Internet of Things (IoT) Control Method and System for Decentralized Integrated Wastewater Treatment Facilities

Abstract

This invention relates to the field of wastewater treatment technology, specifically disclosing an Internet of Things (IoT) control method and system for decentralized integrated wastewater treatment facilities, comprising: an equipment parameter acquisition terminal for acquiring real-time operating parameters of the wastewater treatment equipment, including power parameters of each component and real-time processing load parameters; a sensor group for acquiring real-time status data of the wastewater treatment equipment, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component; and a control center for analyzing and obtaining the critical coefficient of the wastewater treatment equipment based on the real-time processing load parameters and historical processing data, and obtaining the state coefficient of the wastewater treatment equipment based on the power parameters of each component and real-time status data, and determining the equipment status and making corresponding control strategies based on the comparison between the state coefficient and the critical coefficient.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to an Internet of Things (IoT) control system for decentralized integrated wastewater treatment facilities. Background Technology

[0002] Decentralized integrated wastewater treatment facilities are a type of wastewater treatment used in rural areas, scenic spots, and other areas where centralized wastewater treatment is not suitable. They are equipped with independent treatment equipment for each wastewater source, enabling on-site treatment of wastewater. Integrated wastewater treatment equipment mainly uses multi-stage treatment, has automatic dosing and aeration functions, and is buried underground for easier use.

[0003] During operation, integrated wastewater treatment facilities are difficult to monitor individually due to their decentralized setup. Therefore, existing integrated wastewater treatment equipment is equipped with Internet of Things (IoT) modules to monitor their operating status. When an operational risk is detected, timely warnings are issued or corresponding control strategies are implemented to ensure the stable and normal operation of the wastewater treatment equipment.

[0004] Existing wastewater treatment equipment uses threshold warnings for relevant parameters during monitoring and control. When a parameter shows a significant abnormality, a corresponding abnormal command is issued. However, if the same threshold standard is used when the wastewater treatment equipment is in different states, the threshold setting range needs to be large. Obviously, this method results in poor monitoring sensitivity, which makes it difficult to detect problems in the wastewater treatment equipment in a timely manner. Furthermore, due to the distributed layout, adjustments to the equipment are quite inconvenient. Summary of the Invention

[0005] The purpose of this invention is to provide an Internet of Things (IoT) control system for decentralized integrated wastewater treatment facilities, addressing the following technical problems:

[0006] How to improve the sensitivity and timeliness of monitoring and control of decentralized integrated wastewater treatment facilities based on the Internet of Things.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] A decentralized integrated wastewater treatment facility IoT control system, the system comprising:

[0009] The equipment parameter acquisition terminal is used to obtain the real-time operating parameters of the sewage treatment equipment, including the power parameters of each component and the real-time processing load parameters.

[0010] The sensor array is used to collect real-time status data of the wastewater treatment equipment, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component.

[0011] Edge devices are used to connect to the equipment parameter acquisition terminal and sensor group to send the real-time operating parameters and real-time status data of the sewage treatment equipment to the control center.

[0012] The control center is used to obtain the critical coefficient of the sewage treatment equipment based on the real-time processing load parameters and historical processing data, and to obtain the state coefficient of the sewage treatment equipment based on the power parameters and real-time status data of each component. By comparing the state coefficient with the critical coefficient, the equipment status is determined and corresponding control strategies are implemented.

[0013] Furthermore, the historical processing data includes equipment usage data and historical power data of each component; the real-time processing load parameters include wastewater treatment volume and inlet wastewater monitoring data;

[0014] The process of obtaining the critical coefficient includes:

[0015] S1. Based on the current sewage treatment volume and inlet sewage monitoring data, obtain the basic critical coefficient for equipment operation;

[0016] S2. Based on the equipment usage data and the historical power data of each component, obtain the equipment loss coefficient. Adjust the basic critical coefficient of equipment operation using the equipment loss coefficient to obtain the critical coefficient.

[0017] Furthermore, the calculation process of the critical coefficient includes:

[0018] By formulas (1)-(3):

[0019] Cr=Ct*r (1)

[0020]

[0021]

[0022] The critical coefficient Cr was calculated.

[0023] Where Ct is the basic critical coefficient for equipment operation, r is the equipment loss coefficient; Q is the wastewater treatment volume, n is the monitoring item of the detection factor, i∈[1,n]; τ i Let τ0 be the monitoring value of the i-th detection factor. i For τ i The corresponding baseline value, f E For the lookup table function, γ i Let f be the correlation coefficient of the aeration treatment for the i-th detection factor, H be the correlation reference value, and f be the correlation coefficient for the ith detection factor. A Let τ be the first judgment function. i -τ0 i If f > 0, then fA (τ i -τ0 i )=τ i -τ0 i Otherwise, f A (τ i -τ0 i ) = 0; Ls is the time since the equipment started use, Lu is the cumulative operating time of the equipment, x is the parameter tuning coefficient, and x > 1; L0 is the standard life of the equipment, m is the number of times the equipment is operated, j∈[1,m]; t j ~t j+1 Let p(t) be the time period of the j-th operation, p(t) be the operating power of the equipment, and f be the operating power of the equipment. B Let be the second judgment function, if but otherwise, W0 is the critical operating value of the equipment; y1 and y2 are weighting coefficients.

[0024] Furthermore, the process of obtaining the state coefficients includes:

[0025] SS1. Compare the real-time monitored power parameters of each component with the standard power corresponding to the real-time processed load parameters to obtain the control deviation coefficient;

[0026] SS2. Determine the abnormal values ​​of the equipment based on the vibration data at each preset point, and determine the leakage risk value of the equipment based on the flow data at the inlet and outlet points of each component.

[0027] SS3. Determine the state coefficient of the equipment based on the control deviation coefficient, abnormal value, and leakage risk value of the equipment.

[0028] Furthermore, the calculation process of the state coefficients includes:

[0029] By formulas (4)-(7):

[0030] St=g*(u*μ+b) (4)

[0031]

[0032]

[0033]

[0034] The state coefficient St is calculated.

[0035] Where g is the control deviation coefficient, u is the outlier, b is the leakage risk value, μ is the parameter adjustment coefficient, t0 is the start time of the current treatment process, t is the current time, p(t) is the real-time power of the aerator, pc(t) is the control power, and p1 is the power error baseline value. To collect the mean amplitude at fixed time intervals, A0 is the standard amplitude value, and s1 is the variance of the amplitude at all time points. Let f0 be the mean frequency of the vibration curve, s2 be the standard frequency value, s2 be the variance of the mean frequency over a time interval collected at fixed time intervals, Z be the number of processing levels, and k ∈ [1, Z]. 为 The k-th stage processes the inbound flow, Qout k For the k-th stage processing outflow, f k Let be the loss function for the k-th level.

[0036] Furthermore, the process of determining the equipment status includes:

[0037] Compare the state coefficient St with the critical coefficient Cr:

[0038] If the state coefficient St is greater than or equal to the critical coefficient Cr, then an early warning command is issued.

[0039] Furthermore, the system also includes a correction module;

[0040] If g∈[g1, g2] and St<Cr, the correction module is used to correct and adjust the power control parameters of the component based on the difference between g and g1;

[0041] g1 is the first deviation threshold, and g2 is the second deviation threshold.

[0042] Furthermore, the correction and adjustment process includes:

[0043] By formulas (8)-(9):

[0044] pa(t)=pc(t)+Δp (8)

[0045]

[0046] Calculate the corrected control power pa(t), and control the equipment components according to the corrected control power pa(t);

[0047] Where Δp is the control power adjustment amount, σ1 and σ2 are weighting coefficients, and σ1+σ2<1; t1 is the end time of the previous processing, and max{p(t)-pc(t)) is the maximum value of p(t)-pc(t) in the time period from t0 to t1.

[0048] A method for controlling decentralized integrated wastewater treatment facilities using the Internet of Things (IoT), including:

[0049] Step 1: Obtain real-time operating parameters of the wastewater treatment equipment through the equipment parameter acquisition terminal, including the power parameters of each component and the real-time processing load parameters. Collect real-time status data of the wastewater treatment equipment through the sensor group, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component.

[0050] Step 2: By connecting the edge device to the equipment parameter acquisition terminal and sensor group, the real-time operating parameters and real-time status data of the sewage treatment equipment are sent to the control center. The control center obtains the critical coefficient of the sewage treatment equipment by analyzing the real-time treatment load parameters and historical treatment data of the sewage treatment equipment, and obtains the status coefficient of the sewage treatment equipment by collecting the power parameters and real-time status data of each component of the sewage treatment equipment. By comparing the status coefficient and the critical coefficient, the equipment status is determined and corresponding control strategies are implemented.

[0051] The beneficial effects of this invention are:

[0052] (1) The critical coefficient in this invention is obtained based on the analysis of the real-time processing load parameters and historical processing data of the equipment. Therefore, as a standard for judgment, it is adaptively set according to the real-time operating status and historical usage data of the equipment. Thus, its constraint on the calculated state coefficient is more accurate and sensitive, thereby improving the timeliness of monitoring and control of decentralized integrated sewage treatment facilities based on the Internet of Things. Attached Figure Description

[0053] The invention will now be further described with reference to the accompanying drawings.

[0054] Figure 1 This is a logic block diagram of the Internet of Things control system for the decentralized integrated wastewater treatment facility of the present invention;

[0055] Figure 2 This is a flowchart of the process for obtaining the critical coefficient in this invention;

[0056] Figure 3 This is a flowchart of the process for obtaining state coefficients in this invention;

[0057] Figure 4 This is a process flow diagram of the Internet of Things control system for the decentralized integrated wastewater treatment facility of the present invention. Detailed Implementation

[0058] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Please see Figure 1 As shown, in one embodiment, an IoT control system for a decentralized integrated wastewater treatment facility is provided. This system acquires relevant parameters of the wastewater treatment equipment through an equipment parameter acquisition terminal and a sensor array. Specifically, the equipment parameter acquisition terminal acquires real-time operating parameters of the wastewater treatment equipment, including power parameters of each component and real-time processing load parameters. The sensor array acquires real-time status data of the wastewater treatment equipment, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component. The power data of each component includes, but is not limited to, the power of the aerator and the power of the chlorine dioxide generator. The flow data at the inlet and outlet points of each component includes the inlet and outlet flow rates of each level of the treatment area. Typically, the wastewater treatment equipment has at least three treatment levels, but this is not limited here. Additionally, the diagnostic data at the preset points includes vibration data at the outer wall points of each level of the treatment area and vibration data at the locations of components such as the aerator and the chlorine dioxide generator, etc., which are not limited here. Therefore, based on the data from the equipment parameter acquisition terminal and sensor group, the real-time operating parameters and real-time status data of the sewage treatment equipment are sent to the control center through edge devices connecting to the equipment parameter acquisition terminal and sensor group. Then, the control center analyzes the real-time processing load parameters and historical processing data of the sewage treatment equipment to obtain the critical coefficient of the sewage treatment equipment, and obtains the status coefficient of the sewage treatment equipment by collecting the power parameters and real-time status data of each component of the sewage treatment equipment. By comparing the status coefficient and the critical coefficient, the equipment status is judged and corresponding control strategies are made. In this process, the critical coefficient is obtained based on the analysis of the equipment's real-time processing load parameters and historical processing data. Therefore, as a standard for judgment, it is adaptively set according to the equipment's real-time operating status and historical usage data. Thus, its constraint on the calculated status coefficient is more accurate and sensitive, thereby improving the timeliness of monitoring and control of decentralized integrated sewage treatment facilities based on the Internet of Things.

[0060] As one embodiment of the present invention, historical processing data includes equipment usage data and historical power data of each component; real-time processing load parameters include wastewater treatment volume and inlet wastewater monitoring data; please refer to... Figure 2 As shown, the process of obtaining the critical coefficient includes: S1, obtaining the basic critical coefficient for equipment operation based on the current sewage treatment volume and inlet sewage monitoring data; S2, obtaining the equipment loss coefficient based on equipment usage data and historical power data of each component, and adjusting the basic critical coefficient for equipment operation based on the equipment loss coefficient to obtain the critical coefficient. The calculation process of the critical coefficient includes:

[0061] By formulas (1)-(3):

[0062] Cr=Ct*r (1)

[0063]

[0064]

[0065] The critical coefficient Cr is calculated and determined based on a comprehensive assessment of the current operating pressure and historical wear and tear of the equipment. Here, Ct is the basic critical coefficient for equipment operation, and r is the equipment wear coefficient. The basic critical coefficient for equipment operation is determined based on the current wastewater treatment volume Q and the concentration of the detection factors initially monitored at the inlet, where n is the monitored detection factor, i∈[1,n]; τ i Let τ0 be the monitoring value of the i-th detection factor. i For τ i The corresponding baseline value, γ, is set based on empirical data. i Let f be the aeration treatment correlation coefficient for the i-th detection factor. This parameter is set according to the correlation between different detection factors and wastewater treatment. H is the correlation reference value, which is set based on empirical data. A Let τ be the first judgment function. i -τ0 i If fx(τ) > 0, then fx(τ) i -τ0 i )=τ i -τ0 i Otherwise, f A (τ i -τ0 i ) = 0; f E This is a lookup table function, derived from the quantity and corresponding relationships of massive datasets fitted within big data. Therefore, based on... The corresponding basic critical coefficient for equipment operation is obtained from the range of values; in addition, Ls is the time since the equipment started to be used, Lu is the cumulative operating time of the equipment, x is the parameter tuning coefficient, and x>1, which is obtained based on empirical data; L0 is the standard life of the equipment, m is the number of times the equipment is operated, j∈[1,m]; t j ~t j+1 Let p(t) be the time period of the j-th operation, p(t) be the operating power of the equipment, and f be the operating power of the equipment. B Let be the second judgment function, if but otherwise, W0 is the critical operating value of the equipment, which is preset according to the equipment specifications and serves as a reference value for judging the operating status of the equipment; y1 and y2 are weighting coefficients, which are set according to the degree of influence of different influencing factors in the empirical data. Therefore, through the calculation process of the critical coefficients in the above process, the actual state of the equipment can be used as a reference, thereby improving the sensitivity and timeliness of the judgment.

[0066] As one embodiment of the present invention, please refer to Figure 3 As shown, the process of obtaining the state coefficient includes: SS1, comparing the power parameters of each component monitored in real time with the standard power corresponding to the real-time processed load parameters to obtain the control deviation coefficient; SS2, judging the abnormal values ​​of the equipment based on the vibration data of each preset point, and judging the leakage risk value of the equipment based on the flow data of each component inlet and outlet point; SS3, determining the state coefficient of the equipment based on the control deviation coefficient, abnormal values, and leakage risk value. The calculation process of the state coefficient includes:

[0067] By formulas (4)-(7):

[0068] St=g*(u*μ+b) (4)

[0069]

[0070]

[0071]

[0072] The state coefficient St is calculated. The calculation model for the state coefficient is based on the control deviation coefficient and is established according to vibration data and leakage risk data monitored by IoT devices. Here, g is the control deviation coefficient, u is the outlier, b is the leakage risk value, μ is the parameter adjustment coefficient, which is adaptively set according to the different degrees of influencing factors in the empirical data, t0 is the start time of the current treatment process, t is the current time, p(t) is the real-time power of the aerator, pc(t) is the control power, and p1 is the power error baseline value. This indicates the current cumulative deviation, while the control deviation coefficient represents the ratio of power to the power error reference value per unit time; additionally... To collect the mean amplitude at fixed time intervals, A0 is the standard amplitude value, and s1 is the variance of the amplitude at all time points. Here, is the mean frequency of the vibration curve, f0 is the standard frequency value, and s2 is the variance of the mean frequency over a time interval. Therefore, when significant abnormalities occur in the vibration data of the wastewater treatment equipment during operation, such as abnormal amplitude, abnormal frequency, or uneven vibration, these abnormalities can be reflected by the acquired abnormal value u. Z represents the treatment level; in this embodiment, Z = 3, k ∈ [1, Z], Qin k For the k-th stage of processing inbound flow, Qout k For the k-th stage processing outflow, f kThe loss function for the k-th level is set according to the characteristics of different levels of the treatment process. Therefore, by calculating the leakage risk value, the leakage risk of the treatment equipment can be judged. Then, by calculating the state coefficient St, the state of the sewage treatment equipment can be measured. The equipment state judgment process includes: comparing the state coefficient St with the critical coefficient Cr. If the state coefficient St ≥ the critical coefficient Cr, an early warning command is issued, realizing a more timely monitoring and control process of the equipment state.

[0073] In one embodiment of the present invention, the system further includes a correction module; if g∈[g1, g2] and St<Cr, the correction module is used to correct and adjust the power control parameters of the component according to the difference between g and g1; wherein g1 is a first deviation threshold, g2 is a second deviation threshold, and the correction and adjustment process includes:

[0074] By formulas (8)-(9):

[0075] pa(t)=pc(t)+Δp (8)

[0076]

[0077] The corrected control power pa(t) is calculated and adjusted, and the equipment components are controlled according to the corrected control power pa(t). Here, Δp is the control power adjustment amount, σ1 and σ2 are weighting coefficients, and σ1+σ2<1, which are obtained by fitting the test data of each component; t1 is the end time of the last treatment process, and max{p(t)-pc(t)) is the maximum value of p(t)-pc(t) in the time period from t0 to t1. Therefore, when it is determined that the control power of each component is within a reasonable deviation, the above correction and adjustment process can ensure the effectiveness of each component of the sewage treatment equipment through the process of obtaining the control power, thereby ensuring the overall effective and stable operation of the equipment.

[0078] In one embodiment, an IoT control method for a decentralized integrated wastewater treatment facility is provided; please refer to [link / reference]. Figure 4As shown, the process includes: Step 1: Acquiring real-time operating parameters of the wastewater treatment equipment through the equipment parameter acquisition terminal, including power parameters of each component and real-time processing load parameters; collecting real-time status data of the wastewater treatment equipment through the sensor group, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component; Step 2: Sending the real-time operating parameters and real-time status data of the wastewater treatment equipment to the control center through the edge device interface with the equipment parameter acquisition terminal and the sensor group; The control center obtains the critical coefficient of the wastewater treatment equipment based on the real-time processing load parameters and historical processing data, and obtains the state coefficient of the wastewater treatment equipment based on the power parameters of each component and the real-time status data; Based on the comparison between the state coefficient and the critical coefficient, the equipment status is determined and a corresponding control strategy is implemented.

[0079] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An Internet of Things (IoT) control system for decentralized integrated wastewater treatment facilities, characterized in that: The system includes: The equipment parameter acquisition terminal is used to obtain the real-time operating parameters of the sewage treatment equipment, including the power parameters of each component and the real-time processing load parameters. The sensor array is used to collect real-time status data of the wastewater treatment equipment, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component. Edge devices are used to connect to the equipment parameter acquisition terminal and sensor group to send the real-time operating parameters and real-time status data of the sewage treatment equipment to the control center. The control center is used to obtain the critical coefficient of the sewage treatment equipment by analyzing the real-time treatment load parameters and historical treatment data of the sewage treatment equipment, and to obtain the state coefficient of the sewage treatment equipment by taking the power parameters and real-time status data of each component of the sewage treatment equipment. By comparing the state coefficient and the critical coefficient, the equipment status is judged and corresponding control strategies are made. The historical processing data includes equipment usage data and historical power data of each component; the real-time processing load parameters include wastewater treatment volume and inlet wastewater monitoring data. The process of obtaining the critical coefficient includes: S1. Based on the current sewage treatment volume and inlet sewage monitoring data, obtain the basic critical coefficient for equipment operation; S2. Based on the equipment usage data and the historical power data of each component, obtain the equipment loss coefficient. Adjust the basic critical coefficient of equipment operation using the equipment loss coefficient to obtain the critical coefficient. The calculation process of the critical coefficient includes: By formulas (1)-(3): （1）； （2）； （3）； The critical coefficient Cr was calculated. Where Ct is the basic critical coefficient for equipment operation, r is the equipment loss coefficient; Q is the sewage treatment volume, n is the monitoring item of the detection factor, i∈[1,n]; Let i be the monitoring value of the i-th detection factor. for The corresponding baseline value, This is a lookup table function. Let H be the correlation coefficient of the aeration treatment for the i-th detection factor, and let H be the correlation reference value. Let be the first judgment function, if ,but ,otherwise, Ls is the time since the equipment was first put into use, Lu is the cumulative operating time of the equipment, x is the parameter tuning coefficient, and x>1; L0 is the standard life of the equipment, m is the number of times the equipment is operated, j∈[1,m]; For the j-th run, For equipment operating power, For the second judgment function, if ,but ,otherwise, W0 is the critical operating value of the equipment; y1 and y2 are weighting coefficients. The process of obtaining the state coefficients includes: SS1. Compare the real-time monitored power parameters of each component with the standard power corresponding to the real-time processed load parameters to obtain the control deviation coefficient; SS2. Determine the abnormal values ​​of the equipment based on the vibration data at each preset point, and determine the leakage risk value of the equipment based on the flow data at the inlet and outlet points of each component. SS3. Determine the equipment's state coefficient based on the equipment's control deviation coefficient, abnormal values, and leakage risk values; The calculation process of the state coefficients includes: By formulas (4)-(7): （4）； （5）； （6）； （7）； The state coefficient St is calculated. Where g is the control deviation coefficient, u is the outlier, and b is the leakage risk value. Here, t0 is the starting time of the current treatment process, t is the current time, p(t) is the real-time power of the aerator, pc(t) is the control power, and p1 is the power error baseline value. To collect the mean amplitude at fixed time intervals, A0 is the standard amplitude value, and s1 is the variance of the amplitude at all time points. Let f0 be the mean frequency of the vibration curve, f0 be the standard frequency value, s2 be the variance of the mean frequency over a time interval collected at fixed time intervals, Z be the number of processing levels, and k∈[1, Z]. For the k-th stage, handle the inflow. For the k-th stage, process the outflow. Let k be the loss function for the kth level; The process of determining the equipment status includes: Compare the state coefficient St with the critical coefficient Cr: If the state coefficient St is greater than or equal to the critical coefficient Cr, then an early warning command is issued.

2. The decentralized integrated wastewater treatment facility Internet of Things control system according to claim 1, characterized in that, The system also includes a correction module; If g∈[g1, g2] and St<Cr, the correction module is used to correct and adjust the power control parameters of the component based on the difference between g and g1; g1 is the first deviation threshold, and g2 is the second deviation threshold.

3. The decentralized integrated wastewater treatment facility Internet of Things control system according to claim 2, characterized in that, The correction and adjustment process includes: By formulas (8)-(9): （8）； （9）； Calculate the corrected control power pa(t), and control the equipment components according to the corrected control power pa(t); in, To control the power adjustment amount, , These are weighting coefficients, and t1 is the time point at which the previous processing ended. The time period is t0~t1 The maximum value.

4. An Internet of Things (IoT) control method for decentralized integrated wastewater treatment facilities, characterized in that: The method employs the decentralized integrated wastewater treatment facility Internet of Things control system as described in any one of claims 1-3, comprising: Step 1: Obtain real-time operating parameters of the wastewater treatment equipment through the equipment parameter acquisition terminal, including the power parameters of each component and the real-time processing load parameters. Collect real-time status data of the wastewater treatment equipment through the sensor group, including vibration data at each preset point of the wastewater treatment equipment and flow data at the inlet and outlet points of each component. Step 2: By connecting the edge device to the equipment parameter acquisition terminal and sensor group, the real-time operating parameters and real-time status data of the sewage treatment equipment are sent to the control center. The control center obtains the critical coefficient of the sewage treatment equipment by analyzing the real-time treatment load parameters and historical treatment data of the sewage treatment equipment, and obtains the status coefficient of the sewage treatment equipment by collecting the power parameters and real-time status data of each component of the sewage treatment equipment. By comparing the status coefficient and the critical coefficient, the equipment status is determined and corresponding control strategies are implemented.

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