An environmental protection equipment intelligent management method based on internet of things big data fusion

Abstract

The application discloses an environmental protection equipment intelligent management method based on Internet of Things big data fusion, and relates to the technical field of environmental protection equipment intelligent management.The application significantly improves the control quality of an aeration tank under load impact through an Internet of Things data deep fusion mechanism; a time sequence prediction layer fuses near-infrared estimated values and spatial correlation data to construct a dissolved oxygen demand sequence that is ahead of actual pollution changes, and overcomes response lag caused by traditional sensor detection delay; a frequency domain feature extraction module captures high-frequency energy distribution from equipment vibration signals, and reflects aeration efficiency changes in real time to provide physical state basis for a compensation mechanism; a dynamic compensation design breaks through the limitation of a fixed threshold value, dynamically adjusts a time constant according to a flow gradient, and makes the aeration amount adjustment rate and actual impact strength adaptively match, and simultaneously calibrates a sludge concentration linkage threshold value to avoid false triggering under a low-activity sludge working condition.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology for environmental protection equipment, and in particular to an intelligent management method for environmental protection equipment that integrates the Internet of Things and big data. Background Technology

[0002] In urban and industrial wastewater treatment systems, aeration tanks are the core unit of biochemical treatment, and the precise control of dissolved oxygen concentration directly affects the organic matter degradation efficiency and energy consumption level. Currently, mainstream plants have deployed Internet of Things (IoT) sensor networks to collect parameters such as dissolved oxygen, influent flow rate, and sludge concentration in real time and connect them to a central intelligent management platform. Such systems typically use predictive models or adaptive control algorithms based on historical data to achieve dynamic optimization of aeration volume.

[0003] When faced with torrential rain or a sudden influx of high-concentration wastewater, the drastic fluctuations in influent load cause sudden changes in dissolved oxygen demand. Existing intelligent management solutions face a double constraint: on the one hand, water quality parameter sensors have inherent detection delays, such as requiring periodic sampling for online COD analysis, making it impossible to capture key pollution indicators in real time; on the other hand, most control models rely on steady-state condition training and have weak pattern recognition capabilities for transient shocks. This leads to a lag in aeration volume adjustment compared to actual needs, resulting in uncontrolled phenomena such as over-aeration or insufficient oxygen supply.

[0004] Some solutions improve response speed by integrating LSTM time series prediction with reinforcement learning frameworks, such as cross-domain correlation between influent flow sequence and fan current signal to predict dissolved oxygen change trend; or by introducing transfer learning mechanism to adapt to the operating characteristics of different plant areas; however, such solutions are still limited by the inescapable physical sensing delay, and model training requires accumulating several months of abnormal operating condition data; in newly built plant areas lacking prior knowledge or in extreme impact scenarios, there is still a risk of adjustment overshoot and sharp increase in energy consumption. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] This invention provides an intelligent management method for environmental protection equipment that integrates the Internet of Things and big data. This method solves the problem that existing solutions suffer from delayed aeration control when the influent load changes abruptly due to sensor delays and limitations of steady-state models, leading to a surge in energy consumption and fluctuations in water quality.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] This invention provides a method for intelligent management of environmental protection equipment based on the integration of Internet of Things and big data, comprising:

[0009] Step S1, Constructing the time-series prediction layer: Based on the time-series data of influent flow rate, sludge concentration and blower current under historical load shock conditions, generate a dissolved oxygen demand prediction sequence.

[0010] The generation of the dissolved oxygen demand prediction sequence includes:

[0011] An LSTM-attention mechanism model is established, with the input dimension being a standardized vector of influent flow rate, sludge concentration, and blower current within a preset time window;

[0012] The output layer uses a temporal convolutional network to compress the feature dimensions and generate a probability distribution of dissolved oxygen demand for future periods.

[0013] Step S2, Deploy the frequency domain feature extraction module: Real-time acquisition of vibration signals from the aeration equipment, and extraction of frequency domain energy distribution features through multi-scale wavelet transform;

[0014] The operation of the multi-scale wavelet transform includes: using the Daubechies wavelet basis function to decompose the vibration signal into 5 levels, extracting the energy entropy of the detail coefficients of the 3rd-4th level as the equipment state feature quantity, and updating the energy distribution map through the sliding window mechanism;

[0015] Step S3, establish a dynamic compensation mechanism: when the instantaneous change rate of the dissolved oxygen demand prediction sequence exceeds a set threshold, activate the advance adjustment algorithm to generate an aeration compensation command.

[0016] Step S4, execute closed-loop control: superimpose the compensation command onto the basic aeration volume setting value, and drive the variable frequency fan to adjust the output.

[0017] As a preferred embodiment of the intelligent management method for environmental protection equipment based on IoT big data fusion described in this invention, the input data for the time series prediction layer in step S1 includes:

[0018] The soft measurement estimate of influent chemical oxygen demand (COD) is output 60 seconds in advance using a near-infrared spectroscopy analysis model.

[0019] Spatial correlation of dissolved oxygen concentration between adjacent aeration units.

[0020] As a preferred embodiment of the intelligent management method for environmental protection equipment integrating Internet of Things and big data as described in this invention, the operation of the frequency domain feature extraction module in step S2 includes:

[0021] Perform a 128-point Fast Fourier Transform on the vibration signal;

[0022] The energy percentage in the 800-1200Hz frequency band was selected as a characteristic quantity of the equipment status.

[0023] As a preferred embodiment of the intelligent management method for environmental protection equipment based on IoT big data fusion described in this invention, the dynamic compensation mechanism in step S3 specifically comprises:

[0024] The advanced regulation algorithm calculates the compensation amount according to an exponential decay function, and its time constant is related to the gradient of the influent flow rate change.

[0025] The activation threshold for the compensation command is dynamically adjusted based on the sludge concentration.

[0026] As a preferred embodiment of the intelligent management method for environmental protection equipment integrating Internet of Things and big data as described in this invention, in step S3, during the calculation of the dynamic compensation amount and the adaptive adjustment threshold, the compensation amount is... With activation threshold Synchronous calculation and immediate closed-loop activation of the variable frequency fan upon detection of a load surge, the specific steps include:

[0027] a) Calculate the gradient of influent flow rate change and construct the time constant:

[0028] When the inflow rate is monitored When a transition occurs, the flow gradient is calculated using the following formula:

[0029] ,

[0030] in, for Constant water inflow rate, in units of , The control period is expressed in minutes. This represents the flow gradient, in units of... ;

[0031] Then set the time constant in the form of exponential decay. :

[0032] ,

[0033] in, and These are the preset upper and lower time constants, in minutes. Use the gradient as a reference.

[0034] b) Calculate the rate of change in dissolved oxygen demand :

[0035] The predicted dissolved oxygen demand output by the LSTM-attention mechanism model is obtained by difference:

[0036] ,

[0037] in, For the predicted instantaneous dissolved oxygen demand, This represents the dissolved oxygen demand prediction output by the LSTM-attention mechanism model in the previous control cycle, in mg. , Units are ;

[0038] c) Adaptively adjust the activation threshold based on sludge concentration:

[0039] Real-time sludge concentration Trigger activation threshold Downward adjustment:

[0040] ,

[0041] in, The basic threshold, in units of , Sludge concentration, unit , The threshold inflection point,

[0042] In the formula, the linear down-adjustment coefficient is:

[0043] ,

[0044] in, This indicates a downward adjustment factor. The minimum threshold allowed by the system. To design the maximum sludge concentration;

[0045] d) Calculate the exponential decay compensation:

[0046] when Time-activated compensation, compensation amount for:

[0047] ,

[0048] in, To compensate for the magnification factor, This is a sign function used to determine the direction of rising / falling aeration. This indicates rising demand;

[0049] e) Perform threshold adaptive calibration:

[0050] like continued Then re-estimate the base threshold:

[0051] ,

[0052] in, The updated base threshold, Indicates the threshold for triggering traffic gradients. This represents the minimum duration window required to trigger adaptive threshold calibration, in minutes. For memory factors, For the past Inside The average value, Indicates the average time window. This represents the memory factor, which is dimensionless and takes a value between 0 and 1.

[0053] Overlay compensation command:

[0054] ,

[0055] in, The basic aeration rate setpoint, in units of , This is the final instruction after superimposed compensation.

[0056] As a preferred embodiment of the intelligent management method for environmental protection equipment based on IoT big data fusion described in this invention, it further includes:

[0057] After step S4, real-time dissolved oxygen concentration data is collected, and sensor measurement errors are separated by an adversarial feature decoupling algorithm to correct the time-series prediction layer parameters.

[0058] As a preferred embodiment of the intelligent management method for environmental protection equipment based on IoT big data fusion described in this invention, the step of separating sensor measurement errors using an adversarial feature decoupling algorithm includes:

[0059] Constructing the residual sequence: ,

[0060] in, The dissolved oxygen sensor reading at time t is given in units of t. , The residual between model predictions and measurements, in units of ;

[0061] The generator estimates the sensor error:

[0062] ,

[0063] ,

[0064] in, For auxiliary feature vectors such as temperature and pressure on the sensor side, the dimension is... , This is the concatenated input vector. For parameters Generator network, The estimated sensor error is expressed in units of... ;

[0065] Adversarial training loss:

[0066] ,

[0067] ,

[0068] in, For the discriminator network, parameters , , These are the instantaneous losses of the discriminator and the generator, respectively. The consistency penalty coefficient is dimensionless. and A set of trainable parameters, dimensionless;

[0069] Error removal and concentration correction:

[0070] ,

[0071] in, This is the corrected dissolved oxygen concentration, in units of... ;

[0072] Incremental update of parameters in the time series prediction layer:

[0073] ,

[0074] in, This is the updated set of current parameters for the time series prediction layer. This is the current parameter set for the time series prediction layer. The online learning rate is dimensionless.

[0075] As a preferred embodiment of the intelligent management method for environmental protection equipment that integrates Internet of Things and big data as described in this invention, the method realizes data interaction through an industrial Internet of Things gateway, encapsulates vibration signals and control commands using the OPCUA protocol, and implements a lightweight encrypted tunnel at the transmission layer.

[0076] As a preferred embodiment of the intelligent management method for environmental protection equipment that integrates Internet of Things and big data as described in this invention, when multiple aeration units simultaneously activate the compensation mechanism, they share feature parameters through a federated learning framework to generate a collaborative optimization instruction set.

[0077] As a preferred embodiment of the intelligent management method for environmental protection equipment based on IoT big data fusion described in this invention, the feature parameters shared by the federated learning framework include:

[0078] The LSTM hidden layer state vector of each edge node;

[0079] Gradient histogram of frequency domain energy distribution characteristics;

[0080] Activation frequency statistics of dynamic compensation commands;

[0081] The parameter aggregation uses a weighted average algorithm, with the weights determined by the entropy value of the node data quality. This entropy value is calculated based on the node data packet loss rate and signal-to-noise ratio.

[0082] The beneficial effects of this invention are as follows: This invention significantly improves the control quality of aeration tanks under load shocks through a deep fusion mechanism of IoT data; the time-series prediction layer integrates near-infrared prediction values ​​and spatial correlation data to construct a dissolved oxygen demand sequence that anticipates actual pollution changes, overcoming the response lag caused by traditional sensor detection delays; the frequency domain feature extraction module captures high-frequency energy distribution from equipment vibration signals, reflecting aeration efficiency changes in real time and providing a physical state basis for the compensation mechanism; the dynamic compensation design breaks through the limitations of fixed thresholds, dynamically adjusting the time constant with the flow gradient to adaptively match the aeration rate adjustment rate with the actual impact intensity, while avoiding false triggering under low-activity sludge conditions through sludge concentration-linked threshold calibration; the error decoupling module uses generative adversarial structures to separate inherent sensor biases, eliminating the impact of multi-source interference on the control closed loop and ensuring the stability of the prediction model in long-term operation; the federated learning framework enables secure sharing of feature parameters among multiple aeration units, allowing local load shocks to trigger global collaborative optimization and suppressing regional over-aeration.

[0083] The lightweight encrypted transmission of the industrial IoT gateway in this invention ensures secure data interaction and is compatible with mainstream field device protocols. It deeply integrates data sensing, edge computing, and collaborative decision-making, reducing the need for manual intervention while enhancing the system's autonomous decision-making ability to cope with sudden pollution loads, thus providing technical support for energy conservation and consumption reduction in wastewater treatment plants. Attached Figure Description

[0084] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 This is a flowchart illustrating an intelligent management method for environmental protection equipment that integrates IoT and big data, as shown in Example 1. Detailed Implementation

[0086] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0087] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0088] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0089] Example 1, referring to Figure 1 This embodiment provides a method for intelligent management of environmental protection equipment based on the integration of Internet of Things and big data, including:

[0090] Step S1, Constructing the time-series prediction layer: Based on the time-series data of influent flow rate, sludge concentration and blower current under historical load shock conditions, generate a dissolved oxygen demand prediction sequence.

[0091] The generation of the dissolved oxygen demand forecast sequence includes:

[0092] An LSTM-attention mechanism model is established, with the input dimension being a standardized vector of influent flow rate, sludge concentration, and blower current within a preset time window;

[0093] The output layer uses a temporal convolutional network to compress the feature dimensions and generate a probability distribution of dissolved oxygen demand for future periods.

[0094] The input data for the time series prediction layer in step S1 includes:

[0095] The soft measurement estimate of influent chemical oxygen demand (COD) is output 60 seconds in advance using a near-infrared spectroscopy analysis model.

[0096] Spatial correlation of dissolved oxygen concentration between adjacent aeration units;

[0097] Step S2, Deploy the frequency domain feature extraction module: Real-time acquisition of vibration signals from the aeration equipment, and extraction of frequency domain energy distribution features through multi-scale wavelet transform;

[0098] The multi-scale wavelet transform operation includes: using the Daubechies wavelet basis function to decompose the vibration signal into 5 levels, extracting the energy entropy of the detail coefficients of the 3rd-4th level as the equipment state feature quantity, and updating the energy distribution map through the sliding window mechanism;

[0099] The operations of the frequency domain feature extraction module in step S2 include:

[0100] Perform a 128-point Fast Fourier Transform on the vibration signal;

[0101] The energy percentage in the 800-1200Hz frequency band was selected as a characteristic quantity of the equipment status.

[0102] Step S3, establish a dynamic compensation mechanism: when the instantaneous change rate of the dissolved oxygen demand forecast sequence exceeds the set threshold, activate the advance adjustment algorithm to generate an aeration compensation command.

[0103] The dynamic compensation mechanism in step S3 is as follows:

[0104] The advance regulation algorithm calculates the compensation amount according to an exponential decay function, and its time constant is related to the gradient of the influent flow rate change.

[0105] The activation threshold for the compensation command is dynamically adjusted based on the sludge concentration.

[0106] The dynamic adjustment rule for the activation threshold is as follows:

[0107] The baseline threshold is set at 1.8 times the average rate of change under historical operating conditions;

[0108] When the sludge concentration exceeds 3000 mg / L, the threshold is linearly lowered according to the concentration increment;

[0109] When the influent flow rate gradient exceeds 10 m³ / min² for 3 consecutive minutes, threshold adaptive calibration is triggered.

[0110] In step S3, during the calculation of the dynamic compensation amount and the adaptive adjustment threshold, the compensation amount is... With activation threshold Synchronous calculation and immediate closed-loop activation of the variable frequency fan upon detection of a load surge, the specific steps include:

[0111] a) Calculate the gradient of influent flow rate change and construct the time constant:

[0112] When the inflow rate is monitored When a transition occurs, the flow gradient is calculated using the following formula:

[0113] ,

[0114] in, for Constant water inflow rate, in units of , The control period is expressed in minutes. This represents the flow gradient, in units of... ;

[0115] Then set the time constant in the form of exponential decay. :

[0116] ,

[0117] in, and These are the preset upper and lower time constants, in minutes. for The reference gradient; a larger one make Approaching rapidly This improves response speed;

[0118] b) Calculate the rate of change in dissolved oxygen demand. :

[0119] The predicted dissolved oxygen demand output by the LSTM-attention mechanism model is obtained by difference:

[0120] ,

[0121] in, For the predicted instantaneous dissolved oxygen demand, This represents the dissolved oxygen demand prediction output by the LSTM-attention mechanism model in the previous control cycle, in mg. , Units are ;

[0122] c) Adaptively adjust the activation threshold based on sludge concentration:

[0123] Real-time sludge concentration Trigger activation threshold Downward adjustment:

[0124] ,

[0125] in, The basic threshold, in units of , Sludge concentration, unit: , The threshold inflection point,

[0126] In the formula, the linear down-adjustment coefficient is:

[0127] ,

[0128] in, This indicates a downward adjustment factor. To set the minimum threshold allowed by the system, take... , To design the maximum sludge concentration;

[0129] d) Calculate the exponential decay compensation:

[0130] when Time-activated compensation, compensation amount for:

[0131] ,

[0132] in, To compensate for the magnification factor, , This is a sign function used to determine the direction of rising / falling aeration;

[0133] e) Perform threshold adaptive calibration:

[0134] like continued If min, then re-estimate the base threshold:

[0135] ,

[0136] in, The updated base threshold, Indicates the threshold for triggering traffic gradients. This represents the minimum duration window required to trigger adaptive threshold calibration, in minutes. For memory factors, For the past Inside The average value, Indicates the average time window. This represents the memory factor, which is dimensionless and takes a value between 0 and 1.

[0137] Overlay compensation command:

[0138] ,

[0139] in, The basic aeration rate setpoint, in units of , This is the final instruction after superimposed compensation;

[0140] Specifically, the flow gradient is used to dynamically compress the time constant, enabling the system to exhibit a rapid and controllable exponential response to influent impacts. The threshold is linearly adjusted by sludge concentration to synchronize with the breathing demand during high-load periods, reducing false triggering. The compensation amount uses a decay function to weaken high-frequency oscillations and maintain a stable air-to-water ratio. When drastic flow fluctuations persist, the basic threshold is adaptively calibrated and updated to prevent control lag. The final superimposed aeration command is both rapid and smooth, quickly compressing dissolved oxygen deviations to the set range, significantly reducing energy consumption and improving effluent quality.

[0141] Step S4, execute closed-loop control: superimpose the compensation command onto the basic aeration volume setting value, and drive the variable frequency fan to adjust the output;

[0142] After step S4, real-time dissolved oxygen concentration data is collected, and sensor measurement errors are separated by an adversarial feature decoupling algorithm to correct the time-series prediction layer parameters.

[0143] The steps for separating sensor measurement errors using an adversarial feature decoupling algorithm include:

[0144] Constructing the residual sequence: ,

[0145] in, The dissolved oxygen sensor reading at time t is given in units of t. , The residual between model predictions and measurements, in units of ;

[0146] The generator estimates the sensor error:

[0147] ,

[0148] ,

[0149] in, For auxiliary feature vectors such as temperature and pressure on the sensor side, the dimension is... , This is the concatenated input vector. For parameters Generator network, The estimated sensor error is expressed in units of... ;

[0150] Adversarial training loss:

[0151] ,

[0152] ,

[0153] in, For the discriminator network, parameters , , These are the instantaneous losses of the discriminator and the generator, respectively. The consistency penalty coefficient is dimensionless. and A set of trainable parameters, dimensionless;

[0154] Error removal and concentration correction:

[0155] ,

[0156] in, This is the corrected dissolved oxygen concentration, in units of... ;

[0157] Incremental update of parameters in the time series prediction layer:

[0158] ,

[0159] in, This is the updated set of current parameters for the time series prediction layer. This is the current parameter set for the time series prediction layer. The online learning rate is dimensionless.

[0160] Specifically, adversarial feature decoupling with residuals Using real samples, the inherent error distribution of the sensor is learned through a game between the generator and the discriminator; the generator learns the consistency term. Under constraints, the residuals are approximated to avoid overcompensation. The discriminator continuously improves its ability to distinguish between true residuals and spoofing errors, aligning the two. Estimated errors are immediately eliminated, forming a correction concentration. The sequence replaces the original measurement value in the gradient update, enabling the time series model weights to dynamically and adaptively converge towards the true dissolved oxygen. The entire process enhances the robustness of the model to non-process factors such as sensor drift, sludge adhesion, and electrochemical decay, maintains the stability of the predictive control closed loop, further reduces energy consumption, and improves effluent indicators.

[0161] This method enables data interaction through an industrial IoT gateway, encapsulates vibration signals and control commands using the OPCUA protocol, and implements a lightweight encrypted tunnel at the transport layer.

[0162] When multiple aeration units activate the compensation mechanism simultaneously, they share feature parameters through a federated learning framework to generate a collaborative optimization instruction set.

[0163] The feature parameters shared by federated learning frameworks include:

[0164] The LSTM hidden layer state vector of each edge node;

[0165] Gradient histogram of frequency domain energy distribution characteristics;

[0166] Activation frequency statistics of dynamic compensation commands;

[0167] The parameter aggregation uses a weighted average algorithm, with the weights determined by the entropy value of the node data quality.

[0168] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A smart management method for environmental protection equipment integrating Internet of Things and big data, applied to the aeration tank of a sewage treatment plant, characterized in that, Includes the following steps: Step S1, Constructing the time-series prediction layer: Based on the time-series data of influent flow rate, sludge concentration and blower current under historical load shock conditions, generate a dissolved oxygen demand prediction sequence. The generation of the dissolved oxygen demand prediction sequence includes: An LSTM-attention mechanism model is established, with the input dimension being a standardized vector of influent flow rate, sludge concentration, and blower current within a preset time window; The output layer uses a temporal convolutional network to compress the feature dimensions and generate a probability distribution of dissolved oxygen demand for future periods. Step S2, Deploy the frequency domain feature extraction module: Real-time acquisition of vibration signals from the aeration equipment, and extraction of frequency domain energy distribution features through multi-scale wavelet transform; The operation of the multi-scale wavelet transform includes: using the Daubechies wavelet basis function to decompose the vibration signal into 5 levels, extracting the energy entropy of the detail coefficients of the 3rd-4th level as the equipment state feature quantity, and updating the energy distribution map through the sliding window mechanism; Step S3, establish a dynamic compensation mechanism: when the instantaneous change rate of the dissolved oxygen demand prediction sequence exceeds a set threshold, activate the advance adjustment algorithm to generate an aeration compensation command. Step S4, execute closed-loop control: superimpose the compensation command onto the basic aeration volume setting value, and drive the variable frequency fan to adjust the output; After step S4, real-time dissolved oxygen concentration data is collected, and sensor measurement errors are separated by an adversarial feature decoupling algorithm to correct the parameters of the time-series prediction layer. The step of separating sensor measurement errors using the adversarial feature decoupling algorithm includes: Constructing the residual sequence: , in, The dissolved oxygen sensor reading at time t is given in units of t. , The residual between model predictions and measurements, in units of ; The generator estimates the sensor error: ， ， in, For the sensor-side temperature and pressure auxiliary feature vectors, the dimensions are... , This is the concatenated input vector. For parameters Generator network, The estimated sensor error is expressed in units of... ; Adversarial training loss: ， ， in, For the discriminator network, parameters , , These are the instantaneous losses of the discriminator and the generator, respectively. The consistency penalty coefficient is dimensionless. and A set of trainable parameters, dimensionless; Error removal and concentration correction: ， in, This is the corrected dissolved oxygen concentration, in units of... ; Incremental update of parameters in the time series prediction layer: ， in, For the updated set of current parameters for the time series prediction layer, This is the current parameter set for the time series prediction layer. The online learning rate is dimensionless.

2. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 1, characterized in that, The input data for the time series prediction layer in step S1 includes: The soft measurement estimate of influent chemical oxygen demand (COD) is output 60 seconds in advance using a near-infrared spectroscopy analysis model. Spatial correlation of dissolved oxygen concentration between adjacent aeration units.

3. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 1, characterized in that, The operations of the frequency domain feature extraction module in step S2 include: Perform a 128-point Fast Fourier Transform on the vibration signal; The energy percentage in the 800-1200Hz frequency band was selected as a characteristic quantity of the equipment status.

4. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 1, characterized in that, The dynamic compensation mechanism in step S3 is as follows: The advanced regulation algorithm calculates the compensation amount according to an exponential decay function, and its time constant is related to the gradient of the influent flow rate change. The activation threshold for the compensation command is dynamically adjusted based on the sludge concentration.

5. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 4, characterized in that, In step S3, during the calculation of the dynamic compensation amount and the adaptive adjustment threshold, the compensation amount is... With activation threshold Synchronous calculation and immediate closed-loop activation of the variable frequency fan upon detection of a load surge, the specific steps include: a) Calculate the gradient of influent flow rate change and construct the time constant: When the inflow rate is monitored When a transition occurs, the flow gradient is calculated using the following formula: ， in, for Constant water inflow rate, in units of , The control period is expressed in minutes. This represents the flow gradient, in units of... ; Then set the time constant in the form of exponential decay. : ， in, and These are the preset upper and lower time constants, in minutes. Use the gradient as a reference. b) Calculate the rate of change in dissolved oxygen demand. : The predicted dissolved oxygen demand output by the LSTM-attention mechanism model is obtained by difference: ， in, For the predicted instantaneous dissolved oxygen demand, This represents the dissolved oxygen demand prediction output by the LSTM-attention mechanism model in the previous control cycle, in mg. , Units are ; c) Adaptively adjust the activation threshold based on sludge concentration: Real-time sludge concentration Trigger activation threshold Downward adjustment: ， in, The basic threshold, in units of , Sludge concentration, unit: , The threshold inflection point, In the formula, the linear down-adjustment coefficient is: ， in, This indicates a downward adjustment factor. The minimum threshold allowed by the system. To design the maximum sludge concentration; d) Calculate the exponential decay compensation: when Time-activated compensation, compensation amount for: ， in, To compensate for the magnification factor, This is a sign function used to determine the direction of rising / falling aeration. This indicates rising demand; e) Perform threshold adaptive calibration: like continued Then re-estimate the base threshold: ， in, The updated base threshold, Indicates the threshold for triggering traffic gradients. This represents the minimum duration window required to trigger adaptive threshold calibration, in minutes. For memory factors, For the past Inside The average value, Indicates the average time window. This represents the memory factor, which is dimensionless and takes a value between 0 and 1. Overlay compensation command: ， in, The basic aeration rate setpoint, in units of , This is the final instruction after superimposed compensation.

6. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 1, characterized in that, The method achieves data interaction through an industrial IoT gateway, encapsulates vibration signals and control commands using the OPCUA protocol, and implements a lightweight encrypted tunnel at the transmission layer.

7. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 1, characterized in that, When multiple aeration units activate the compensation mechanism simultaneously, they share feature parameters through a federated learning framework to generate a collaborative optimization instruction set.

8. The intelligent management method for environmental protection equipment based on IoT big data integration as described in claim 7, characterized in that, The feature parameters shared by the federated learning framework include: The LSTM hidden layer state vector of each edge node; Gradient histogram of frequency domain energy distribution characteristics; Activation frequency statistics of dynamic compensation commands; The parameter aggregation uses a weighted average algorithm, with the weights determined by the entropy value of the node data quality. This entropy value is calculated based on the node data packet loss rate and signal-to-noise ratio.

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