnitude of the rate of change of pressure wave amplitude. A large rate of change of amplitude indicates that the pressure wave fluctuates more violently during that period. We observe the trend of the rate of change of amplitude, plot the curve of the rate of change of amplitude over time, and analyze its rising, falling and stabilizing trends.

4. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 1, characterized in that, The fault diagnosis module combines the analysis results of propagation delay and pressure wave amplitude change rate to comprehensively evaluate the pipeline's operating status, introducing a comprehensive evaluation index Z, the calculation formula of which is as follows: ,in, This represents the average value within the normal propagation delay range. This represents the normal average rate of change of pressure wave amplitude. and The weighting coefficients represent the propagation delay and the rate of change of pressure wave amplitude, respectively. The comprehensive evaluation index Z reflects the degree to which the pipeline's operating state deviates from the normal state. A large value of Z indicates a higher risk of pipeline abnormalities.

5. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 1, characterized in that, The redundancy verification module monitors the intensity and duration of abnormal signals of the comprehensive evaluation index in real time. When an abnormal signal of the comprehensive evaluation index first appears, a timing mechanism is started to record the duration of the abnormal signal. The intensity of abnormal signals in the comprehensive evaluation index monitored in real time is compared with a first preset threshold. When the intensity of abnormal signals in the comprehensive evaluation index continues to be greater than the first preset threshold for an extended period of time, the abnormal signal is detected. When the preset time length is reached, the redundant sensor array activation command is triggered. If, during the timing process, the signal strength of the comprehensive evaluation index drops below the first preset threshold, the timing is stopped and monitoring is restarted.

6. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 5, characterized in that, The redundant sensor array is activated by sending a start signal to the distributed fiber optic pressure sensors along the pipeline axis. Once activated, the redundant sensor array begins acquiring three-dimensional pressure field distribution data within the pipeline at a set frequency and transmits the data to the data processing center. The three-dimensional pressure field distribution data acquired by the redundant sensor array is integrated with the data acquired by the original pressure sensors, ensuring data timestamp consistency. The integrated data undergoes preprocessing, including noise removal and data normalization. Data from the redundant sensors at the same location is compared with the original sensor data, and the difference in pressure values ​​at the same time is calculated. The specific calculation formula is as follows: ,in, This represents the difference in pressure values ​​at the same time. This indicates redundant sensor data. This represents the existing sensor data, and a permissible error range is set. When | |> If the sensor data at that location is inconsistent, it is considered that there may be a sensor malfunction. Based on the equipment topology, the connection and position relationships between the sensors with inconsistent data are analyzed. If the data of multiple adjacent redundant sensors on a section of pipeline are significantly different from the original sensor data, while the data in other areas are normal, it can be preliminarily determined that the fault is in the sensor on that pipeline.

7. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 1, characterized in that, The fault level assessment module considers the comprehensive assessment indicators, including abnormal signal strength. Duration Fault sensor location We assign weights to each evaluation indicator, quantify the above indicators, and let the comprehensive evaluation indicator, the abnormal signal strength, be... The preset threshold is The intensity quantification score can be expressed as: ,in, , This represents the score for the corresponding interval. , Thresholds representing different intervals are used to quantize the duration, where the duration is... Duration quantification score It can be represented as: ,in, , This represents the score for the corresponding interval. , The threshold values ​​representing different intervals are used to quantify the location of fault sensors. The pipeline is divided into n regions, and their importance weights are defined as follows: When the fault sensor is located in region j, the quantitative evaluation index of the fault sensor location is... It can be represented as: ,in, This indicates an indicator function, when i=j, , indicating that the faulty sensor is in region j.

8. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 6, characterized in that, Based on the intensity quantification score, the specific formula for calculating the fault level score S is as follows: Set the fault level classification threshold as and The fault level is determined as follows: Based on the calculated fault level score, the fault level of the equipment is determined, and the corresponding equipment fault level signal is generated.

9. The intelligent control system for operation and maintenance of an online wastewater monitoring device according to claim 1, characterized in that, The operation and maintenance instruction generation and execution module obtains equipment fault levels from the fault level assessment module and wastewater flow parameters from the wastewater flow monitoring system, including real-time flow and flow change trend data. It establishes an assessment model of the impact of different fault types on equipment operation and wastewater treatment under different wastewater flow conditions, considering the influence of wastewater flow changes on fault development. Based on the assessment model, it determines the degree of influence of different fault levels under different wastewater flow parameters. To quantify the degree of impact of faults on equipment operation and wastewater treatment under different flow conditions, a fault impact factor F is defined. Let the wastewater flow be Q and the equipment fault level be S. The fault impact factor F is calculated using the following formula: ,in, A function representing the flow rate Q, used to describe the weight of the flow rate's impact on the fault. This function represents the fault level S, used to describe the basic impact of different fault levels. Based on the impact level, operation and maintenance strategies are formulated for different fault levels and operating conditions.

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