A sewage pump station control method and device based on combined regulation of pollution reduction and carbon reduction

Abstract

The present invention relates to the technical field of sewage treatment, and discloses a sewage pump station control method and device based on joint regulation of pollution reduction and carbon reduction, which obtains multidimensional data of the pump station; preprocesses the multidimensional data of the pump station to delete abnormal data; constructs an objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pump station, and establishes target constraints in combination with factors such as the pump station operation time, electricity, and water level; searches for parameter solutions for the operation of the pump station according to a nutcracker optimization algorithm; predicts carbon emissions and sewage discharge capacity using a carbon emission and sewage discharge capacity prediction model based on the searched parameter solutions for the operation of the pump station; and when the comprehensive fitness function value of carbon emissions and sewage discharge capacity is greater than a preset threshold or the number of iterations is greater than a preset number of iterations, outputs the optimal parameter solution for the operation of the pump station as the optimal control strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to a sewage pump station control method and device based on combined regulation of pollution reduction and carbon reduction. Background Art

[0002] Sewage pumping stations, as an important component of the drainage system, play a key role in the sewage transportation process. However, the large amount of carbon emissions generated during the operation of sewage pumping stations has become a major source of greenhouse gases.

[0003] Currently, the operation and management of urban sewage pumping stations primarily rely on manual scheduling based on experience, lacking scientific data support and optimization mechanisms. While some sewage pumping stations are equipped with water monitoring equipment and drainage scheduling models, these primarily focus on optimizing water quantity and quality, lacking a systematic carbon reduction scheduling mechanism. Furthermore, some traditional pumping stations suffer from aging pumps and motors, resulting in low energy efficiency. The pipe network characteristics are poorly matched to the pump operating conditions, further exacerbating energy waste and environmental pollution. Summary of the Invention

[0004] In view of this, the present application provides a method for controlling urban sewage pumping stations based on joint regulation of pollution reduction and carbon reduction, which solves the technical problem that the operation and management of urban sewage pumping stations in the existing technology mainly rely on manual experience scheduling and lack of scientific data support and optimization mechanism.

[0005] According to a first aspect of the present application, a method for controlling an urban sewage pumping station based on combined regulation of pollution reduction and carbon reduction is provided, comprising:

[0006] Acquire multi-dimensional data of the pumping station, including weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data, and pumping station power monitoring data;

[0007] Preprocess the multidimensional data of the pumping station to remove abnormal data;

[0008] The objective function is constructed based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and the target constraint conditions are established by combining factors such as the pumping station operation time, power consumption, and water level. Among them, the objective function for minimizing carbon emission of the pumping station is: , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0009] Search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm;

[0010] Based on the searched parameter solutions of the pump station operation, the carbon emission and sewage discharge capacity prediction model is used to predict the carbon emission and sewage discharge capacity;

[0011] Determine whether the comprehensive fitness function value of the predicted carbon emission and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emission and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, output the optimal parameter solution for the pump station operation as the optimal control strategy, otherwise continue to perform the optimization iterative calculation of the Nutcracker optimization algorithm.

[0012] According to a second aspect of the present application, a control device for an urban sewage pumping station based on combined regulation of pollution reduction and carbon reduction is provided, comprising:

[0013] A data acquisition module is used to acquire multi-dimensional data of the pumping station, wherein the multi-dimensional data of the pumping station includes weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data and pumping station power monitoring data;

[0014] A preprocessing module is used to preprocess the multi-dimensional data of the pumping station to delete abnormal data;

[0015] The initialization module is used to construct the objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and to establish the target constraint conditions based on the pumping station operation time, power, water level and other factors. Among them, the objective function of the minimum carbon emission of the pumping station is , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0016] Parameter optimization module, used to search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm;

[0017] A prediction module is used to predict carbon emissions and sewage discharge capacity based on the searched parameter solutions of the pump station operation and using the carbon emissions and sewage discharge capacity prediction model;

[0018] The output module is used to determine whether the comprehensive fitness function value of the predicted carbon emissions and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emissions and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, the optimal parameter solution for the operation of the pump station is output as the optimal control strategy. Otherwise, the optimization iterative calculation of the Nutcracker optimization algorithm continues.

[0019] According to the third aspect of the present application, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for controlling an urban sewage pumping station based on combined regulation of pollution reduction and carbon reduction are implemented.

[0020] According to the fourth aspect of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the above-mentioned urban sewage pump station control method based on combined regulation of pollution reduction and carbon reduction.

[0021] By means of the above-mentioned technical solution, the present application provides a method, device and equipment for controlling urban sewage pumping stations based on joint regulation of pollution reduction and carbon reduction, which obtains multidimensional data of the pumping station; pre-processes the multidimensional data of the pumping station to delete abnormal data; constructs an objective function based on the minimum carbon emissions and maximum sewage discharge capacity of the pumping station, and establishes target constraints based on factors such as the pumping station operation time, electricity, and water level; searches for parameter solutions for the operation of the pumping station according to the star crow optimization algorithm; predicts carbon emissions and sewage discharge capacity using a carbon emission and sewage discharge capacity prediction model based on the searched parameter solutions for the operation of the pumping station; when the comprehensive fitness function value of carbon emissions and sewage discharge capacity is greater than a preset threshold or the number of iterations is greater than the preset number of iterations, outputs the optimal parameter solution for the operation of the pumping station as the optimal control strategy.

[0022] The above description is only an overview of the technical solution of this application. In order to more clearly understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other purposes, features and advantages of this application more obvious and easy to understand, the specific implementation methods of this application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] Figure 1 A schematic diagram of an application scenario of a method for controlling a municipal sewage pumping station based on combined regulation of pollution reduction and carbon reduction provided in an embodiment of the present application is shown;

[0025] Figure 2A flow chart of a method for controlling an urban sewage pumping station based on combined regulation of pollution reduction and carbon reduction provided in an embodiment of the present application is shown;

[0026] Figure 3 A flow chart of the method for training the emission and discharge capacity prediction model provided in the embodiments of the present application is shown;

[0027] Figure 4 A schematic diagram of the method flow of the optimized Nutcracker optimization algorithm provided in an embodiment of the present application is shown;

[0028] Figure 5 A structural schematic diagram of a city sewage pump station control device based on combined regulation of pollution reduction and carbon reduction provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] The specific implementation of the present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0030] The embodiment of the present invention provides a method for controlling a municipal sewage pump station based on combined regulation of pollution reduction and carbon reduction, which can be applied to Figure 1 In the scenario of the urban sewage pumping station shown, the sewage pumping station automation system consists of an industrial computer and a field control system. The field control system includes an inlet control subsystem and an outlet control subsystem. The inlet control subsystem and the outlet control subsystem are both composed of liquid level sensors and actuators. The industrial computer and the inlet control subsystem are connected to the outlet control subsystem through a field bus, realizing the automation of the entire process from sewage collection, treatment to discharge. A joint regulation based on pollution reduction and carbon reduction is executed through the industrial computer (model training) or the field control system (edge ​​computing can be used for model deployment, local analysis and decision-making, and the control actuator executes the pump station control strategy). A control method for an urban sewage pumping station is proposed, which obtains multidimensional data of the pumping station; preprocesses the multidimensional data of the pumping station to delete abnormal data; constructs an objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and establishes target constraints based on factors such as the pumping station operation time, power consumption, and water level; searches for parameter solutions for the operation of the pumping station according to the Nutcracker optimization algorithm; predicts the carbon emission and sewage discharge capacity using a carbon emission and sewage discharge capacity prediction model based on the searched parameter solutions for the operation of the pumping station; and outputs the optimal parameter solution for the operation of the pumping station as the optimal control strategy when the comprehensive fitness function value of the carbon emission and sewage discharge capacity is greater than a preset threshold or the number of iterations is greater than a preset number of iterations.

[0031] The present invention is described in detail below through specific examples.

[0032] Example 1:

[0033] like Figure 2 As shown in the figure, a method for controlling a municipal sewage pumping station based on combined regulation of pollution reduction and carbon reduction is provided in an embodiment of the present invention, comprising:

[0034] Step 201: Acquire multi-dimensional data of the pumping station;

[0035] Among them, the multi-dimensional data of the pumping station includes weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data and pumping station power monitoring data;

[0036] Step 202: pre-process the multi-dimensional data of the pumping station to delete abnormal data;

[0037] Wherein, step 202 specifically includes:

[0038] Step 202-1: perform integrity check and null value processing on the multi-dimensional data of the pumping station;

[0039] Step 202-2: Detect and clean up abnormal values ​​of the multi-dimensional data of the pumping station;

[0040] Step 202-3: Standardize the multi-dimensional data of the pumping station.

[0041] Step 203: construct an objective function based on minimizing carbon emissions and maximizing sewage discharge capacity of the pumping station, and establish objective constraints based on factors such as pumping station operation time, power consumption, and water level;

[0042] Among them, the minimum objective function of carbon emissions of the pumping station is: , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0043] Step 204: Search for a parameter solution for pump station operation according to the Nutcracker optimization algorithm;

[0044] Among them, before step 204, it is necessary to set various parameters of the Nutcracker optimization algorithm, and the initialization parameters include the upper and lower limits of the parameters. , maximum number of iterations , population size N, population dimension D, first stage search probability , the second stage search probability .

[0045] Step 205: Predicting carbon emissions and sewage discharge capacity using a carbon emissions and sewage discharge capacity prediction model based on the searched pump station operation parameter solution;

[0046] Step 206: Determine whether the value of the comprehensive fitness function of the predicted carbon emissions and pollution discharge capacity is less than a preset threshold;

[0047] Among them, the comprehensive fitness function value is ,in, is the current carbon emission value, For the current sewage disposal capacity, is the optimal reference value for carbon emissions. It is the optimal reference value for sewage discharge capacity.

[0048] Step 207: When the comprehensive fitness function value of carbon emissions and sewage discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, the optimal parameter solution for the pump station operation is output as the optimal control strategy; otherwise, the process is adjusted to step 204 to continue the optimization iterative calculation of the Nutcracker optimization algorithm.

[0049] Among them, the optimal control strategy includes the pump station start and stop time strategy and pump speed adjustment strategy. The output optimal control strategy is, for example, the start and stop time points are 06:00-22:00 every day, and the sewage discharge capacity is 1500m 3 / h, carbon emissions and pollution discharge capacity are balanced and improved at the same time during the optimization process.

[0050] A first embodiment of the present invention provides a method, device, and equipment for controlling an urban sewage pump station based on combined regulation of pollution reduction and carbon reduction, which obtains multidimensional data of the pump station; preprocesses the multidimensional data of the pump station to delete abnormal data; constructs an objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pump station, and establishes target constraints in combination with factors such as the pump station operation time, power, and water level; searches for parameter solutions for the operation of the pump station according to the Nutcracker optimization algorithm; predicts carbon emissions and sewage discharge capacity using a carbon emission and sewage discharge capacity prediction model based on the searched parameter solutions for the operation of the pump station; when the comprehensive fitness function value of carbon emissions and sewage discharge capacity is greater than a preset threshold or the number of iterations is greater than a preset number of iterations, outputs the optimal parameter solution for the operation of the pump station as the optimal control strategy

[0051] Example 2:

[0052] On the basis of Example 1 of the present invention, a step of training the emission and discharge capacity prediction model is added before step 205, such as Figure 3 Shown, including:

[0053] Step 301: Acquire historical multidimensional data of a pumping station as a sample data set;

[0054] Step 302: define the feature number of the input data, select an appropriate number of neurons, set the transfer function of the hidden layer to the logsig function, and set the transfer function of the output layer to the purelin function;

[0055] Step 303: Input the sample data set from the input layer into the carbon emission and pollution discharge capacity prediction model, perform nonlinear mapping in the hidden layer through the logsig activation function, and use the linear function purelin to generate the prediction result;

[0056] Step 304: Compare the prediction result with the target data and calculate the mean square error (MSE).

[0057] Step 305: Use the Levenberg-Marquardt algorithm to perform error back propagation, and adjust the weight and threshold of the model according to the mean square error (MSE).

[0058] The second embodiment of the present invention can adapt to environmental changes and fluctuations in operating conditions through continuous learning and adjustment, maintaining the stability and reliability of predictions. The use of the Levenberg-Marquardt algorithm improves the robustness of the model, enabling it to quickly adjust in the face of data changes.

[0059] Example 3:

[0060] In order to improve the global optimization ability of the algorithm, improve convergence performance, enhance robustness and generalization ability, the parameter solution of the Nutcracker optimization algorithm in step 204, which searches for pump station operation, is optimized and improved based on the dual population mechanism of reinforcement learning. Figure 4 Shown, including:

[0061] Step 401: Initialize the Q-table in the Q-learning algorithm. The table stores the value of each state-action pair, and its initial value is zero.

[0062] In addition to setting the initialization parameters for the nutcracker optimization algorithm, initializing the random positions of the nutcracker population, and setting the initial Q-table for Q-learning with an initial value of 0, the Q-table is a data structure in reinforcement learning (RL) that stores the expected utility or value of an agent taking a specific action in a specific state. The Q-table is a central concept in Q-learning. It records the estimated cumulative reward for each state-action pair, which are often called Q-values. The basic components of a Q-table include the state: the environment or situation in which the agent is located. The action: the possible behavior that the agent can take in that state. The Q-value: represents the expected reward of taking a specific action in a given state. The higher the Q-value, the better the long-term benefits of taking that action in that state.

[0063] Step 402: Sort the population in ascending order according to fitness values. Assign the top 50% of the fitness values ​​to the development population for local search and deep optimization. Assign the remaining individuals to the exploration population for global search in the solution space to find potential new solutions.

[0064] Step 403: Adjust the current position of each individual in the exploration population according to the population mean and the positions of other individuals, introduce random perturbations, and search for potential new solutions;

[0065] The position update formula is: ,in, is the population mean, A and B are randomly selected individuals from the population, is the control parameter.

[0066] Step 404: For each individual in the development population, use the Q-learning reinforcement learning algorithm to adjust the individual position.

[0067] Among them, the state encoding is the local diversity change rate ,in, is the individual diversity measure in the current iteration, It is a measure of individual diversity in the previous generation. The local diversity change rate is used to measure the spatial distribution and diversity level of the population, indicating whether the current search process is concentrated in the local area to avoid falling into the local optimal solution. The fitness change rate is ,in, is the current fitness value, The fitness value of the previous round, and the fitness change rate are used to measure the quality change of the current solution, indicating the convergence speed of the search process and the degree of optimization of the objective function. By encoding and using these two parameters together, the algorithm can dynamically perceive the search status, adjust the search behavior in the development population, and improve the local search accuracy and convergence speed. This mechanism effectively combines the Nutcracker optimization algorithm with the reinforcement learning algorithm, improving the comprehensive performance of global optimization and local development.

[0068] The action selection strategy is to use the SoftMax function to select the storage and recovery strategy as ,in, Choose the probability for the action, in state Select Action The probability of executing an action in this state the possibility of Q value represents the state Next action The expected cumulative reward value that can be obtained is the core parameter of the Q-learning algorithm. The current state represents the current environmental state, which is generally encoded by a combination of features such as the fitness change rate and the local diversity change rate. For action j in state The available actions include strategic operations such as "storing the current position" and "restoring to the best historical solution". n is the size of the action space, the number of different strategies to choose from, and the dimension of the action space.

[0069] The storage policy is ,in, The updated individual position represents the position vector of individual i in the next iteration (t+1 round), The current individual position represents the position vector of individual i in the current iteration (round t), To control the step size of the current solution approaching the optimal solution, it is used to balance the search intensity and accuracy. The position of the individual with the best fitness found in the current population is used as the reference target point. represents a random number used to introduce disturbances to avoid premature convergence. It is a random number between 0 and 1 used to enhance search diversity. The positions of two different individuals randomly selected from the population serve as reference points for generating random differences.

[0070] The recovery strategy is ,in, The updated individual position represents the position vector of individual i in the next iteration (t+1 round); Alternative position 1 is the first alternative position in the recovery strategy, which is a potential better position found by individual i in local search or historical records; The second alternative position in the recovery strategy is alternative position 2, which is the alternative solution found by individual i in another search path or other strategy; The fitness value of the first candidate position represents the candidate position Evaluation results in the fitness function; The fitness value of the second candidate position represents the candidate position Evaluation results in the fitness function; The current individual fitness value represents the fitness value of individual i in the current solution space position, which is one of the decision-making bases.

[0071] Reward and Q table update, where the reward is ,in, The reward value represents the immediate reward obtained by individual i after performing the current action, which measures the effectiveness of the action in improving fitness; The fitness value for the next round represents the fitness function value of individual i after the position is updated; The current fitness value represents the fitness function value of individual i at the current position.

[0072] The Q table is updated to , where is the current Q value indicating the current state Select Action Expected cumulative rewards; The learning rate controls the amplitude of Q value updates, ranging from 0<α≤1. A larger value indicates a more sensitive update to new information. For immediate rewards, it means the current state Execute an action Immediate rewards after The discount factor measures the degree of influence of future rewards on current decisions, ranging from 0≤γ≤1. A larger value indicates a greater emphasis on long-term returns. The optimal Q value for the future is expressed in the next state The maximum Q value of all possible actions a represents the optimal return expectation in the future; The current state indicates the current state The action selected next may be storage, recovery, or other strategic behavior; The next state represents the execution of the action After that, the system transfers to the new environment state.

[0073] Example 3 of the present invention proposes a dual-population mechanism based on reinforcement learning, which divides the original population into exploration sub-populations and utilization sub-populations according to the fitness values ​​of individuals to better balance the global exploration and local development capabilities. An improved foraging strategy based on randomized opposition learning is designed for the exploration sub-population to enhance diversity. Q-learning is used as an adaptive selector to optimally adjust the behavior of the utilization sub-population in different problems. The state is encoded using the individual fitness value and the relative change of local diversity, and the action is selected according to the Q table value to dynamically optimize the benefits at different stages. Reinforcement learning is combined with the nutcracker optimization algorithm, and the exploration and utilization strategies are dynamically selected through RL to improve the global optimization capability of the algorithm.

[0074] Further, as Figures 2 to 4 The specific implementation of the method, the embodiment of the present invention provides a city sewage pump station control device based on pollution reduction and carbon reduction combined regulation, such as Figure 5 As shown, the device includes:

[0075] The data acquisition module 510 is used to acquire multi-dimensional data of the pump station, wherein the multi-dimensional data of the pump station includes weather conditions, rainfall, pump station water level monitoring data, pump station flow monitoring data, pump station water quality monitoring data, and pump station power monitoring data;

[0076] A pre-processing module 520 is used to pre-process the multi-dimensional data of the pumping station to delete abnormal data;

[0077] Initialization module 530 is used to construct an objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pump station, and to establish target constraints based on factors such as pump station operation time, power consumption, and water level. The objective function for the minimum carbon emission of the pump station is: , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0078] a parameter optimization module 540 for searching for parameter solutions for pump station operation according to a Nutcracker optimization algorithm;

[0079] Prediction module 550, for predicting carbon emissions and sewage discharge capacity based on the searched pump station operation parameter solution and using the carbon emissions and sewage discharge capacity prediction model;

[0080] Output module 560 is used to determine whether the comprehensive fitness function value of the predicted carbon emissions and pollution discharge capacity is less than a preset threshold. When the comprehensive fitness function value of the carbon emissions and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, the optimal parameter solution for the operation of the pump station is output as the optimal control strategy. Otherwise, the optimization iterative calculation of the Nutcracker optimization algorithm continues.

[0081] An embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for controlling an urban sewage pumping station based on combined regulation of pollution reduction and carbon reduction are implemented, including:

[0082] Acquire multi-dimensional data of the pumping station, including weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data, and pumping station power monitoring data;

[0083] Preprocess the multidimensional data of the pumping station to remove abnormal data;

[0084] The objective function is constructed based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and the target constraint conditions are established by combining factors such as the pumping station operation time, power consumption, and water level. Among them, the objective function for minimizing carbon emission of the pumping station is: , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0085] Search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm;

[0086] Based on the searched parameter solutions of the pump station operation, the carbon emission and sewage discharge capacity prediction model is used to predict the carbon emission and sewage discharge capacity;

[0087] Determine whether the comprehensive fitness function value of the predicted carbon emission and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emission and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, output the optimal parameter solution for the pump station operation as the optimal control strategy, otherwise continue to perform the optimization iterative calculation of the Nutcracker optimization algorithm.

[0088] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the following steps are implemented:

[0089] Acquire multi-dimensional data of the pumping station, including weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data, and pumping station power monitoring data;

[0090] Preprocess the multidimensional data of the pumping station to remove abnormal data;

[0091] The objective function is constructed based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station. The target constraint conditions are established by combining factors such as the pumping station operation time, power, and water level. Among them, the objective function for minimizing carbon emission of the pumping station is, and the objective function for maximizing sewage discharge capacity is. The target constraint conditions are: E is the power consumption, G is the carbon emission factor, is the pumping station operation time, S is the pumping station flow, and a is the water quality data. is the water level, is the width, is the length, is the rainfall, duration of rainfall;

[0092] Search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm;

[0093] Based on the searched parameter solutions of the pump station operation, the carbon emission and sewage discharge capacity prediction model is used to predict the carbon emission and sewage discharge capacity;

[0094] Determine whether the comprehensive fitness function value of the predicted carbon emission and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emission and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, output the optimal parameter solution for the pump station operation as the optimal control strategy, otherwise continue to perform the optimization iterative calculation of the Nutcracker optimization algorithm.

[0095] It should be noted that the above embodiments only use the urban sewage treatment pump station as an example to illustrate the principles and implementation steps of the embodiments of the present invention, and do not specifically limit the actual application scenarios. For example, the technical solution of the present invention can also be applied to metering fault prediction of other power grid-related instruments. Regarding the functions or steps that can be implemented by computer-readable storage media or computer devices, please refer to the aforementioned method embodiments. To avoid repetition, they will not be described one by one here.

[0096] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0097] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for controlling a municipal sewage pumping station based on combined regulation of pollution reduction and carbon reduction, characterized in that: include: Acquiring multidimensional data of the pumping station, wherein the multidimensional data of the pumping station includes weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data, and pumping station power monitoring data; Preprocessing the multidimensional data of the pumping station to delete abnormal data; The objective function is constructed based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and the target constraint conditions are established by combining the pumping station operation time, power consumption, and water level. Among them, the objective function of the minimum carbon emission of the pumping station is: , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall; Search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm; Based on the searched parameter solutions of the pump station operation, the carbon emission and sewage discharge capacity prediction model is used to predict the carbon emission and sewage discharge capacity; Determine whether the comprehensive fitness function value of the predicted carbon emission and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emission and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, output the optimal parameter solution for the pump station operation as the optimal control strategy, otherwise continue to perform the optimization iterative calculation of the Nutcracker optimization algorithm.

2. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: The step of pre-processing the multi-dimensional data of the pumping station to delete abnormal data includes: Performing integrity check and null value processing on the multi-dimensional data of the pump station; Detecting and cleaning abnormal values ​​in the multi-dimensional data of the pumping station; The multi-dimensional data of the pumping station is standardized.

3. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: Before the step of searching for a parameter solution for pump station operation according to the Nutcracker optimization algorithm and predicting carbon emissions and sewage discharge capacity using the carbon emissions and sewage discharge capacity prediction model, the step of training the emissions and sewage discharge capacity prediction model is included, specifically including: Obtain historical multidimensional data of the pumping station as a sample data set; Define the number of features of the input data, select the appropriate number of neurons, set the transfer function of the hidden layer to the logsig function, and set the transfer function of the output layer to the purelin function; Input the sample data set from the input layer into the carbon emission and pollution discharge capacity prediction model, perform nonlinear mapping in the hidden layer through the logsig activation function, and use the linear function purelin to generate the prediction result; Compare the predicted results with the target data and calculate the mean square error (MSE); The Levenberg-Marquardt algorithm is used for error back propagation, and the weights and thresholds of the model are adjusted according to the mean square error (MSE).

4. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: Before the step of searching for a parameter solution for pump station operation according to the Nutcracker optimization algorithm and predicting carbon emissions and sewage discharge capacity using an artificial neural network model, the method includes: Preset the initialization parameters required for the iteration of the Nutcracker optimization algorithm, where the initialization parameters include the upper and lower limits of the parameters , maximum number of iterations , population size N, population dimension D, first stage search probability , the second stage search probability .

5. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: The Nutcracker optimization algorithm is used to search for parameter solutions for pump station operation, including: Initialize the Q-table in the Q-learning algorithm, which stores the value of each state-action pair, with its initial value being zero; The population is sorted in ascending order according to the fitness value, and the top 50% of the fitness ranking is assigned to the development population for local search and deep optimization; the remaining individuals are assigned to the exploration population to conduct a global search in the solution space to find potential new solutions; The current position of each individual in the exploration population is adjusted according to the population mean and the positions of other individuals, random perturbations are introduced, and potential new solutions are sought; For each individual in the development population, a Q-learning reinforcement learning algorithm is used to adjust the individual position.

6. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: The comprehensive fitness function value is ,in, is the current carbon emission value, For the current sewage disposal capacity, is the optimal reference value for carbon emissions. It is the optimal reference value for sewage discharge capacity.

7. The urban sewage pump station control method based on pollution reduction and carbon reduction combined regulation according to claim 1 is characterized in that: The optimal control strategy includes a pump station start and stop time strategy and a pump speed adjustment strategy.

8. A control device for a municipal sewage pumping station based on combined regulation of pollution reduction and carbon reduction, characterized in that: include: A data acquisition module is used to acquire multi-dimensional data of the pumping station, wherein the multi-dimensional data of the pumping station includes weather conditions, rainfall, pumping station water level monitoring data, pumping station flow monitoring data, pumping station water quality monitoring data and pumping station power monitoring data; A preprocessing module, configured to preprocess the multidimensional data of the pumping station to delete abnormal data; The initialization module is used to construct the objective function based on the minimum carbon emission and maximum sewage discharge capacity of the pumping station, and to establish the target constraint conditions based on the pumping station operation time, power consumption, and water level. The objective function of the minimum carbon emission of the pumping station is , the maximum objective function of sewage discharge capacity is , the target constraint is , E is electricity consumption, G is carbon emission factor, is the pump station operation time, S is the pump station flow, a is the water quality data, is the water level, is the width, is the length, is the rainfall, duration of rainfall; Parameter optimization module, used to search for parameter solutions for pump station operation based on the Nutcracker optimization algorithm; A prediction module is used to predict carbon emissions and sewage discharge capacity based on the searched parameter solutions of the pump station operation and using the carbon emissions and sewage discharge capacity prediction model; The output module is used to determine whether the comprehensive fitness function value of the predicted carbon emissions and pollution discharge capacity is less than the preset threshold. When the comprehensive fitness function value of carbon emissions and pollution discharge capacity is greater than the preset threshold or the number of iterations is greater than the preset number of iterations, the optimal parameter solution for the operation of the pump station is output as the optimal control strategy. Otherwise, the optimization iterative calculation of the Nutcracker optimization algorithm continues.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the urban sewage pumping station control method based on combined regulation of pollution reduction and carbon reduction as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the urban sewage pumping station control method based on combined regulation of pollution reduction and carbon reduction as described in any one of claims 1 to 7 are implemented.

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