Artificial intelligence-based carbon management method and system for wastewater treatment plant

Abstract

The application discloses a sewage treatment plant carbon control method and system based on artificial intelligence, which comprises the following steps: constructing a carbon emission prediction model according to collected real-time monitoring data, historical data and a mechanism model; generating a parameter optimization trigger instruction based on processing load data and water condition change rate output by the carbon emission prediction model, and then generating a process unit operation parameter configuration scheme based on water condition; obtaining a first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme, and establishing a dynamic parameter control strategy library; matching real-time water condition fluctuation data with the dynamic parameter control strategy library and the mechanism model, determining a real-time parameter control strategy, and executing a device parameter adjustment instruction according to the real-time parameter control strategy. The application solves the problem that current sewage treatment plants cannot predict carbon emission under different water conditions in real time, and achieves the effect of real-time prediction of carbon emission under different water conditions and reduction of energy loss.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of sewage treatment, and in particular to a sewage treatment plant carbon control method and system based on artificial intelligence. BACKGROUND

[0002] As an important part of urban infrastructure, sewage treatment is not only an important source of carbon emissions in ensuring water environment safety, but also directly affects the realization process of the national carbon peak and carbon neutralization targets.

[0003] With increasingly stringent environmental protection requirements and increasing carbon emission reduction pressure, carbon emission control of sewage treatment plants has become a key issue in environmental governance. At present, the carbon emission control of sewage treatment plants mainly relies on artificial experience and fixed parameter setting, which is difficult to respond to the dynamic changes of water treatment capacity in real time, and since a static operation mode is adopted, relevant technical personnel cannot flexibly adjust equipment parameters according to actual treatment load, which not only causes energy waste, but also leads to a continuously high carbon emission. In order to solve the above problems, relevant technical personnel usually use a post hoc statistical analysis method to collect and organize historical operation data, calculate key indicators such as energy consumption carbon emission, and then evaluate the carbon emission reduction effect based on the statistical results.

[0004] However, this passive management mode of "first emission, then statistics" has obvious hysteresis, and sewage treatment plants are difficult to develop corresponding parameter adjustment strategies in advance and can only passively respond to changes in water quantity and quality. SUMMARY

[0005] The embodiment of the application provides a sewage treatment plant carbon control method based on artificial intelligence, which solves the problem that the sewage treatment plant cannot predict the carbon emission under different water conditions in real time, realizes real-time prediction of the carbon emission under different water conditions, reduces unnecessary energy consumption, and reduces the carbon emission during sewage treatment.

[0006] The embodiment of the application provides a sewage treatment plant carbon control method based on artificial intelligence, which comprises:

[0007] A carbon emission prediction model is constructed according to collected real-time monitoring data, historical data and a mechanism model, wherein the real-time monitoring data comprises water condition fluctuation data and process unit operation state data; the water condition fluctuation data comprises water quantity fluctuation data and water quality fluctuation data; the historical data comprises a historical water condition fluctuation sequence, historical process unit operation parameters and historical carbon emission;

[0008] A parameter optimization trigger instruction is generated based on the treatment load data and the water condition change rate output by the carbon emission prediction model, and a process unit operation parameter configuration scheme based on the water condition is generated according to the parameter optimization trigger instruction.

[0009] obtaining a first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme, and establishing a dynamic parameter control strategy library;

[0010] matching the real-time water quality fluctuation data with the dynamic parameter control strategy library and the mechanism model to determine a real-time parameter control strategy, and executing a device parameter adjustment instruction according to the real-time parameter control strategy.

[0011] Optionally, the step of generating a parameter optimization trigger instruction based on the processing load data and the water quality change rate output by the carbon emission prediction model, and generating a process unit operation parameter configuration scheme based on a water quality condition according to the parameter optimization trigger instruction comprises:

[0012] if the carbon emission value in the processing load data exceeds a preset carbon emission threshold value, and the water quality change rate exceeds a preset fluctuation amplitude, the parameter optimization trigger instruction is generated;

[0013] starting a genetic algorithm program according to the parameter optimization trigger instruction, obtaining a running parameter boundary value of the process unit, and initializing a population individual matrix of the genetic algorithm according to the running parameter boundary value, the population individual matrix comprising a device parameter combination of aeration quantity, reflux ratio and sludge concentration;

[0014] evaluating a second carbon emission prediction value and a processing efficiency index of the device parameter combination based on the water quality condition, and generating a fitness score;

[0015] if the fitness score does not meet an expected condition, performing a crossover and mutation operation on the device parameter combination, re-evaluating the device parameter combination after the crossover and mutation, and updating the fitness score;

[0016] when the fitness score meets the expected condition, outputting the device parameter combination corresponding to the expected condition, and generating the process unit operation parameter configuration scheme.

[0017] Optionally, the step of constructing a carbon emission prediction model according to the collected real-time monitoring data, historical data and mechanism model comprises:

[0018] taking the historical water quality fluctuation sequence as an input feature vector and the historical carbon emission sequence as a target output value to generate a training data set, wherein each training sample comprises water quality data and a corresponding single carbon emission value of a preset time length;

[0019] calculating a loss function value between a predicted carbon emission value and an actual carbon emission value based on the training data set by a back propagation algorithm;

[0020] Adjust the network parameter configuration according to the loss function value, and evaluate the prediction accuracy of the network parameter configuration to determine a first weight parameter;

[0021] Receive the real-time monitoring data according to the first weight parameter, construct a prediction input vector, and generate the carbon emission prediction model in combination with the mechanism model.

[0022] Optionally, the step of obtaining the first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme and establishing a dynamic parameter regulation strategy library comprises:

[0023] If the difference between the first carbon emission prediction value and the preset carbon emission threshold value is less than a critical range, calculate the carbon emission threshold correlation degree of the process unit operation parameter configuration scheme;

[0024] If the carbon emission threshold correlation degree exceeds a preset correlation reference value, assign an emergency regulation index identifier, and generate a strategy classification result of a hierarchical index structure;

[0025] Establish a storage structure of the dynamic parameter regulation strategy library through the strategy classification result, and construct a strategy retrieval mechanism according to the emergency regulation index identifier to determine the calling weight of the process unit operation parameter configuration scheme regulation scheme;

[0026] Generate the dynamic parameter regulation strategy library of hierarchical management according to the calling weight.

[0027] Optionally, the step of matching the real-time water condition fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy, and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy comprises:

[0028] Obtain a fluctuation feature vector of the water condition fluctuation data, perform similarity matching of the fluctuation feature vector with the dynamic parameter regulation strategy library and the mechanism model, and extract a parameter regulation strategy record according to the matching result;

[0029] Perform carbon emission prediction calculation on the device operation parameters in the parameter regulation strategy record, and if the predicted carbon emission value is less than a preset safety threshold value, extract a device parameter adjustment instruction from the parameter regulation strategy record to determine the real-time parameter regulation strategy;

[0030] Determine a target device control interface address according to the parameter type identifier in the real-time parameter regulation strategy, and send the device parameter adjustment instruction to the target device.

[0031] Optionally, after the step of matching the real-time water quality fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy, the method further comprises:

[0032] In the execution of the device parameter adjustment instruction, the inflow water quality data, the effluent water quality data and the key node water quality data of the sewage plant are obtained; wherein the water quality data in the inflow water quality data and the effluent water quality data include but are not limited to PH / T, SS, TP, TN, COD and the data of the key nodes include but are not limited to the nodes of the biological reaction tank and the high-speed sedimentation tank; the key node water quality data include but are not limited to the data of DO, ORP, MLSS, TP, TN, COD, ;

[0033] The inflow water quality data is a feedforward signal of the system, and the effluent water quality data and the key node data are feedback signals of the system;

[0034] If the difference between the water quality data and the standard process exceeds the preset water quality deviation range, the water quality data is converted into an input variable of a fuzzy control algorithm, and a fuzzified value is output;

[0035] According to the fuzzified value, a corresponding inference rule is matched in a pre-established fuzzy rule library; if the number of the matched inference rules exceeds a single rule threshold, the inference rules are weighted calculated through a fuzzy inference mechanism, and the order of the device parameter adjustment instruction is adjusted.

[0036] Optionally, after the step of matching the real-time water quality fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy, the method further comprises:

[0037] After the execution of the device parameter adjustment instruction, actual carbon emission data is obtained; if the actual carbon emission data is abnormal and the number of continuous abnormal times is equal to an abnormality determination threshold, a carbon emission prediction model parameter updating program is started;

[0038] According to the carbon emission prediction model parameter updating program, the type of the parameter of the first prediction module that needs to be corrected is determined;

[0039] According to the type of the parameter, the connection weight and the bias parameter in the first prediction module are updated to generate a second prediction module;

[0040] The verification error based on the second prediction module is obtained, and it is judged whether the verification error is abnormal; if not, the carbon emission prediction model is updated according to the second prediction module.

[0041] Optionally, the step of updating the carbon emission prediction model based on the second prediction module further comprises:

[0042] acquiring process unit operation state data applying the updated carbon emission prediction model;

[0043] comparing the process unit operation state data with historical data to determine energy consumption level and emission trend curve of the process unit;

[0044] if it is detected that the emission trend curve of the process unit presents an upward state for three consecutive times, retrieving the corresponding aeration amount adjustment scheme and reflux ratio control scheme from the dynamic parameter regulation strategy library to obtain process parameter setting values of the process unit;

[0045] acquiring a dissolved oxygen concentration fluctuation value based on the process parameter setting values, and determining a carbon control state according to the dissolved oxygen concentration fluctuation value;

[0046] if the carbon control state meets a preset carbon control condition, keeping the process parameter setting values of the process unit unchanged.

[0047] In addition, to achieve the above-mentioned purpose, the embodiment of the present application also provides a sewage treatment plant carbon control system based on artificial intelligence, comprising:

[0048] a model construction module for constructing a carbon emission prediction model according to collected real-time monitoring data, historical data and a mechanism model, wherein the real-time monitoring data includes water condition fluctuation data and process unit operation state data; the water condition fluctuation data includes water quantity fluctuation data and water quality fluctuation data; the historical data includes historical water condition fluctuation sequence, historical process unit operation parameters and historical carbon emission;

[0049] an instruction generation module for generating a parameter optimization trigger instruction based on processing load data and water condition change rate output by the carbon emission prediction model, and generating a process unit operation parameter configuration scheme based on water condition according to the parameter optimization trigger instruction;

[0050] a strategy construction module for acquiring a first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme, and establishing a dynamic parameter regulation strategy library;

[0051] an instruction execution module for matching real-time water condition fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy, and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy.

[0052] In addition, to achieve the above object, the embodiment of the present application also provides a computer readable storage medium, which stores an artificial intelligence based sewage treatment plant carbon control program, and the artificial intelligence based sewage treatment plant carbon control program is executed by a processor to realize the method as described above.

[0053] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0054] (1) The carbon emission prediction model is constructed according to the collected real-time monitoring data, historical water condition fluctuation sequence, historical process unit operation parameters, historical carbon emission and mechanism model, wherein the real-time monitoring data includes water condition fluctuation data and process unit operation state data. The carbon emission of the sewage treatment plant is usually estimated by a static model, which cannot dynamically reflect the influence of water condition and water quality fluctuation on carbon emission, and the artificial experience adjustment has a lag, which is difficult to match the real-time working condition change. By integrating the real-time data such as water condition fluctuation data, process unit operation parameters and carbon emission, the carbon emission prediction model is constructed by machine learning or mechanism model, the treatment load is associated with the water condition change rate, the instantaneous change of carbon emission is quantified, and compared with the static model, the minute-level prediction of carbon emission can be realized.

[0055] (2) The parameter optimization trigger instruction is generated based on the treatment load data and the water condition change rate output by the carbon emission prediction model, and the process unit operation parameter configuration scheme based on the water condition is generated according to the parameter optimization trigger instruction. The process parameters are usually fixed at the design value and cannot adapt to the water condition fluctuation, resulting in waste of energy consumption. Based on the load data output by the prediction model, the invention automatically generates adjustment instructions, pre-trains the optimal parameter combination of different water condition intervals through the fuzzy rule base, which can effectively reduce the energy consumption and avoid sludge expansion or water exceeding standard caused by parameter mismatch of each process unit.

[0056] (3) The first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme is obtained, and a dynamic parameter control strategy library is established. That is, based on the first carbon emission prediction value, a multi-objective optimization scheme is generated, and the strategy update period is shortened from "day level" to "minute level".

[0057] (4) The real-time water condition fluctuation data is matched with the dynamic parameter control strategy library to determine the real-time parameter control strategy, and the equipment parameter adjustment instruction is executed according to the real-time parameter control strategy. The real-time strategy is converted into an equipment control signal and is sent to the field equipment, so as to adjust the process unit parameters according to the real-time water condition and realize carbon control. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1Flowchart of the first embodiment of the sewage treatment plant carbon control method based on artificial intelligence of the present application;

[0059] Figure 2 Flowchart of the second embodiment of the sewage treatment plant carbon control method based on artificial intelligence of the present application;

[0060] Figure 3 Flowchart of the third embodiment of the sewage treatment plant carbon control method based on artificial intelligence of the present application;

[0061] Figure 4 Terminal structure diagram of the hardware running environment involved in an embodiment of the present application. DETAILED DESCRIPTION

[0062] To solve the problem of low energy efficiency and uncontrollable carbon emissions of sewage treatment plants due to water level fluctuations, the present application constructs a dynamic carbon emission prediction model through historical water level fluctuation data, historical process unit operating parameters, historical carbon emissions, mechanism models, and real-time monitoring data. Based on the treatment load and water level change rate output by the model, a parameter optimization trigger instruction is generated, and then a process unit operating parameter configuration scheme matching the water level condition is formed. By evaluating the carbon emission prediction values of different configuration schemes, a dynamic parameter control strategy library is established, and finally the optimal strategy is matched combined with real-time water level fluctuation data, and the device parameter adjustment instruction is automatically executed. The accuracy and response speed of process control are improved, and the effect of adjusting carbon emissions according to different water level conditions and reducing energy consumption is achieved.

[0063] In order to better understand the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0064] In order to better understand the above technical solutions, the above technical solutions will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0065] Embodiment one

[0066] In this embodiment, a sewage treatment plant carbon control method based on artificial intelligence is provided.

[0067] With reference to Figure 1 The sewage treatment plant carbon control method based on artificial intelligence of the present embodiment includes the following steps:

[0068] Step S100: Construct a carbon emission prediction model according to the collected real-time monitoring data, historical data, and mechanism model;

[0069] In this embodiment, a multi-sensor data acquisition system is used to obtain real-time water condition fluctuation data, operation state data of each process unit, and historical data of the sewage treatment plant. The water condition fluctuation data includes water quantity fluctuation data and water quality fluctuation data; the historical data includes historical water condition fluctuation sequence, historical process unit operation parameters, and historical carbon emission, the historical data and real-time monitoring data are preprocessed to generate corresponding standardized data sets, then a long short-term memory neural network model is constructed according to the standardized data sets, the network weight parameters are trained through the correlation between the historical water condition fluctuation and the carbon emission, a nonlinear mapping relationship between the water condition change and the carbon emission trend is established, and a trained carbon emission prediction model is obtained. The water condition data and process unit operation parameters at the current time are used as the input of the carbon emission prediction model. This prediction method based on deep learning can accurately identify the influence mode of water condition fluctuation on carbon emission, greatly improve the prediction accuracy of carbon emission, and provide reliable data support for energy saving and emission reduction decision of the sewage treatment plant.

[0070] As an optional implementation, the real-time monitoring data is preprocessed to generate a multi-dimensional time series data set. The multi-dimensional time series data set contains four dimensions of time stamp, standardized water condition fluctuation value, process state value and carbon emission concentration value. Arranging the real-time monitoring data into a multi-dimensional time series data set can effectively improve the data quality, provide a reliable data foundation for subsequent intelligent analysis and prediction model, and significantly improve the monitoring accuracy and decision efficiency of the sewage treatment process.

[0071] Exemplarily, the multi-sensor data acquisition interface simultaneously receives real-time data of the flow sensor, process state monitor and carbon emission detector of the sewage treatment plant. The sensor precision calibration module corrects the original data by a preset calibration coefficient, for example, the calibration coefficient of the flow sensor is 0.98, and the original reading 15.2 cubic meters per hour is corrected to 14.896 cubic meters per hour, ensuring the data accuracy. Then the calibrated real-time monitoring data is preprocessed by a preset data cleaning rule, the data cleaning rule includes removing obvious error values, processing data format inconsistencies, etc. Different dimensional monitoring data can be converted into a unified standard format by a standardization parameter conversion module, for example, water condition data is converted from cubic meters per hour to standardized value 0.76, process unit state is converted from running state code to standardized value 1.0, and carbon emission concentration is converted from milligrams per cubic meter to standardized value 0.43, forming a unified time series data dimension matrix, i.e. a standardized data set.

[0072] Optionally, after generating the time series data dimension matrix, abnormal data detection can be performed. The time series data dimension matrix is scanned row by row using an outlier threshold detection algorithm, and upper and lower threshold values are set. When a data point is detected to be outside the normal range, it is marked as an outlier. For example, when a value deviating significantly from the normal range appears in the carbon emission concentration data sequence, a cubic spline interpolation method is used to calculate a reasonable replacement value using the normal data points before and after it, to determine the complete data set after repair. The complete data set is checked according to a processing data integrity verification program to ensure the continuity and reliability of the data.

[0073] It should be noted that the processing data integrity verification program evaluates data quality by checking indicators such as data missing rate and outlier proportion. When the data missing rate in a certain time period exceeds 5%, the system will trigger a data quality warning to ensure the accuracy of subsequent analysis.

[0074] Optionally, after integrity checking of the complete data set, a timestamp alignment algorithm can be used to synchronize and sort the data collected by different sensors according to a unified time reference, to obtain a standardized multi-dimensional time series data set. For example, a flow sensor collects data every 10 seconds, while a carbon emission detector collects data every 30 seconds. An interpolation method is used to unify all data to a 10-second time reference, achieving data synchronization.

[0075] It should be noted that the collected historical water level fluctuation sequence, historical process unit operating parameters and historical carbon emission amount are also preprocessed in the same way to generate a multi-dimensional time series data set corresponding to the historical data, i.e. a standardized data set.

[0076] As another optional implementation, the historical water level fluctuation sequence is used as an input feature vector, and the historical carbon emission amount sequence is used as a target output value, to generate a training data set, wherein each training sample contains water level data of a preset time length and a corresponding single carbon emission value.

[0077] Exemplarily, a historical water quality fluctuation sequence is extracted, a sliding window method is used to take the historical water quality fluctuation data of a continuous time period as an input feature vector, and the carbon emission at the corresponding time point of the water quality fluctuation data is taken as a target output value to obtain a complete training data set. The sliding window method constructs training samples by setting a fixed time window length, for example, 24 hours is selected as the window size, the water quality fluctuation data collected every 10 minutes within the continuous 24 hours is used to form a 144-dimensional input feature vector, and the historical carbon emission at the end time point of the time period is used as a label value. For example, when processing the historical data of a certain sewage treatment plant for one week, the first sample contains the water quality data of the first to 144 time points, and the carbon emission at the 145th time point is predicted, the second sample contains the data of the second to 145 time points, and the carbon emission at the 146th time point is predicted, and through this sliding method, a complete matrix containing thousands of training samples is finally generated, that is, a training data set.

[0078] As another optional implementation, after generating the training data set, a loss function value between the predicted carbon emission and the actual carbon emission is calculated based on the training data set by a back propagation algorithm.

[0079] Exemplarily, based on the training data set, a loss function value between the predicted carbon emission and the actual carbon emission is calculated by a back propagation algorithm, and if the loss function value does not significantly decrease for a plurality of batches, the learning rate parameter is adjusted to determine the optimized network parameter configuration. The back propagation algorithm uses mean square error as an evaluation index when calculating the loss function, and when the loss value of 20 consecutive batches changes by less than 0.001, the learning rate is adjusted from the initial value 0.01 to 0.005, ensuring that the network parameters can continue to be optimized. For example, during a certain training process, the loss function value gradually decreases from the initial 2.45 to 0.32 and then stagnates, at which time the learning rate decay mechanism is triggered to enable the training process to jump out of the local optimal solution and continue to converge.

[0080] As another optional implementation, the network parameter configuration is evaluated for prediction accuracy to determine a first weight parameter, real-time monitoring data is received according to the first weight parameter to construct a prediction input vector, and a carbon emission prediction model is generated in combination with a mechanism model.

[0081] Exemplarily, forward propagation calculation can be performed on the network parameter configuration, the prediction accuracy is evaluated by using the mean square error index, the early stopping mechanism is triggered to terminate the training process if the verification error continues to increase, the network training completion state is judged and the optimal weight parameter is saved, the real-time water condition data collected at the current time and the state operation parameters of each process unit are received according to the optimal weight parameter, the prediction input vector is constructed, the forward calculation is performed through the trained long short-term memory neural network, the final carbon emission trend prediction output is obtained, and the construction of the carbon emission prediction model is completed. The early stopping mechanism prevents overfitting by monitoring the prediction error on the verification set. When the verification error continues to increase for 15 consecutive training periods, the system immediately terminates the training and saves the current optimal network weight parameter as the first weight parameter. For example, in a certain training, the verification error reaches the minimum value of 0.28 at the 180th period, and then starts to rise. The system triggers the early stopping mechanism at the 195th period, avoiding further deterioration of the model performance.

[0082] After obtaining the first weight parameter, the real-time monitored water condition data is integrated based on the first weight parameter obtained by training to form a prediction input vector with time sequence correlation. The vector not only contains conventional water quantity, water quality and other monitoring indicators, but also integrates the theoretical carbon emission benchmark value calculated by the mechanism model and its physical constraint boundary. Then the composite vector is input into the prediction network, and the original prediction result output by the neural network is bidirectionally checked with the theoretical value generated by the mechanism model: on the one hand, the prediction value that obviously exceeds the abnormal value is corrected by the preset physical rule, such as forcing the negative emission to be zero; on the other hand, a dynamic weighting algorithm is used to fuse the theoretical value of the mechanism model and the data-driven prediction result, and finally a carbon emission prediction model is generated which not only conforms to the physical law but also adapts to the actual working condition. The whole process is continuously optimized through real-time feedback mechanism, and when the deviation between the monitoring data and the prediction result continuously exceeds the theoretical fluctuation range given by the mechanism model, the online adjustment of the model parameters is automatically triggered, ensuring that the prediction system always operates within the physically credible range.

[0083] Optionally, the hidden layer of the long short-term memory neural network contains 128 neural units, which can effectively capture the long-term dependence relationship between water condition fluctuation and carbon emission. In actual prediction of carbon emission, the water condition data sequence of the previous 24 hours at the current time can be received, and the 256-dimensional prediction input vector is constructed by combining the dissolved oxygen concentration of the aeration tank, the sludge return ratio and other process parameters. The trained network model is used for forward calculation to obtain the carbon emission trend prediction value in the next 2 hours.

[0084] Step S200: generating a parameter optimization trigger instruction based on the processing load data and the water condition change rate output by the carbon emission prediction model, and generating a process unit operation parameter configuration scheme based on the water condition according to the parameter optimization trigger instruction;

[0085] In this embodiment, real-time monitoring data is used as the input vector of the carbon emission prediction model, which can be used to predict the carbon emission under the processing load condition of the future period. If the predicted carbon emission exceeds the preset carbon emission threshold and the water condition change rate exceeds the baseline change range, the parameter optimization program is triggered, and the genetic algorithm is used to search for the optimal device parameter combination to obtain the optimal operation parameter configuration scheme of each process unit under different water conditions. This adaptive parameter optimization mechanism can dynamically adjust the process operation strategy according to the actual water condition fluctuation, effectively balance the treatment effect and carbon emission control target, and provide intelligent decision support for precise operation and management of wastewater treatment plants.

[0086] As an optional implementation, the processing load data and water condition fluctuation information output by the carbon emission prediction model are received. If the carbon emission value in the processing load data exceeds the preset carbon emission threshold, and the water condition change rate exceeds the preset fluctuation amplitude, a parameter optimization trigger instruction is generated.

[0087] Exemplarily, real-time monitoring data is input into the carbon emission prediction model, and processing load data and water condition fluctuation information are output. When the daily processing capacity of a certain wastewater treatment plant reaches 80,000 tons and the carbon emission detection value is 145 tons of carbon dioxide equivalent, it is determined that the carbon emission detection value exceeds the preset threshold of 120 tons. At the same time, the water condition change rate monitoring shows that the current fluctuation amplitude is 35%, which exceeds the normal operation baseline range of 25%. At this time, a parameter optimization trigger instruction is automatically generated to start the subsequent regulation process.

[0088] As another optional implementation, the genetic algorithm program is started according to the parameter optimization trigger instruction, the operation parameter boundary values of the process units are obtained through database query, the population individual matrix is initialized, the population individual matrix includes the device parameter combination of aeration amount, reflux ratio, and sludge concentration, the fitness function is used to receive the device parameter combination, the second carbon emission prediction value and the processing efficiency index of each parameter combination under different water conditions are calculated, if the second carbon emission prediction value is lower than the target threshold, the fitness score of this individual is increased, the new generation population is generated through the cross variation operation of the fitness score, and the fitness evaluation process is repeated until convergence, and the optimal process unit operation parameter configuration scheme of each process unit under different water conditions is obtained.

[0089] Exemplarily, after receiving the trigger instruction, the genetic algorithm program extracts the operation boundary constraints from the process parameter database, including that the aeration amount control range is 2000 to 8000 cubic meters per hour, the sludge return ratio is set to 50% to 200%, and the mixed liquor suspended solids concentration is maintained at 2000 to 6000 mg / L. Based on these constraints, a population matrix containing 100 individuals is initialized, each individual representing a complete set of device parameter combinations. Then the fitness function receives the device parameter combination, and the fitness function is designed to use a multi-objective evaluation mechanism, taking carbon emissions as the main optimization target, while considering treatment efficiency and energy cost. For example, a parameter combination scheme sets the aeration amount to 5500 cubic meters per hour, the return ratio to 120%, and the sludge concentration to 4200 mg / L. After simulation calculation, the carbon emissions under this configuration are 98 tons, which is lower than the target threshold, so a higher fitness score is obtained.

[0090] The fitness score is subjected to cross variation operation, which generates new parameter combinations by simulating the biological evolution process. The top 30% of individuals with the highest fitness scores are selected as parents, and the parameter gene fragments are exchanged through single-point crossover. At the same time, a part of the parameters are randomly mutated with a probability of 5% to ensure population diversity. After 50 generations of continuous evolution iteration, when the optimal solution improvement amplitude of 10 consecutive generations is less than 1%, the algorithm is determined to be converged. The final optimization result shows that under low load operation state, the optimal operation parameter configuration scheme is aeration amount of 3200 cubic meters per hour, return ratio of 85%, and sludge concentration of 3500 mg / L, and the predicted carbon emissions can be reduced to 85 tons. Under high load conditions, the optimal operation parameter configuration scheme is aeration amount of 6800 cubic meters per hour, return ratio of 150%, and sludge concentration of 5200 mg / L, which realizes carbon emission control within 115 tons.

[0091] Step S300: obtaining a first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme, and establishing a dynamic parameter control strategy library;

[0092] In this embodiment, the first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme and the water condition fluctuation characteristic data are obtained, a dynamic parameter control strategy library is established, a strategy index mechanism is constructed through carbon emission threshold correlation analysis of the parameter configuration scheme, and if the predicted carbon emissions corresponding to the parameter configuration scheme are in the critical threshold position, it is marked as a high-priority control strategy, and a hierarchical management parameter control strategy library is obtained.

[0093] As an optional implementation, the optimal process unit operation parameter configuration scheme output by the genetic algorithm is received, the corresponding first carbon emission prediction value and water condition fluctuation characteristic data are obtained through database query, if the difference between the first carbon emission prediction value and the preset carbon emission threshold value is less than the critical range, the parameter configuration scheme is marked as a high priority category, and the parameter configuration data of the high priority category is clustered to calculate the carbon emission threshold correlation degree of each configuration scheme.

[0094] Exemplarily, after receiving the process unit operation parameter configuration scheme output by the genetic algorithm, the corresponding carbon emission prediction value and water condition fluctuation characteristic data are obtained through a database query mechanism. When a configuration scheme shows that the aeration amount is 4500 cubic meters per hour and the reflux ratio is 110%, the query obtains that the carbon emission prediction value is 118 tons of carbon dioxide equivalent, and the difference from the preset carbon emission threshold value of 120 tons is only 2 tons, which is less than the critical range of 5 tons. The configuration scheme is marked as a high priority category. The parameter configuration data of the high priority category needs to be clustered to identify the similarity pattern. A clustering algorithm based on Euclidean distance is used, and key parameters such as aeration amount, reflux ratio, and sludge concentration are taken as feature vectors for grouping. For example, when it is identified that the aeration amounts of three configuration schemes are 4200, 4500, and 4800 cubic meters per hour respectively, and the reflux ratios are all within the interval of 100% to 120%, these parameter configuration schemes are classified into the same clustering cluster.

[0095] For the parameter configuration schemes in each clustering cluster, the carbon emission threshold correlation degree is further calculated, which is determined by the ratio of the actual carbon emission prediction value to the preset carbon emission threshold value. Specifically, when the carbon emission prediction value of a configuration scheme is 118 tons and the preset carbon emission threshold value is 120 tons, the correlation degree is 98.3%. This index can directly reflect the optimization potential of each configuration scheme under the condition of meeting the carbon emission constraint, and the higher the correlation degree, the greater the margin from the upper limit of the threshold, and the more significant the optimization value.

[0096] Optionally, each clustering cluster can be sorted according to the calculated correlation degree, and a parameter configuration scheme with a higher correlation degree is preferentially selected as a candidate object for process optimization implementation, while invalid schemes with a negative correlation degree are automatically excluded, thereby forming the final operation parameter configuration scheme.

[0097] As another optional implementation, when it is detected that the carbon emission threshold correlation degree exceeds a preset correlation reference value, an emergency control index is assigned to obtain a hierarchical index structure of the strategy classification result, a storage structure of the dynamic parameter control strategy library is established through the strategy classification result, a strategy retrieval mechanism is constructed according to the emergency control index, the calling weight of each control scheme is determined, a hierarchical management rule is generated according to the calling weight, and then the dynamic parameter control strategy library is generated.

[0098] Exemplarily, when the carbon emission prediction value of a certain configuration scheme is 116 tons, the corresponding carbon emission threshold correlation degree is calculated to be 96.7%, which exceeds the preset correlation reference value of 85%, and thus an emergency control index mark is obtained. This marking mechanism can ensure the rapid response capability in the critical state. The preset correlation reference value is a critical point obtained by analyzing historical accident data. The response mechanism adopts a three-level index architecture. The first level index corresponds to the configuration scheme with the carbon emission value in the range of (115 tons, 120 tons]. The second level index covers the interval of (110 tons, 115 tons]. The third level index includes the scheme below 110 tons. Each level is further subdivided according to the water quality fluctuation characteristics, forming a multi-dimensional strategy index system. The storage structure of the dynamic parameter control strategy library adopts an association table design, which maps the process unit operation parameter configuration, carbon emission prediction value, water quality fluctuation mode and calling weight. When the water quality change rate is 30% and the carbon emission risk is high, the corresponding configuration scheme is assigned a calling weight of 0.9, while the calling weight is set to 0.3 in the case of low risk. The hierarchical management rule determines the strategy priority according to the calling weight. The weight threshold can be set to 0.7. The process unit operation parameter configuration scheme that exceeds the weight threshold has the qualification of automatic calling, and the construction of the dynamic parameter control strategy library is completed.

[0099] Optionally, after the dynamic parameter control strategy library is constructed, its actual application process exhibits a complete closed-loop control system. When the water quality change rate is monitored to reach 32%, a multi-dimensional collaborative response is started. First, the fast retrieval function of the strategy library is activated. Through the pre-set hash index structure, the mode matching of the massive stored strategies is completed within milliseconds, and the parameter configuration group most suitable for the current working condition is accurately located. The weight coefficients of each candidate strategy are comprehensively evaluated. When the calling weight of the configuration scheme exceeds the weight threshold of 0.7, it is determined to use the configuration scheme "aeration amount 5200 cubic meters per hour, reflux ratio 125%", and automatically execute the control instruction. The actual effect is continuously monitored, the actual carbon emission change is recorded, and the weight of the configuration scheme is updated according to the change result. If the emission reduction meets the standard, the weight increases by 0.1.

[0100] Step S400: matching the real-time water quality fluctuation data with the dynamic parameter control strategy library and the mechanism model, determining the real-time parameter control strategy, and executing the equipment parameter adjustment instruction according to the real-time parameter control strategy.

[0101] In this embodiment, real-time water condition fluctuation data is matched with a dynamic parameter regulation strategy library and a mechanism model to determine a real-time regulation strategy. If the current water condition change rate exceeds a preset change threshold and the carbon emission prediction value of the matched strategy is lower than a safety threshold, the corresponding device parameter adjustment instruction is automatically executed to obtain real-time updated device operation parameters. This closed-loop control mechanism realizes complete automation of the monitoring, decision-making, and execution processes, significantly improving the response speed and control accuracy of the sewage treatment system to water condition fluctuations, while ensuring the effective achievement of environmental protection goals through carbon emission prediction constraints.

[0102] As an optional implementation, a fluctuation feature vector of real-time water condition fluctuation data is obtained, and the fluctuation feature vector is input into a mechanism model for theoretical carbon emission pre-calculation to obtain the physical feasible region boundary under the current working condition. At the same time, the fluctuation feature vector is matched with the dynamic parameter regulation strategy library in terms of similarity, and the matching result needs to be verified for physical feasibility by the mechanism model, and the deviation between the actual carbon emission and the predicted carbon emission is continuously monitored during the execution process. When the deviation exceeds the theoretical capacity given by the mechanism model, the dynamic adjustment of the strategy and the online update of the strategy library are triggered, forming a closed-loop control flow of "monitoring-matching-verification-execution-feedback". Then, according to the matching result that has passed the feasibility verification, the parameter regulation strategy record is extracted, and the device operation parameters in the parameter regulation strategy record are calculated for carbon emission prediction. If the predicted carbon emission value is less than a preset safety threshold, the device parameter adjustment instruction is extracted from the parameter regulation strategy record to determine the real-time parameter regulation strategy. The fluctuation feature vector includes multi-dimensional characteristic parameters such as flow rate change rate, water level fluctuation amplitude, and time interval, forming a standardized data structure for subsequent matching analysis.

[0103] For example, the sensor network data acquisition mechanism obtains the operation state information of the sewage treatment system in real time through distributed monitoring nodes. When the water flow sensor detects that the current flow rate is 2.8 cubic meters per second, and the previous recorded value is 2.3 cubic meters per second, the difference is calculated as 0.5 cubic meters per second. Since the difference exceeds the preset change threshold of 0.3 cubic meters per second, the fluctuation feature vector generation program is triggered. The fluctuation feature vector is obtained, and the similarity matching calculation uses the cosine similarity algorithm to search the strategy library for the fluctuation feature vector. When a feature vector containing a flow rate change rate of 27%, a water level rise of 0.5 meters, and a time interval of 15 minutes is received, the historical records in the strategy library are traversed to identify a matching item with a similarity of 0.87, which exceeds the preset matching reference value of 0.8. The corresponding strategy record is extracted, which contains key parameter information such as adjusting the aeration device power to 75% and setting the high frequency of the sludge return pump to 45 Hz.

[0104] The mechanism model verification module receives the matching result, calculates the physical feasible region boundary under the current working condition through the hydrodynamic model, and confirms that the strategy parameter does not exceed the theoretical capacity range. During execution, the carbon emission monitoring system continuously compares the actual carbon emission with the predicted value, and when the actual carbon emission deviates from the predicted value by 4% (without exceeding the 8% theoretical capacity given by the mechanism model), the current strategy is continued to be executed.

[0105] The neural network carbon emission prediction module receives the device operating parameters in the strategy record as input features. When the aeration power is 75% and the reflux pump frequency is 45 Hz, the multi-layer perceptron network calculates and predicts the carbon emission value to be 102 tons of carbon dioxide equivalent. Since the predicted value is lower than the preset safety threshold of 110 tons, the feasibility of the parameter adjustment scheme is confirmed, the parameter control strategy is determined, and the specific device parameter adjustment instructions are extracted from the strategy record.

[0106] As another optional implementation, after determining the parameter control strategy, the specific device parameter adjustment instructions are extracted from the strategy record, and the target device control interface address is determined according to the parameter type identifier in the adjustment instructions. The parameter modification command is sent to the target device through the industrial communication protocol, and the device response state is obtained. If the device response state is successful, the modified device operating parameters are written into the parameter configuration database.

[0107] Exemplarily, the parameter type identifier parsing mechanism determines the control interface address according to the device code in the adjustment instructions. When the aeration device identifier AER_001 is identified, the corresponding Modbus communication address 192.168.1.15 and port number 502 are obtained through the device mapping table query. The system uses the industrial Ethernet protocol to build a communication connection, sends a power adjustment command to the target device, and modifies the current 75% power to 68%. The device controller returns an acknowledgement signal after receiving the command, and the response status code is displayed as 0x01, indicating that the parameter modification is successfully executed. The parameter configuration database update mechanism ensures real-time synchronization of the system operating state. When the device response state is confirmed to be successful, the system writes the modified operating parameters into the corresponding record in the configuration database. The power parameter of the aeration device AER_001 is updated from 75% to 68%, and the modification timestamp and operation source identifier are recorded.

[0108] In this embodiment, a carbon emission prediction model is constructed by fusing real-time monitoring data, historical operation data and mechanism model, effectively solving the problem of delayed response in regulation and control caused by post-emission monitoring in the related art. Meanwhile, a parameter optimization triggering mechanism based on water quality change characteristics is established, solving the problem that the fixed parameter mode is difficult to adapt to water quality fluctuations. The constructed strategy library breaks through the randomness and limitations of manual experience parameter adjustment through intelligent matching algorithm, shortening the decision-making time of process adjustment from hours to seconds. The dynamic parameter regulation strategy library has a continuous self-learning ability and can be continuously optimized with the accumulation of operation data, which can provide a replicable intelligent solution for low-carbon operation in the field of wastewater treatment. This "prediction-decision-execution-optimization" control mode can effectively predict the carbon emissions under different water conditions and reduce energy consumption.

[0109] Embodiment Two

[0110] Based on Embodiment One, another embodiment of the present application is proposed, referring to Figure 2 After matching the real-time water quality fluctuation data with the dynamic parameter regulation strategy library and the mechanism model, determining the real-time parameter regulation strategy, and executing the equipment parameter adjustment instruction according to the real-time parameter regulation strategy, the following steps are included:

[0111] Step S500: When executing the equipment parameter adjustment instruction, acquiring the influent water quality data, effluent water quality data and key node water quality data of the wastewater plant; wherein the water quality data in the influent water quality data and effluent water quality data includes but is not limited to PH / T, SS, TP, TN, COD and The key nodes include but are not limited to the biological reaction tank node and the high school sedimentation tank water node; the key node water quality data includes but is not limited to DO, ORP, MLSS, TP, TN, COD, Step S500: When executing the equipment parameter adjustment instruction, acquiring the influent water quality data, effluent water quality data and key node water quality data of the wastewater plant; wherein the water quality data in the influent water quality data and effluent water quality data includes but is not limited to PH / T, SS, TP, TN, COD and

[0112] The influent water quality data is the feedforward signal of the system, and the effluent water quality data and the key node data are the feedback signals of the system;

[0113] Step S600: If the water quality data and the standard process difference exceed the preset water quality deviation range, convert the water quality data into the input variable of the fuzzy control algorithm, and output the fuzzy data;

[0114] In this embodiment, the fuzzy control algorithm can be used to finely regulate the equipment parameters of each process unit, and the uncertain factors in the parameter adjustment process can be processed through the fuzzy inference mechanism. If multiple process units are adjusted at the same time, the control instruction is executed according to the preset priority order to determine the final equipment operation state.

[0115] As an optional implementation, when executing the device parameter adjustment instruction, the full-process water quality data of the sewage plant is collected in real time by the distributed sensor network, including the water quality and quantity parameters of the inlet, outlet and key process nodes (such as biochemical reaction tank and high-efficiency sedimentation tank). The inlet water quality data (PH / T, SS, TP, TN, COD, , etc.) are input as feedforward signals into the control model to predict process disturbance and adjust device parameters in advance; the outlet water quality and key node data (DO, ORP, MLSS, TP, TN, COD, , etc.) are used as feedback signals to check process effect in real time and trigger dynamic correction. When the deviation of the inlet or node water quality parameters (such as COD, ) from the standard process value exceeds the preset threshold (such as COD deviation > 15%, deviation > 10%), the exceeding parameters are automatically converted into input variables of the fuzzy control algorithm, which are fuzzified into “low”, “medium” and “high” three-level language variables through the membership function, and the fuzzy adjustment instructions (such as “aeration quantity increases by 20%” and “return ratio decreases by 10%”) are output in combination with the process rule base, and finally the accurate device control parameters are obtained by defuzzification.

[0116] For example, when a sewage plant executes the instruction “aeration device power adjustment to 75%”, it simultaneously starts full-process water quality monitoring, first collects data, the inlet sensor feedbacks the current PH value of 7.2 (standard range 6.8-7.5), COD concentration of 320 mg / L (standard value ≤ 250 mg / L, deviation + 28%), and concentration of 35 mg / L (standard value ≤ 25 mg / L, deviation + 40%), showing that the inlet water quality has a significant fluctuation; the dissolved oxygen (DO) concentration in the biochemical reaction tank is 1.8 mg / L (standard value 2-4 mg / L, deviation -25%), and the mixed liquor suspended solids (MLSS) concentration is 3800 mg / L (standard value 3000-4500 mg / L, within the normal range); the outlet sensor shows that the COD concentration is 45 mg / L (standard value ≤ 50 mg / L), and concentration is 4.2 mg / L (standard value ≤ 5 mg / L), and the outlet water quality has not exceeded the standard. Since the deviations of the inlet COD and concentrations from the standard process value exceed the preset threshold (15% and 10% respectively), the system automatically triggers the fuzzy control logic, taking the inlet COD (320 mg / L) and (35 mg / L) as input variables, which are fuzzified into “low”, “medium” and “high” three-level language variables through the trapezoidal membership function—among which the COD is determined as “high” (membership degree 0.8) due to the deviation + 28%, The deviation of +40% was classified as "high" (membership degree 0.9). This is consistent with the process rule library's statement that "if the influent COD is high and..." Following the rule of "increasing aeration rate and sludge return ratio when the aeration rate is high," the system outputs fuzzy adjustment commands: "Aeration rate increase = 0.7 (high), sludge return ratio adjustment = 0.5 (medium)." Precise control parameters are obtained through defuzzification using the center-of-gravity method: aeration equipment power is increased from 75% to 85%, and sludge return pump frequency is increased from 45Hz to 50Hz. After 10 minutes of adjustment, the DO concentration in the biological reactor rises to 2.5 mg / L, and the effluent COD concentration drops to 38 mg / L. When the concentration dropped to 3.5 mg / L, all parameters returned to the standard range, verifying the dynamic correction capability of fuzzy control for water quality fluctuations.

[0117] Step S700: Match the corresponding inference rule in the pre-established fuzzy rule library according to the fuzzy value. If the number of matched inference rules exceeds the threshold of a single rule, the inference rules are weighted and calculated through the fuzzy inference mechanism to adjust the order of the device parameter adjustment instructions.

[0118] As an optional implementation, the corresponding inference rule is matched in a pre-established fuzzy rule base according to the fuzzy value. If the number of matched rules exceeds the threshold of a single rule, the fuzzy inference mechanism is used to perform weighted calculation on multiple rules, and the parameter adjustment instruction is obtained through defuzzification.

[0119] For example, the fuzzy rule base matching mechanism retrieves the corresponding inference rules based on the fuzzy values. When a fuzzy state of "low temperature" and "medium pressure" is identified, three relevant rules are matched: Rule A suggests increasing the heater power by 15%, Rule B suggests increasing the circulation pump frequency by 8 Hz, and Rule C suggests adjusting the inlet valve opening to 70%. Since the number of matched rules is 3, exceeding the single rule threshold of 2, the fuzzy inference mechanism is activated for weighted calculation. The weighted calculation process allocates weights based on the confidence level of each rule. The confidence level of Rule A is 0.8, the confidence level of Rule B is 0.6, and the confidence level of Rule C is 0.7. Through centroid defuzzification, a comprehensive control command is finally obtained, which adjusts the heater power to 78%, sets the circulation pump frequency to 52 Hz, and adjusts the inlet valve opening to 68%.

[0120] As another optional embodiment, when there are multiple parameter adjustment instructions, a priority sorting algorithm can be used to sort the parameter adjustment instructions according to the importance of the process units. If multiple process units need to be adjusted at the same time and there is a resource conflict, the control instructions are executed in the preset priority order, the specific parameter modification values of each device are obtained, the parameter modification values are sent to the process unit through the device control interface, if the device response time exceeds the preset response time limit, the device abnormal information is recorded and switched to the backup control scheme, and the actual running state parameters of each process unit device are determined.

[0121] Exemplarily, the priority sorting algorithm sorts the control instructions according to the importance of the process units. When the anaerobic tank, the aerobic tank and the secondary sedimentation tank need parameter adjustment at the same time, the anaerobic tank is set as priority 1 according to the influence degree of the treatment effect, the aerobic tank is set as priority 2, and the secondary sedimentation tank is set as priority 3. If there is a conflict in the heating device resources, the temperature control instruction of the anaerobic tank is executed first, and then the control requirements of other process units are processed in turn. The specific parameter modification values of each device are obtained, and the parameter modification values are sent to the process unit through the device control interface. The device control interface communication mechanism ensures the reliable transmission of the parameter modification instruction. When the power adjustment instruction is sent to the anaerobic tank heater, the response time limit is set to 3 seconds. If the device does not return an acknowledgement signal within the specified time, the abnormal information of the device AH_003 is recorded, and the backup control scheme is switched to, and the temperature adjustment target is achieved through the auxiliary heating device. This fault-tolerant mechanism ensures the continuity and reliability of the process parameter control, significantly improves the automation control level and operation stability of the sewage treatment system.

[0122] In this embodiment, after the parameter control instruction is sent to the target device, the pressure value and the temperature value of each process unit are continuously monitored, and a closed-loop fuzzy control system is constructed to realize intelligent dynamic optimization of the sewage treatment process parameters. When the device parameter adjustment causes the temperature to exceed the standard, the real-time data is automatically converted into fuzzy variables, the order and parameters of the adjustment instruction are dynamically optimized through rule base matching and multi-rule weighted calculation, thereby significantly improving the process stability and reducing energy consumption.

[0123] Embodiment three

[0124] Based on embodiment one, another embodiment of the present application is proposed, referring to Figure 3 After the step of matching the real-time water condition fluctuation data with the dynamic parameter control strategy library, determining the real-time parameter control strategy, and executing the device parameter adjustment instruction according to the real-time parameter control strategy, the following steps are included:

[0125] Step S800: After executing the device parameter adjustment instruction, actual carbon emission data is obtained, and if the actual carbon emission data is abnormal and the number of continuous abnormal times is equal to an abnormality determination threshold, a carbon emission prediction model parameter updating procedure is started;

[0126] Step S900: According to the carbon emission prediction model parameter updating procedure, the type of the parameter of the first prediction module that needs to be corrected is determined.

[0127] In this embodiment, after the device parameter adjustment is performed, the actual carbon emission data after the parameter adjustment is obtained, and a comparison analysis is performed on the actual carbon emission data and the carbon emission prediction value output by the carbon emission prediction model, and if the deviation between the actual carbon emission data and the first carbon emission prediction value exceeds a permissible range, the carbon emission prediction model parameter is updated, and the carbon emission prediction accuracy is corrected.

[0128] As an optional implementation, the concentration values of nitrous oxide and methane during the operation of the process unit can be collected by a carbon emission monitoring sensor, and if the collection interval time of the concentration values exceeds a preset monitoring frequency, a data compensation mechanism is triggered to perform interpolation calculation on the emission data of the actual time period to obtain a complete actual carbon emission monitoring data sequence, a pre-trained neural network prediction module is called according to the actual carbon emission monitoring data sequence, if there is a significant difference between the process parameters of the current time period and the parameter range of the historical training data, the input weight of the prediction module is dynamically adjusted, the carbon emission prediction value corresponding to the time period is obtained, the actual carbon emission data and the carbon emission prediction value are subjected to difference operation by using a deviation calculation formula, and if the absolute value of the calculated deviation exceeds a preset permissible range and the number of continuous occurrences reaches an abnormality determination threshold, a model parameter updating procedure is started.

[0129] Exemplarily, the anaerobic tank is configured with methane and nitrous oxide concentration sensors, and the sampling frequency is set to collect data once every 30 seconds. Under normal operating conditions, the methane concentration is maintained within the range of 800-1200 ppm, and the nitrous oxide concentration is 50-150 ppm. During the monitoring process from 14:30 to 14:33 on a certain day, the system detects that the methane concentration data at 14:32 is missing, and immediately triggers the data compensation mechanism: based on the linear interpolation algorithm, the compensation value of 14:32 is calculated to be 1000 ppm by combining the measured value of 980 ppm at 14:30 and the measured value of 1020 ppm at 14:33, thereby forming a complete time series data set.

[0130] The neural network prediction module is called, and the neural network prediction module adopts a multi-layer perceptron structure. The input layer includes 12 process parameter nodes such as temperature, pH value and dissolved oxygen. When it is identified that the temperature of the current anaerobic tank is 28 degrees Celsius, and the temperature range of the historical training data is 30 to 38 degrees Celsius, the neural network prediction module automatically adjusts the input weight of the temperature parameter from 0.8 to 0.6 to adapt to the current working condition. After processing by the activation function in the hidden layer, the output layer generates the methane concentration of 1050 ppm and the nitrous oxide concentration of 110 ppm in the next 2 hours.

[0131] The difference calculation formula is used for difference operation, and the deviation calculation mechanism compares the actual monitoring value with the predicted value in real time. The actual monitoring value (methane 1020 ppm, nitrous oxide 115 ppm) at 14:33 is compared with the predicted value: the absolute value of the methane deviation is |1020-1050|=30ppm (not exceeding the preset tolerance 45ppm), and the absolute value of the nitrous oxide deviation is |115-110|=5ppm (normal). However, the methane deviation of the subsequent 5 consecutive sampling points (14:34-14:36) continues to expand, with a maximum deviation of 65ppm, triggering the abnormal judgment threshold. The model parameter updating program is started. At this time, it is identified that the types of parameters that need to be corrected include the hidden layer weight matrix and the output layer bias vector.

[0132] As another optional implementation, according to the carbon emission prediction model parameter updating program, the types of parameters of the first prediction module that need to be corrected are determined.

[0133] For example, when the prediction deviation exceeds the standard for 5 consecutive times, the parameter tracing analysis is started: the contribution of each network layer to the total error is calculated by the back propagation algorithm, it is found that the sensitivity of the hidden layer weight matrix to abnormal temperature input increases, the contribution is 42%, and the output layer bias vector shows a systematic deviation in error transmission, the deviation is more than 2 times the standard deviation. At this time, combined with the real-time working condition data and model structure analysis, it is identified that these two types of parameters have produced adaptive degradation due to long-term non-adjustment, such as the L2 norm of the weight matrix decreasing by 37% and the bias vector distribution deviating from the training period benchmark by 15%, so as to accurately lock the hidden layer weight matrix and the output layer bias vector as the core parameters to be corrected. This triple verification mechanism based on error contribution analysis, parameter health evaluation and working condition adaptability detection ensures the pertinence and effectiveness of parameter correction.

[0134] Step S1000: updating the connection weight and bias parameter in the first prediction module according to the parameter type, to generate a second prediction module;

[0135] As an optional implementation, the connection weight and bias parameter in the first prediction module can be iteratively updated by a gradient descent optimization algorithm to generate a second prediction module.

[0136] Illustratively, the gradient descent optimization algorithm adjusts the network parameters through a backpropagation mechanism, with a learning rate set to 0.01 and a batch size of 32 samples. In the first iteration, the key connection weight of the third hidden layer is adjusted from the initial value of 0.45 to 0.52 along the error gradient direction, while the corresponding bias parameter is optimized from 0.12 to 0.08. This precise parameter update significantly improves the model's performance on the validation set: for the validation data containing temperature fluctuations, the average prediction error decreases from 38 ppm for the first prediction module to 24 ppm for the second prediction module, a decrease of 37%. The parameter-optimized prediction module can more accurately identify the impact of process operation mode changes on carbon emissions. When the aeration rate of the aerobic tank is adjusted from 180 cubic meters per hour to 220 cubic meters per hour, the corrected model predicts that the carbon dioxide emissions will peak within 30 minutes and then gradually fall to a stable level. This dynamic prediction capability provides reliable decision support for operators, effectively avoiding abnormal fluctuations in carbon emissions caused by process adjustments, and significantly improving the environmental management and control level and operational efficiency of wastewater treatment.

[0137] Step S1100: Obtain the validation error based on the second prediction module, and determine whether the validation error is abnormal. If not, update the carbon emission prediction model according to the second prediction module.

[0138] As an optional implementation, the validation error refers to the error indicator calculated when the performance of the second prediction module is evaluated using an independent validation dataset after updating the model parameters. The validation dataset contains historical temperature, pH, dissolved oxygen, and other input features and corresponding true carbon dioxide concentration values. If the validation error after parameter updating shows a downward trend compared to the error value before updating, i.e., there is no abnormality, then save the updated model parameter configuration to obtain the corrected carbon emission prediction accuracy indicator.

[0139] Exemplarily, when it is detected that the carbon dioxide concentration prediction deviation exceeds 45 ppm for 5 times in succession, the parameter updating procedure is started. Assuming that the mean square error of the model before updating on the validation set is 80 ppm2, after 3 rounds of iterative adjustment of the hidden layer weights and output layer bias by the gradient descent algorithm, the same validation set is retested, and it is found that the mean square error is reduced to 65 ppm2, with a reduction of 18.75%. It is determined that this parameter update is effective, and the new weight matrix and bias vector are immediately saved, and the updated prediction accuracy index is recorded. For example, in the actual operation of an anaerobic tank, the updated model improves the average deviation of the predicted value of carbon emission in the next 2 hours from the actual monitoring value from ±58 ppm to ±43 ppm, significantly improving the early warning reliability. This dynamic updating mechanism based on the comparison of validation errors not only avoids the performance fluctuations of the model caused by local optimization, but also ensures that each parameter adjustment brings quantifiable precision improvement.

[0140] As another optional implementation, after updating the carbon emission prediction model according to the second prediction module, the energy consumption level and emission trend of each process unit can be recalculated. The process unit operating state data of the updated carbon emission prediction model is obtained, the process unit operating state data is compared with the historical data, the energy consumption level and the emission trend curve of the process unit are determined, the energy consumption level and the emission trend curve are read by the feedback control program, and if it is detected that the emission trend curves of three process units in succession present an upward state, the corresponding aeration amount adjustment scheme and reflux ratio control scheme are retrieved from the dynamic parameter control strategy library, and the updated process parameter set value is obtained.

[0141] For example, the energy consumption rating calculation mechanism recalculates the power consumption of each process unit based on the corrected prediction accuracy. The power calculation for the biological treatment tank involves a comprehensive energy consumption analysis of aeration blowers, agitators, and circulating pumps. When the aeration blower power in the aerobic tank of the secondary wastewater treatment A2O process reaches 85 kW, while the historical average for the same period is 72 kW, this unit is marked as a high-energy-consumption state. Simultaneously, the gas emission rate calculation shows that methane emissions during this period are 12 cubic meters per hour, an increase of approximately 30% compared to normal operating conditions. The emission trend curve is generated using a sliding window algorithm for data processing. Continuous monitoring of emission data from the anaerobic, anoxic, and aerobic tanks is performed. When the carbon dioxide emission rates of the three consecutive units are detected to increase from 8 cubic meters per hour, 6 cubic meters per hour, and 15 cubic meters per hour to 11 cubic meters per hour, 9 cubic meters per hour, and 19 cubic meters per hour, respectively, the feedback control program immediately triggers the parameter adjustment mechanism. The dynamic parameter control strategy library contains preset schemes for different operating conditions. When the emission trend is upward, the aeration rate reduction scheme is activated, gradually reducing the aeration rate of the aerobic tank from 220 cubic meters per hour to 180 cubic meters per hour. Simultaneously, the internal recirculation ratio is adjusted from 200% to 150%, and the external recirculation ratio is adjusted from 80% to 60%. This coordinated control strategy can effectively balance biochemical reaction efficiency and energy consumption control.

[0142] As another optional implementation, after obtaining the process parameter setting values, a real-time monitoring program is used to apply the process parameter setting values ​​to obtain the dissolved oxygen concentration fluctuation range value during operation after parameter adjustment, and the carbon control status is determined based on the dissolved oxygen concentration fluctuation value; if the carbon control status meets the preset carbon control conditions, the process parameter setting values ​​of the process unit are kept unchanged.

[0143] For example, the real-time monitoring program verifies and evaluates the effect of parameter adjustments. Dissolved oxygen concentration monitoring shows that the adjusted value is stable within the range of 2.5 mg / L to 3.2 mg / L, with a fluctuation range of only 0.7 mg / L, far less than the preset threshold of 1.2 mg / L. The sludge settling ratio is maintained in the ideal range of 25% to 30%, indicating that the current operational stability indicators have met the requirements. A confirmation signal of stable operation is obtained, and the current aeration rate setting and return ratio control parameters are kept unchanged. At this time, the aeration rate is maintained at 180 cubic meters per hour, and the internal return ratio is fixed at 150%.

[0144] Optionally, after obtaining a confirmation signal of stable operation, monitoring continues. If a significant change in influent water quality or fluctuation in treatment load is detected, the parameter control process is restarted. For example, when the chemical oxygen demand (COD) concentration suddenly rises from 300 mg / L to 450 mg / L, or the treatment capacity increases from 800 cubic meters per hour to 1000 cubic meters per hour, the load fluctuation detection module identifies the change in operating conditions and automatically restarts the parameter control process.

[0145] In the embodiment, after the device parameter adjustment is performed, the sewage treatment process is continuously monitored, and the related parameters of the carbon emission prediction model are optimized according to the monitoring result, and the closed-loop control mechanism realizes the dynamic balance of energy consumption optimization and emission control. By adjusting the process parameters in real time, the overall energy consumption can be reduced under the premise of meeting the effluent quality standards, and the emission of greenhouse gases can be reduced.

[0146] Embodiment Four

[0147] In the embodiment of the present application, an artificial intelligence-based carbon control device for a sewage treatment plant is provided.

[0148] Reference Figure 4 , Figure 4 The terminal structure diagram of the hardware running environment involved in an embodiment of the present application is shown.

[0149] As Figure 4 shown, the control terminal can include a processor 1001, such as a CPU, a network interface 1003, a memory 1004, and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between these components. The network interface 1003 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1004 can be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a magnetic disk memory. The memory 1004 can optionally be a storage device independent of the aforementioned processor 1001.

[0150] Those skilled in the art can understand that Figure 4 the terminal structure shown in the embodiment does not constitute a limitation on the terminal, and can include more or fewer components than the diagram, or combine certain components, or different component arrangements.

[0151] As Figure 4 shown, the memory 1004, as a computer storage medium, can include an operating system, a network communication module, and an artificial intelligence sewage treatment plant carbon control program.

[0152] In Figure 4 the hardware structure of the artificial intelligence sewage treatment plant carbon control device, the processor 1001 can call the artificial intelligence sewage treatment plant carbon control program stored in the memory 1004, and perform the following operations:

[0153] A carbon emission prediction model is constructed according to collected real-time monitoring data, historical data and a mechanism model, wherein the real-time monitoring data includes water condition fluctuation data and process unit operation state data; the water condition fluctuation data includes water quantity fluctuation data and water quality fluctuation data; the historical data includes historical water condition fluctuation sequences, historical process unit operation parameters and historical carbon emission amounts;

[0154] A parameter optimization trigger instruction is generated based on processing load data and a water condition change rate output by the carbon emission prediction model, and a process unit operation parameter configuration scheme based on a water condition is generated according to the parameter optimization trigger instruction;

[0155] A first carbon emission prediction value corresponding to the process unit operation parameter configuration scheme is obtained, and a dynamic parameter regulation strategy library is established;

[0156] Real-time water condition fluctuation data is matched with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy, and a device parameter adjustment instruction is executed according to the real-time parameter regulation strategy.

[0157] Optionally, the processor 1001 can invoke an artificial intelligence sewage treatment plant carbon control program stored in the memory 1004, and further perform the following operations:

[0158] If a carbon emission amount value in the processing load data exceeds a preset carbon emission threshold value, and the water condition change rate exceeds a preset fluctuation amplitude, the parameter optimization trigger instruction is generated;

[0159] A genetic algorithm program is started according to the parameter optimization trigger instruction, operation parameter boundary values of a process unit are obtained, and a population individual matrix of the genetic algorithm is initialized according to the operation parameter boundary values, wherein the population individual matrix includes a device parameter combination of aeration quantity, reflux ratio and sludge concentration;

[0160] A second carbon emission prediction value and a processing efficiency index of the device parameter combination are evaluated based on the water condition, and an adaptability score is generated;

[0161] If the adaptability score does not satisfy an expected condition, a cross variation operation is performed on the device parameter combination, and the device parameter combination after the cross variation operation is reevaluated, and the adaptability score is updated;

[0162] When the adaptability score satisfies the expected condition, a device parameter combination corresponding to the expected condition is output, and the process unit operation parameter configuration scheme is generated.

[0163] Optionally, the processor 1001 can invoke an artificial intelligence sewage treatment plant carbon control program stored in the memory 1004, and further perform the following operations:

[0164] The historical water quality fluctuation sequence is taken as an input feature vector, and the historical carbon emission sequence is taken as a target output value to generate a training data set, wherein each training sample contains water quality data of a preset time length and a corresponding single carbon emission value;

[0165] A loss function value between the predicted carbon emission and the actual carbon emission is calculated based on the training data set through a back propagation algorithm;

[0166] The network parameter configuration is adjusted according to the loss function value, and the prediction accuracy of the network parameter configuration is evaluated to determine a first weight parameter;

[0167] The real-time monitoring data is received according to the first weight parameter, a prediction input vector is constructed, and the carbon emission prediction model is generated in combination with the mechanism model.

[0168] Optionally, the processor 1001 can invoke the artificial intelligence sewage treatment plant carbon management program stored in the memory 1004, and further perform the following operations:

[0169] If the difference between the first carbon emission prediction value and the preset carbon emission threshold value is less than a critical range, the carbon emission threshold value correlation degree of the process unit operation parameter configuration scheme is calculated;

[0170] If the carbon emission threshold value correlation degree exceeds a preset correlation reference value, an emergency control index identifier is assigned, and a hierarchical index structure strategy classification result is generated;

[0171] A storage structure of a dynamic parameter control strategy library is established through the strategy classification result, a strategy retrieval mechanism is constructed according to the emergency control index identifier, and the calling weight of the process unit operation parameter configuration scheme control scheme is determined;

[0172] The dynamic parameter control strategy library is generated according to the calling weight in a hierarchical management manner.

[0173] Optionally, the processor 1001 can invoke the artificial intelligence sewage treatment plant carbon management program stored in the memory 1004, and further perform the following operations:

[0174] The fluctuation feature vector of the water quality fluctuation data is obtained, the fluctuation feature vector is matched with the dynamic parameter control strategy library and the mechanism model in terms of similarity, and a parameter control strategy record is extracted according to the matching result;

[0175] The device operation parameters in the parameter control strategy record are subjected to carbon emission prediction calculation, and if the predicted carbon emission value is less than a preset safety threshold value, a device parameter adjustment instruction is extracted from the parameter control strategy record to determine the real-time parameter control strategy;

[0176] According to the parameter type identifier in the real-time parameter regulation strategy, a target device control interface address is determined, and the device parameter adjustment instruction is sent to the target device.

[0177] Optionally, the processor 1001 can call the artificial intelligence sewage treatment plant carbon management program stored in the memory 1004, and further perform the following operations:

[0178] When the device parameter adjustment instruction is executed, the influent water condition data, effluent water condition data, and key node water condition data of the sewage plant are obtained; the water quality data in the influent water condition data and effluent water condition data includes but is not limited to PH / T, SS, TP, TN, COD, and The key nodes include but are not limited to the biological and reverse tank nodes and the high school sedimentation tank water nodes; the key node water quality data includes but is not limited to DO, ORP, MLSS, TP, TN, COD, , and the like;

[0179] The influent water condition data is a feedforward signal of the system, and the effluent water condition data and the key node data are feedback signals of the system;

[0180] If the difference between the water quality data and the standard process exceeds the preset water quality deviation range, the water quality data is converted into an input variable of a fuzzy control algorithm, and a fuzzy value is output;

[0181] According to the fuzzy value, a corresponding inference rule is matched in a pre-established fuzzy rule library; if the number of matched inference rules exceeds a single rule threshold, the inference rules are weighted calculated through a fuzzy inference mechanism, and the order of the device parameter adjustment instruction is adjusted.

[0182] Optionally, the processor 1001 can call the artificial intelligence sewage treatment plant carbon management program stored in the memory 1004, and further perform the following operations:

[0183] After the device parameter adjustment instruction is executed, actual carbon emission data is obtained; if the actual carbon emission data is abnormal and the number of continuous abnormal times is equal to an abnormality determination threshold, a carbon emission prediction model parameter updating program is started;

[0184] According to the carbon emission prediction model parameter updating program, the parameter type of the first prediction module that needs to be corrected is determined;

[0185] According to the parameter type, the connection weight and bias parameters in the first prediction module are updated to generate a second prediction module;

[0186] Obtaining a verification error based on the second prediction module, and determining whether the verification error is abnormal, if not, updating the carbon emission prediction model according to the second prediction module.

[0187] Optionally, the processor 1001 can call the sewage treatment plant carbon management program of artificial intelligence stored in the memory 1004, and further perform the following operations:

[0188] Obtaining the process unit operating state data applying the updated carbon emission prediction model;

[0189] Comparing the process unit operating state data with historical data to determine the energy consumption level and emission trend curve of the process unit;

[0190] If it is detected that the three consecutive emission trend curves present an upward state, the corresponding aeration amount adjustment scheme and reflux ratio control scheme are retrieved from the dynamic parameter regulation strategy library to obtain the process parameter set value of the process unit;

[0191] Obtaining a dissolved oxygen concentration fluctuation value based on the process parameter set value, and determining the carbon management state according to the dissolved oxygen concentration fluctuation value;

[0192] If the carbon management state meets the preset carbon management condition, the process parameter set value of the process unit is kept unchanged.

[0193] In addition, to achieve the above-mentioned purpose, the embodiment of the present application further provides a kind of sewage treatment plant carbon management system based on artificial intelligence, the system includes:

[0194] A model construction module constructs a carbon emission prediction model according to collected real-time monitoring data, historical data and mechanism model, wherein the real-time monitoring data includes water fluctuation data, process unit operating state data; The water fluctuation data includes water fluctuation data and water quality fluctuation data; The historical data includes historical water fluctuation sequence, historical process unit operating parameter and historical carbon emission;

[0195] An instruction generation module is used to generate parameter optimization trigger instruction based on the process load data and water condition change rate output by the carbon emission prediction model, and generate process unit operating parameter configuration scheme based on water condition according to the parameter optimization trigger instruction;

[0196] A strategy construction module is used to obtain a first carbon emission prediction value corresponding to the process unit operating parameter configuration scheme, and establish a dynamic parameter regulation strategy library;

[0197] An instruction execution module is configured to match the real-time water condition fluctuation data with the dynamic parameter regulation strategy library and the mechanism model, determine a real-time parameter regulation strategy, and execute a device parameter adjustment instruction according to the real-time parameter regulation strategy.

[0198] In addition, to achieve the above object, the embodiment of the present application further provides a computer readable storage medium, and the computer readable storage medium stores an artificial intelligent sewage treatment plant carbon control program. When the artificial intelligent sewage treatment plant carbon control program is executed by a processor, the artificial intelligent sewage treatment plant carbon control method is realized.

[0199] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can adopt a computer program product in the form of being implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0200] The present application is described with reference to flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts 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, a special purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device implemented in the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks.

[0201] These computer program instructions can also be stored in a computer readable storage medium which can guide the computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction devices which implement the flowcharts and / or block diagrams. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks. Figure 1 The device for implementing the functions specified in one flow or multiple flows and / or blocks.

[0202] These computer program instructions can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer implemented process, so that the instructions executed on the computer or other programmable device provide a process for implementing the flowcharts and / or block diagrams. Figure 1one or more processes and / or blocks Figure 1 steps of the functions specified in the one or more blocks or one or more blocks.

[0203] It is noted that in the claims the word "comprising" does not exclude not having other parts than those listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. It is further noted that claims which include an element having a predefined property can also include other elements without the predefined property. The application can be implemented by means of both hardware and software, and any combination thereof. In a unitary claim, several devices or means can be listed having any combination of the stated features to provide several arguably distinct devices. The herein described elements, features and acts can also be implemented in software providing functions to be performed on or by one or more appropriate apparatuses or means. These elements, features and acts can also be implemented in one or more of the aforementioned means comprising more than one device or means. Each of the devices or means can also be implemented, fully or partially, in hardware. The embodiments are not, however, restricted to hardware implementation, they can and also pertain also to software or computer program product.

[0204] Although the preferred embodiments of the application have been described, those skilled in the art will recognize that many modifications and variations of the described implementation can be made without departing from the spirit or scope of the application. Accordingly, it is intended that all such modifications and variations be included within the scope of the following claims and the scope of the application.

[0205] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. An artificial intelligence-based carbon management method for a sewage treatment plant, characterized by, include: A carbon emission prediction model is constructed based on the collected real-time monitoring data, historical data, and mechanistic models. The real-time monitoring data includes water condition fluctuation data and process unit operating status data; the water condition fluctuation data includes water quantity fluctuation data and water quality fluctuation data; the historical data includes historical water condition fluctuation sequences, historical process unit operating parameters, and historical carbon emissions. Based on the processing load data and water condition change rate output by the carbon emission prediction model, a parameter optimization trigger command is generated, and a process unit operation parameter configuration scheme based on water condition conditions is generated according to the parameter optimization trigger command. Obtain the first carbon emission prediction value corresponding to the process unit operating parameter configuration scheme, and establish a dynamic parameter control strategy library; The real-time water condition fluctuation data is matched with the dynamic parameter control strategy library and the mechanism model to determine the real-time parameter control strategy, and the equipment parameter adjustment command is executed according to the real-time parameter control strategy. The step of generating parameter optimization trigger instructions based on the treatment load data and water condition change rate output by the carbon emission prediction model, and generating a process unit operation parameter configuration scheme based on water condition conditions according to the parameter optimization trigger instructions, includes: If the carbon emission value in the processing load data exceeds the preset carbon emission threshold, and the water condition change rate exceeds the preset fluctuation range, then the parameter optimization trigger instruction is generated. The genetic algorithm program is started according to the parameter optimization trigger command to obtain the boundary values ​​of the operating parameters of the process unit, and the population individual matrix of the genetic algorithm is initialized according to the boundary values ​​of the operating parameters. The population individual matrix includes the combination of equipment parameters such as aeration rate, return ratio and sludge concentration. Based on the water conditions, the second carbon emission prediction value and treatment efficiency index of the equipment parameter combination are evaluated, and a fitness score is generated. If the fitness score does not meet the expected conditions, a crossover mutation operation is performed on the device parameter combination, and the crossover mutation device parameter combination is re-evaluated to update the fitness score. When the fitness score meets the expected conditions, the combination of equipment parameters that meets the expected conditions is output, and the configuration scheme of the process unit operating parameters is generated. The step of obtaining the first carbon emission prediction value corresponding to the process unit operating parameter configuration scheme and establishing a dynamic parameter control strategy library includes: If the difference between the first carbon emission prediction value and the preset carbon emission threshold is less than the critical range, then the carbon emission threshold correlation degree of the process unit operating parameter configuration scheme is calculated. If the correlation of the carbon emission threshold exceeds the preset correlation benchmark value, an emergency control index identifier is assigned, and a strategy classification result with a hierarchical index structure is generated. The storage structure of the dynamic parameter control strategy library is established based on the strategy classification results, and a strategy retrieval mechanism is constructed based on the emergency control index identifier to determine the calling weight of the process unit operation parameter configuration scheme control scheme. The dynamic parameter control strategy library is generated based on the call weight and managed hierarchically.

2. The artificial intelligence based carbon management method for wastewater treatment plant as claimed in claim 1 wherein, The step of constructing a carbon emission prediction model according to the collected real-time monitoring data, historical data and mechanism model comprises: The historical water quality fluctuation sequence is taken as an input feature vector, and the historical carbon emission sequence is taken as a target output value to generate a training data set, wherein each training sample contains water quality data of a preset time length and a corresponding single carbon emission value; A loss function value between the predicted carbon emission and the actual carbon emission is calculated based on the training data set through a back propagation algorithm; The network parameter configuration is adjusted according to the loss function value, and the prediction accuracy of the network parameter configuration is evaluated to determine a first weight parameter; The real-time monitoring data is received according to the first weight parameter to construct a prediction input vector, and the carbon emission prediction model is generated in combination with the mechanism model. 3.The artificial intelligence-based carbon management method for a wastewater treatment plant of claim 1, wherein, The step of matching the real-time water quality fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy comprises: A fluctuation feature vector of the water quality fluctuation data is obtained, the fluctuation feature vector is matched with the dynamic parameter regulation strategy library and the mechanism model in terms of similarity, and a parameter regulation strategy record is extracted according to the matching result; Carbon emission prediction calculation is performed on the device operation parameters in the parameter regulation strategy record, if the predicted carbon emission value is less than a preset safety threshold, a device parameter adjustment instruction is extracted from the parameter regulation strategy record to determine the real-time parameter regulation strategy; The parameter type identifier in the real-time parameter regulation strategy is used to determine a target device control interface address, and the device parameter adjustment instruction is sent to the target device. 4.The artificial intelligence-based carbon management method for a wastewater treatment plant of claim 1, wherein After the step of matching the real-time water quality fluctuation data with the dynamic parameter regulation strategy library and the mechanism model to determine a real-time parameter regulation strategy and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy, the following steps are included: When the device parameter adjustment instruction is executed, the influent water quality data, effluent water quality data and key node water quality data of the sewage plant are obtained; wherein the water quality data in the influent water quality data and effluent water quality data includes but is not limited to PH / T, SS, TP, TN, COD and NH3 values; the key nodes include but are not limited to the biological reaction tank node and the high-rate sedimentation tank water node; the key node water quality data includes but is not limited to DO, ORP, MLSS, TP, TN, COD and NH3 data; The influent water quality data is a feedforward signal of the system, and the effluent water quality data and the key node data are feedback signals of the system; If the difference between the water quality data and the standard process exceeds a preset water quality deviation range, the water quality values are converted into input variables of a fuzzy control algorithm, and fuzzy values are output; The fuzzy values are matched with corresponding inference rules in a pre-established fuzzy rule library, if the number of the matched inference rules exceeds a single rule threshold, the inference rules are weighted calculated through a fuzzy inference mechanism, and the order of the device parameter adjustment instruction is adjusted. 5.The artificial intelligence-based carbon management method for a wastewater treatment plant of claim 1, wherein, The step of matching the real-time water condition fluctuation data with the dynamic parameter regulation strategy library and the mechanism model, determining a real-time parameter regulation strategy, and executing a device parameter adjustment instruction according to the real-time parameter regulation strategy further comprises: After executing the device parameter adjustment instruction, actual carbon emission data is obtained, if the actual carbon emission data is abnormal and the number of continuous abnormalities is equal to an abnormality determination threshold, a carbon emission prediction model parameter updating program is started; According to the carbon emission prediction model parameter updating program, the type of the parameter of the first prediction module that needs to be corrected is determined; According to the type of the parameter, the connection weight and the bias parameter in the first prediction module are updated to generate a second prediction module; Verification errors based on the second prediction module are obtained, and it is determined whether the verification errors are abnormal, if not, the carbon emission prediction model is updated according to the second prediction module.

6. The artificial intelligence based carbon management method for wastewater treatment plant as claimed in claim 5 wherein, The step of obtaining verification errors based on the second prediction module and determining whether the verification errors are abnormal, if not, updating the carbon emission prediction model according to the second prediction module further comprises: Obtaining the process unit operating state data applying the updated carbon emission prediction model; Comparing the process unit operating state data with historical data to determine the energy consumption level and the emission trend curve of the process unit; If it is detected that the continuous three emission trend curves present an upward state, the corresponding aeration amount adjustment scheme and reflux ratio control scheme are retrieved from the dynamic parameter regulation strategy library to obtain the process parameter set value of the process unit; Dissolved oxygen concentration fluctuation values based on the process parameter set value are obtained, and the carbon control state is determined according to the dissolved oxygen concentration fluctuation values; If the carbon control state meets the preset carbon control condition, the process parameter set value of the process unit is kept unchanged.

7. An artificial intelligence-based carbon management system for wastewater treatment plants for implementing the method of any one of claims 1-6, characterized by, It comprises: A model construction module constructs a carbon emission prediction model according to collected real-time monitoring data, historical data and a mechanism model, wherein the real-time monitoring data includes water condition fluctuation data and process unit operating state data; the water condition fluctuation data includes water quantity fluctuation data and water quality fluctuation data; the historical data includes historical water condition fluctuation sequence, historical process unit operating parameters and historical carbon emission amount; An instruction generation module generates a parameter optimization trigger instruction based on the processing load data and the water condition change rate output by the carbon emission prediction model, and generates a process unit operating parameter configuration scheme based on water condition according to the parameter optimization trigger instruction; A strategy construction module acquires a first carbon emission prediction value corresponding to the process unit operating parameter configuration scheme, and establishes a dynamic parameter regulation strategy library; An instruction execution module matches real-time water condition fluctuation data with the dynamic parameter regulation strategy library and the mechanism model, determines a real-time parameter regulation strategy, and executes a device parameter adjustment instruction according to the real-time parameter regulation strategy.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores an artificial intelligence-based sewage treatment plant carbon management program, and the artificial intelligence-based sewage treatment plant carbon management program, when executed by the processor, implements the method of any one of claims 1-6.

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