Modular farm wastewater treatment station operation management method and system

Abstract

The application provides a modularized farm sewage treatment station operation management method and system, collects operation parameters, water quality parameters and water inflow load parameters of each treatment module, constructs digital twin unit models of the treatment modules and networking, forms an overall digital twin model to realize virtual-real mapping and collaborative visualization; based on water quality and quantity prediction and historical optimization data, a multi-objective optimization algorithm is used to determine a set of collaborative operation parameters of each module that is self-adaptive to water inflow load; the operation state of each module is monitored in real time, and the overall efficiency deviation is calculated; when the efficiency deviation exceeds the threshold or the single module effluent continuously exceeds the standard, a hierarchical adjustment instruction is generated and linked to control. The application can realize intelligent collaborative operation management and self-adaptive optimization control of the modularized farm sewage treatment station.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a modular operation and management method and system for a wastewater treatment plant in a livestock farm. Background Technology

[0002] With the large-scale and intensive development of livestock and poultry farming, the amount of wastewater generated by farms continues to increase. This wastewater is characterized by high concentrations of organic matter, high ammonia nitrogen content, and large fluctuations in water quantity and quality. Improper treatment can cause serious pollution to the surrounding water environment. Modular farm wastewater treatment plants have been widely used in the treatment of wastewater from small and medium-sized farms due to their advantages such as short construction period, flexible deployment, and rapid expansion according to the treatment scale.

[0003] Currently, the operation and management of modular wastewater treatment plants mainly adopt two approaches. The first approach is a manual monitoring mode based on fixed process parameters. This mode is simple to implement but cannot adapt to the significant fluctuations in influent water quality and quantity caused by changes in the breeding cycle, manure removal methods, and seasons in livestock farms. This can easily lead to unstable treatment effects and effluent exceeding standards. At the same time, the long-term fixed operating parameters also result in high energy and chemical consumption, leading to high operating costs. The second approach is an automatic control mode based on single parameter feedback, such as adjusting aeration intensity according to dissolved oxygen concentration. Although this method can achieve closed-loop control of a single process parameter, each treatment module operates independently and lacks a collaborative optimization mechanism. It is difficult to achieve optimal overall treatment efficiency at the system level, and the response to sudden changes in influent load is lagging, which can easily cause shocks to the treatment system and fluctuations in effluent water quality. Therefore, how to achieve intelligent collaborative operation management and adaptive optimization control of modular livestock farm wastewater treatment plants has become a challenge for the industry. Summary of the Invention

[0004] Based on this, the present invention provides a method and system for the operation and management of modular farm wastewater treatment plants that can realize intelligent collaborative operation management and adaptive optimization control.

[0005] In a first aspect, the present invention provides a method for the operation and management of a modular livestock farm wastewater treatment plant, comprising the following steps: Collect operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant; Construct digital twin unit models of each processing module and network them to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module; Based on the predicted results of influent water quality and quantity and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of collaborative operation parameters for each treatment module to be adaptively adjusted according to the influent load. Real-time monitoring of the effluent quality and operating status of each module to determine the current overall operating status of the wastewater treatment plant and its performance deviation from the historical best benchmark. When the performance deviation exceeds the preset performance threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period, a graded operation adjustment command is generated and each processing module is controlled in conjunction with the operation.

[0006] In some embodiments, collecting the operating parameters, water quality parameters, and influent load parameters of each treatment module in a modular livestock farm wastewater treatment plant specifically includes: Water quality sensors and operating status sensors are deployed at the inlet, outlet and key process nodes of each treatment module. By periodically collecting detection data from each sensor through an industrial fieldbus, the operating parameters, water quality parameters, and influent load parameters of each processing module are obtained.

[0007] In some embodiments, digital twin unit models of each processing module are constructed and networked to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module. Specifically, this includes: Based on the technological principles and equipment parameters of each processing module, digital twin unit models of the corresponding processing modules are constructed. Based on the actual process flow and hydraulic connection relationship of the sewage treatment plant, the digital twin unit models are networked together to form an overall digital twin model of the sewage treatment plant. The collected operating parameters and water quality parameters are mapped to the corresponding digital twin unit models in real time, driving the operation of the overall digital twin model and realizing the virtual-real mapping and collaborative visualization of the operating status of each module.

[0008] In some embodiments, based on influent water quality and quantity prediction results and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of collaborative operation parameters for each treatment module to adaptively adjust with the influent load. Specifically, this includes: Based on historical water inflow data and the production rhythm of aquaculture farms, a time-series prediction algorithm is used to predict the water quality and quantity of water inflow in future periods, and the water inflow load prediction results are obtained. Extract historical operating condition data and their corresponding optimal operating parameters that are similar to the current water inflow load prediction results from the historical optimized operation database; With treatment effect, operating energy consumption and reagent consumption as optimization objectives, a multi-objective optimization algorithm is used to coordinately optimize the operating parameters of each treatment module, resulting in a set of coordinated operating parameters that adaptively adjust with the influent load.

[0009] In some embodiments, real-time monitoring of the effluent quality and operating status of each module to determine the performance deviation of the current overall operating status of the wastewater treatment plant relative to the historical best baseline specifically includes: Real-time acquisition of effluent water quality indicators and operating status parameters of each processing module; Extract the historical best benchmark data that matches the current operating conditions from the historical best operating database; Based on the processing efficiency weight of each module and the deviation of the current measured data from the historical best benchmark, the efficiency deviation of the overall operation status of the sewage treatment plant is calculated.

[0010] In some embodiments, when the performance deviation exceeds a preset performance threshold or the effluent index of a single module exceeds the corresponding discharge standard within a continuous monitoring period, generating a graded operation adjustment command and coordinating the control of each processing module specifically includes: Continuously monitor whether the performance deviation exceeds the preset performance threshold, and monitor whether the effluent indicators of each treatment module exceed the corresponding discharge standards within the continuous monitoring period; When any monitoring condition is met, the corresponding control level is determined based on the type and severity of the triggering condition; Based on the control level, an operation adjustment instruction containing specific parameter adjustment amounts is generated, and the operation adjustment instruction is sent to the execution mechanism of the corresponding processing module to realize the linkage control of each module.

[0011] In some embodiments, the processing module includes a pretreatment module, an anaerobic treatment module, an aerobic treatment module, an advanced treatment module, and a sludge treatment module.

[0012] In some embodiments, the operating parameters include aeration intensity, return ratio, sludge concentration, reagent dosage, and hydraulic retention time; the water quality parameters include chemical oxygen demand (COD), ammonia nitrogen concentration, total phosphorus concentration, suspended solids concentration, and dissolved oxygen concentration; and the influent load parameters include influent flow rate, influent COD concentration, and influent ammonia nitrogen concentration.

[0013] In some embodiments, the performance deviation comprehensively considers the degree of deviation of each treatment module in three dimensions: the effluent compliance rate, the unit water treatment energy consumption, and the reagent consumption rate.

[0014] Secondly, the present invention provides a modular operation and management system for a livestock farm wastewater treatment plant, comprising: The data acquisition module is used to collect the operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant. The modeling module is used to construct digital twin unit models of each processing module and network them to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module; The optimization module is used to dynamically determine the set of collaborative operating parameters for each treatment module to adapt to the influent load based on the predicted results of influent water quality and quantity and historical optimization operation data, using a multi-objective optimization algorithm. The evaluation module is used to monitor the effluent quality and operating status of each module in real time, and to determine the performance deviation of the current overall operating status of the sewage treatment plant from the historical best benchmark. The control module is used to generate graded operation adjustment instructions and coordinate the control of each processing module when the performance deviation exceeds the preset performance threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period.

[0015] Compared with the prior art, the present invention has the following advantages: The modular livestock farm wastewater treatment plant operation management method and system provided by this invention first collects the operating parameters, water quality parameters, and influent load parameters of each treatment module of the modular livestock farm wastewater treatment plant. This step enables standardized and periodic collection of multi-dimensional operating data throughout the entire wastewater treatment plant process, providing a comprehensive and reliable data foundation for subsequent modeling, optimization, and control, ensuring that the system can perceive the operating status of each treatment module and changes in influent load in real time. Secondly, digital twin unit models of each treatment module are constructed and networked to form an overall digital twin model of the wastewater treatment plant, thereby achieving virtual-real mapping and collaborative visualization of the operating status of each module. This step utilizes digital twin technology to create a precise virtual mirror of the physical wastewater treatment plant, enabling intuitive display and collaborative monitoring of the operational status of each treatment module. This allows operation and management personnel to grasp the overall operational situation from a system-level perspective, while also providing model support for subsequent simulation optimization and virtual debugging. Then, based on influent water quality and quantity prediction results and historical optimized operational data, a multi-objective optimization algorithm is used to dynamically determine the collaborative operational parameter set for each treatment module, which adaptively adjusts with the influent load. This step can predict changes in water quality and quantity in advance through influent load prediction, and combines historical optimization experience with the multi-objective optimization algorithm to consider multiple dimensions such as treatment effect, operating energy consumption, and reagent consumption. The system collaboratively optimizes the operating parameters of each module to achieve adaptive dynamic adjustment of operating parameters according to the influent load. This overcomes the drawbacks of fixed-parameter operating modes that cannot adapt to fluctuations in water quality and quantity, effectively reducing operating costs while ensuring effluent meets standards. Next, the system monitors the effluent quality and operating status of each module in real time to determine the deviation of the overall operating status of the wastewater treatment plant from its historical best performance. This step can quantitatively assess the overall operating efficiency of the wastewater treatment plant by constructing a multi-dimensional performance evaluation system, promptly identifying deterioration trends in system operation, and achieving early detection and warning of operational anomalies, avoiding problems caused by incomplete monitoring of single parameters. The system performance deteriorates; finally, when the efficiency deviation exceeds the preset efficiency threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period, a graded operation adjustment command is generated and the treatment modules are linked for control. This step can achieve graded response based on dual monitoring criteria, and quickly restore the system operation efficiency through the linkage control of each treatment module. It can not only cope with the gradual anomaly of overall efficiency deterioration, but also handle the sudden problem of single module effluent exceeding the standard, thereby ensuring the stable, efficient and economical operation of the sewage treatment plant. In summary, the solution of the present invention can realize intelligent collaborative operation management and adaptive optimization control of modular livestock farm sewage treatment plants. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1This is an exemplary flowchart of a modular livestock farm wastewater treatment plant operation and management method according to some embodiments of the present invention; Figure 2 This is a schematic diagram illustrating an application scenario of a modular livestock farm wastewater treatment plant operation and management system according to some embodiments of the present invention; Figure 3 This is a schematic flowchart illustrating the determination of performance deviation according to some embodiments of the present invention; Figure 4 This is a schematic diagram of the operation and management system of a modular farm wastewater treatment plant according to some embodiments of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a modular livestock farm wastewater treatment plant operation and management method, as shown in some embodiments of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] The present invention will be further described in detail below with reference to the accompanying drawings.

[0019] refer to Figure 1 The figure is an exemplary flowchart of a modular livestock farm wastewater treatment plant operation and management method according to some embodiments of the present invention. This method mainly includes the following steps: In step 101, the operating parameters, water quality parameters, and influent load parameters of each treatment module of the modular farm wastewater treatment station are collected.

[0020] In practical implementation, the collection of operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant can be achieved in the following way: First, at the influent, effluent, and key process nodes of the pretreatment module, anaerobic treatment module, aerobic treatment module, advanced treatment module, and sludge treatment module of the modular livestock farm wastewater treatment plant, corresponding water quality sensors and operating status sensors are deployed respectively; among them, for the pretreatment module, an electromagnetic flow meter and an online chemical oxygen demand (COD) analyzer are installed at its influent channel to collect influent flow rate and influent COD concentration, and at the bar screen and regulating... Liquid level sensors and operational status monitoring devices are installed at the tank to collect data on equipment operation status and liquid level. For the anaerobic treatment module, online ammonia nitrogen analyzers and chemical oxygen demand (COD) analyzers are installed at its inlet and outlet ends, respectively, to collect ammonia nitrogen and COD concentrations in the influent and effluent. Temperature sensors and sludge concentration meters are installed on the anaerobic reactor to collect temperature and sludge concentration within the reactor. For the aerobic treatment module, dissolved oxygen sensors, sludge concentration meters, and oxidation-reduction potential (ORP) sensors are installed in its aeration tank to collect dissolved oxygen concentration, mixed liquor sludge concentration, and ORP. A temperature sensor and a sludge concentration meter are installed at its outlet end. Chemical oxygen demand (COD) analyzers and ammonia nitrogen analyzers are used to collect effluent water quality indicators. Current sensors and flow sensors are installed on the aeration blowers and return pumps to collect aeration intensity and return ratio. For the advanced treatment module, flow sensors and level sensors are installed on its dosing device to collect reagent dosage and tank level. A total phosphorus analyzer and suspended solids meter are installed at the effluent outlet to collect effluent total phosphorus concentration and suspended solids concentration. For the sludge treatment module, level sensors and operational status monitoring devices are installed on its sludge thickening tank and dewatering machine to collect sludge level and equipment operating status. Then, All sensors are connected to the field control unit and periodically collect detection data from each sensor via PROFINET or Modbus industrial fieldbus according to a set sampling period, such as five minutes. Finally, the collected data are classified and organized according to the processing modules to form a complete dataset containing operating parameters, water quality parameters, and influent load parameters. This dataset is then stored in the field data acquisition server, awaiting subsequent transmission and processing. This completes the acquisition of operating parameters, water quality parameters, and influent load parameters for each processing module. Other methods can also be used in other embodiments, which are not limited here.

[0021] It should be noted that the treatment modules in this invention refer to a combination of units with independent treatment functions, divided according to the wastewater treatment process flow. Each treatment module corresponds to a major treatment stage in the wastewater treatment process, and the modules are hydraulically connected to form a complete treatment system. The operating parameters are parameters that characterize the operating status and process operating conditions of each treatment module equipment, and are used to reflect the intensity and level of process control. The water quality parameters are parameters that characterize the influent and effluent water quality of each treatment module, and are used to measure the treatment effect and effluent quality. The influent load parameters are parameters that characterize the amount of wastewater entering the wastewater treatment plant and the concentration level of pollutants, and are used to reflect the treatment load that the system can withstand.

[0022] In some embodiments, reference Figure 2 As shown in the figure, this diagram illustrates an application scenario of the modular livestock farm wastewater treatment plant operation and management system according to some embodiments of the present invention. The figure includes three main components: on-site data acquisition equipment, edge computing server, and cloud data storage equipment. The on-site data acquisition equipment is responsible for collecting the operating parameters, water quality parameters, and influent load parameters of each treatment module of the modular livestock farm wastewater treatment plant, and sending the collected data to the edge computing server through an industrial communication network. The wastewater treatment plant operation and management system runs on the edge computing server, which stores the processing results and historical data in the cloud data storage equipment and performs remote visual monitoring and management.

[0023] In step 102, digital twin unit models of each processing module are constructed and networked to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module.

[0024] In some embodiments, constructing digital twin unit models of each processing module and networking them to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module, can be achieved by the following steps: Based on the technological principles and equipment parameters of each processing module, digital twin unit models of the corresponding processing modules are constructed. Based on the actual process flow and hydraulic connection relationship of the sewage treatment plant, the digital twin unit models are networked together to form an overall digital twin model of the sewage treatment plant. The collected operating parameters and water quality parameters are mapped to the corresponding digital twin unit models in real time, driving the operation of the overall digital twin model and realizing the virtual-real mapping and collaborative visualization of the operating status of each module.

[0025] In specific implementation, the digital twin unit model of each treatment module can be constructed according to the process principle and equipment parameters of each treatment module in the following way: For each treatment module, firstly, the equipment specifications, process design parameters, and historical operating data of the module are collected. For example, for the aerobic treatment module, data such as the effective volume of the aeration tank, the oxygen transfer efficiency of the aerator, the rated air volume of the blower, and the historical dissolved oxygen variation are collected. Then, based on the dynamic model and fluid mechanics principle of the activated sludge process, a mechanism simulation model of the treatment module is constructed. This model can simulate and calculate the changes in effluent quality indicators and internal state parameters according to the influent water quality, water quantity, and operating parameter inputs. Next, the three-dimensional geometric model of the treatment module and the three-dimensional model of the equipment are combined to construct a visualized three-dimensional scene. Finally, the mechanism simulation model and the three-dimensional visualized scene are integrated to form the digital twin unit model of the treatment module. This model can perform both numerical simulation of the process and three-dimensional visualization. Other methods can also be used in other embodiments, which are not limited here.

[0026] In practical implementation, the digital twin unit models are networked and connected according to the actual process flow and hydraulic connection relationships of the wastewater treatment plant to form an overall digital twin model of the wastewater treatment plant. This can be achieved in the following way: First, the actual process flow of the wastewater treatment plant is analyzed to determine the hydraulic connection sequence and material flow relationship between each treatment module. For example, wastewater sequentially passes through the pretreatment module, anaerobic treatment module, aerobic treatment module, and advanced treatment module, while sludge is discharged from each treatment module into the sludge treatment module. Then, standardized data input and output interfaces are defined for each digital twin unit model. The input interface is used to receive data from the upstream module. Water data and the operating parameters of this module are used to output the effluent data and internal status data of this module. Next, according to the process flow sequence, the output interface of the previous module is connected to the input interface of the next module, and the sludge discharge interface of each treatment module is connected to the input interface of the sludge treatment module. Finally, through the interconnection of interfaces, all digital twin unit models are integrated into an organic whole to form a whole digital twin model of the wastewater treatment plant. This whole model can simulate the complete treatment process of the entire wastewater treatment plant from influent to effluent. Other methods can also be used in other embodiments, which are not limited here.

[0027] It should be noted that the digital twin unit model in this invention refers to a high-fidelity simulation model constructed in virtual space that corresponds one-to-one with a single physical processing module. It can simulate the process of the module, reflect its operating status, and provide a three-dimensional visualization. The overall digital twin model is an integrated virtual model corresponding to the entire physical wastewater treatment plant, which is formed by networking and connecting multiple digital twin unit models according to the actual process flow. It can simulate the entire process and achieve collaborative visualization of the overall operating status.

[0028] In specific implementation, the collected operating parameters and water quality parameters are mapped in real time to the corresponding digital twin unit models to drive the overall digital twin model. This virtual-to-real mapping and collaborative visualization of the operating status of each module can be achieved in the following way: First, the operating parameters and water quality parameters of each processing module collected on-site are received through the data access layer, and the data format is converted and outlier filtering is performed. Then, according to the module identifier and parameter type of the data source, the real-time data is mapped to the input and state variables of the corresponding digital twin unit models, updating the current operating status of the model. Next, the simulation engine of the overall digital twin model is driven to perform a one-step simulation calculation based on the updated state, pre-simulating... The system status at the next moment is measured. Simultaneously, the 3D visualization engine is driven to update the visual representation of the corresponding equipment and water bodies in the 3D scene based on the real-time status of each digital twin unit model. For example, the color depth of the water in the aeration tank is changed according to the dissolved oxygen concentration, the density and size of the bubbles are changed according to the aeration intensity, and the water level of each pool is changed according to the liquid level. Finally, in the visualization interface of the digital twin model, the real-time operating status of each processing module, key water quality indicators, and the overall process flow are displayed synchronously in the form of a 3D scene combined with a data dashboard. This achieves the virtual-real mapping and collaborative visualization of the operating status of each module. Other methods can also be used in other embodiments, which are not limited here.

[0029] It should be noted that the above steps can establish an accurate virtual mirror of the physical wastewater treatment plant through digital twin technology, enabling intuitive display and collaborative monitoring of the operating status of each treatment module. This allows operation and management personnel to grasp the overall operating status from a system perspective, while also providing model support for subsequent simulation optimization and virtual debugging.

[0030] In step 103, based on the predicted results of influent water quality and quantity and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of collaborative operation parameters for each treatment module to adaptively adjust with the influent load.

[0031] In some embodiments, based on influent water quality and quantity prediction results and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of collaborative operation parameters for each treatment module to adaptively adjust with the influent load. Specifically, this includes: Based on historical water inflow data and the production rhythm of aquaculture farms, a time-series prediction algorithm is used to predict the water quality and quantity of water inflow in future periods, and the water inflow load prediction results are obtained. Extract historical operating condition data and their corresponding optimal operating parameters that are similar to the current water inflow load prediction results from the historical optimized operation database; With treatment effect, operating energy consumption and reagent consumption as optimization objectives, a multi-objective optimization algorithm is used to coordinately optimize the operating parameters of each treatment module, resulting in a set of coordinated operating parameters that adaptively adjust with the influent load.

[0032] It should be noted that the influent load prediction in this invention refers to the estimation of the amount and concentration of sewage entering the sewage treatment plant in the future based on historical data and production patterns. Its function is to predict the changes in the treatment load that the system will bear in advance, and to provide a basis for the advance adjustment of operating parameters. The collaborative operating parameter set refers to the optimal combination of operating parameters of each treatment module obtained after multi-objective optimization. This parameter combination enables the entire sewage treatment system to achieve the comprehensive optimization of operating energy consumption and reagent consumption while meeting the effluent compliance requirements.

[0033] In practice, based on historical influent data and the production rhythm of the aquaculture farm, a time-series prediction algorithm is used to predict the influent water quality and quantity for future periods. The influent load prediction results can be obtained in the following way: First, extract time-series data on influent flow rate, influent chemical oxygen demand (COD) concentration, and influent ammonia nitrogen concentration over a relatively long period from the historical operation database. Simultaneously, collect production rhythm information such as the farm's scale, manure removal cycle, feed feeding patterns, and seasonal variations. Then, preprocess the historical time-series data, including missing value imputation, outlier correction, and normalization. Next, use the preprocessed historical influent data as input features, incorporating the farm's production rhythm information... As an auxiliary feature, a long short-term memory network time-series prediction algorithm is used to construct prediction models for influent flow rate, influent chemical oxygen demand (COD) concentration, and influent ammonia nitrogen concentration, respectively. After training and validating the prediction models using historical data, the trained prediction models are used to predict the influent water quality and quantity for future periods, such as the next 24 hours. Finally, the predicted influent flow rate, COD concentration, and ammonia nitrogen concentration data for each period are integrated to form the influent load prediction result for the future period. This result can reflect the changing trend and peak characteristics of the influent load over a future period. Other methods can also be used in other embodiments, which are not limited here.

[0034] In specific implementation, extracting historical operating condition data and corresponding optimal operating parameters that are similar to the current influent load prediction results from the historical optimization operation database can be achieved in the following way: First, access the historical optimization operation database, which stores the optimal operating parameter combinations, treatment effects, operating energy consumption, and other performance indicators corresponding to different influent load conditions in history; then, based on the current influent load prediction results, calculate the comprehensive similarity between it and each historical operating condition in the database in three dimensions: influent flow rate, influent chemical oxygen demand concentration, and influent ammonia nitrogen concentration; next, sort them according to the comprehensive similarity from high to low, and select several historical operating conditions with the highest similarity as similar operating conditions; finally, extract the operating parameter combinations and performance indicator data corresponding to these similar operating conditions as the initial solution reference and constraint boundary for subsequent multi-objective optimization, thereby accelerating the convergence speed of the optimization process and improving the reliability of the optimization results. Other methods can also be used in other embodiments, which are not limited here.

[0035] In practical implementation, with treatment effect, operating energy consumption, and reagent consumption as optimization objectives, a multi-objective optimization algorithm is used to collaboratively optimize the operating parameters of each treatment module. The resulting set of collaborative operating parameters, adaptively adjusted according to the influent load, can be achieved as follows: First, determine the optimization decision variables, including the aeration intensity and reflux ratio of the aerobic treatment module, the sludge reflux ratio of the anaerobic treatment module, the reagent dosage of the advanced treatment module, and the hydraulic retention time allocation of each module. Then, determine the optimization objectives: the first objective is to maximize the effluent quality compliance rate, i.e., the highest degree to which all effluent indicators meet discharge standards; the second objective is to minimize the energy consumption per unit of water treatment, i.e., the minimum electrical energy consumed per unit volume of wastewater; and the third objective is to minimize the reagent consumption per unit volume of wastewater, i.e., the lowest reagent cost per unit volume of wastewater. Next, set constraints, including the value range constraints of each operating parameter and effluent quality... The constraints of meeting emission standards and ensuring that the treatment capacity does not exceed the rated capacity of the equipment must be met. Subsequently, using the optimal parameters extracted from similar historical operating conditions as the initial population, a non-dominated sorting genetic algorithm is used to solve the multi-objective optimization problem. Through continuous iterative evolution, a set of Pareto optimal solutions is obtained, and each optimal solution corresponds to a set of operating parameter combinations. Finally, based on the current operating strategy preference, such as prioritizing energy saving or prioritizing ensuring effluent stability, a compromise optimal solution is selected from the Pareto optimal solution set. The operating parameter combination of each treatment module corresponding to this solution is the collaborative operating parameter set. This collaborative operating parameter set is associated and stored with the corresponding influent load prediction results to form an optimized operation record. As historical data accumulates, the historical optimized operation database is continuously enriched, thereby realizing the adaptive dynamic adjustment of the operating parameters of each treatment module according to the influent load. Other methods can also be used in other embodiments, which are not limited here.

[0036] It should be noted that the above steps can predict changes in water quality and quantity in advance by forecasting the influent load, and combine historical optimization experience with multi-objective optimization algorithms to coordinately optimize the operating parameters of each module from multiple dimensions such as treatment effect, operating energy consumption and reagent consumption. This enables the operating parameters to be adaptively and dynamically adjusted according to the influent load, overcoming the drawbacks of fixed parameter operating mode being unable to adapt to fluctuations in water quality and quantity, and effectively reducing operating costs while ensuring that the effluent meets the standards.

[0037] In step 104, the effluent quality and operating status of each module are monitored in real time to determine the efficiency deviation of the current overall operating status of the sewage treatment plant from the historical best benchmark.

[0038] In some embodiments, real-time monitoring of the effluent quality and operating status of each module, and determination of the performance deviation of the current overall operating status of the wastewater treatment plant from the historical best baseline, can be achieved through the following steps: Real-time acquisition of effluent water quality indicators and operating status parameters of each processing module; Extract the historical best benchmark data that matches the current operating conditions from the historical best operating database; Based on the processing efficiency weight of each module and the deviation of the current measured data from the historical best benchmark, the efficiency deviation of the overall operation status of the sewage treatment plant is calculated.

[0039] In some embodiments, reference Figure 3 As shown in the figure, this is a flowchart illustrating the process of determining the performance deviation in some embodiments of the present invention. In this embodiment, determining the performance deviation of the current overall operating status of the wastewater treatment plant relative to the historical best benchmark can be achieved through the following steps: In step 1041, the effluent water quality indicators and operating status parameters of each processing module are acquired in real time. In step 1042, historical best benchmark data matching the current operating conditions is extracted from the historical best operating database; In step 1043, the efficiency deviation of the overall operation of the wastewater treatment plant is calculated based on the processing efficiency weight of each module and the deviation of the current measured data from the historical best benchmark.

[0040] It should be noted that the efficiency deviation in this invention is a comprehensive indicator that quantifies the overall deviation of the current wastewater treatment plant's overall operating efficiency from the historical best benchmark. It can reveal the deterioration trend of the system's operating status and is the core criterion for achieving early warning and proactive control of operational anomalies. The historical best benchmark refers to the values ​​of various performance indicators corresponding to the best overall performance in historical operating records under the same or similar influent load conditions, and serves as a reference benchmark for evaluating the quality of the current operating status.

[0041] In specific implementation, the real-time acquisition of effluent water quality indicators and operating status parameters of each treatment module can be achieved in the following way: From the real-time data acquisition system, acquire the effluent water quality indicators of each treatment module within the latest monitoring period, including the suspended solids concentration of the pretreatment module effluent, the chemical oxygen demand (COD) and ammonia nitrogen (AM) concentration of the anaerobic treatment module effluent, the COD and AM concentration of the aerobic treatment module effluent, and the COD, AM, total phosphorus, and suspended solids concentration of the deep treatment module effluent; simultaneously acquire the operating status parameters of each module, including aeration intensity, return ratio, sludge concentration, reagent dosage, and the operating status of each device; align and organize these data according to timestamps to form a dataset of the operating status of each treatment module at the current moment, which is used for subsequent performance evaluation. Other methods can also be used in other embodiments, and are not limited here.

[0042] In specific implementation, extracting historical best benchmark data matching the current operating condition from the historical best operating database can be achieved in the following way: First, determine the current influent load operating condition level based on the current influent flow rate, influent chemical oxygen demand concentration, and influent ammonia nitrogen concentration; then, access the historical best operating database, which stores historical best operating records for each level according to the influent load operating condition level. Each record contains data such as corresponding effluent water quality indicators, operating energy consumption, reagent consumption, and operating parameters; next, based on the current operating condition level, retrieve and extract the historical best operating record with the highest comprehensive efficiency ranking under that operating condition level from the database. The performance index values ​​in this record are used as the historical best benchmark data for the current operating condition; if there is no directly matching historical best record for the current operating condition level, select the best record of the adjacent operating condition level and obtain the appropriate benchmark data through interpolation calculation, thereby ensuring that a reasonable historical best benchmark can be obtained under various operating conditions. Other methods can also be used in other embodiments, which are not limited here.

[0043] In practical implementation, the efficiency deviation of the overall operation of the wastewater treatment plant can be calculated based on the processing efficiency weights of each module and the deviation of the current measured data from the historical best benchmark. This can be achieved in the following way: First, determine the three core dimensions of efficiency assessment: effluent compliance rate, unit water treatment energy consumption, and reagent consumption rate. Set corresponding weight coefficients for each dimension. These weight coefficients can be adjusted according to operational management objectives. For example, during periods of strict discharge, the weight of the effluent compliance rate dimension can be increased; during periods of energy conservation and consumption reduction, the weights of the energy consumption and reagent consumption dimensions can be increased. Then, for each dimension, calculate the relative deviation of the current measured value from the historical best benchmark value. The deviation of the effluent compliance rate dimension is calculated based on the magnitude and frequency of each effluent indicator exceeding the discharge standard. The deviation of the energy consumption dimension and the relative deviation of the unit water treatment energy consumption and reagent consumption rate are calculated. The deviation of the reagent dimension is calculated based on the increase ratio of the current value relative to the optimal value. Next, according to the importance of each treatment module in the overall treatment process, a treatment efficiency weight is assigned to each module. For example, the aerobic treatment module and the deep treatment module have higher weights, while the pretreatment module has a lower weight. Subsequently, the deviation of each dimension of each module is weighted and summed to obtain the comprehensive efficiency deviation value of each module. Finally, the comprehensive efficiency deviation values ​​of each module are weighted and summed again according to the module efficiency weights and normalized to obtain a scalar value between zero and one. This scalar value is the efficiency deviation of the overall operating state of the wastewater treatment plant. The larger the value, the more severe the deterioration of the current operating state relative to the historical best level. Other methods can also be used in other embodiments, which are not limited here.

[0044] It should be noted that the above steps can be used to quantitatively evaluate the overall operational efficiency of the wastewater treatment plant by constructing a multi-dimensional performance evaluation system, promptly detect the deterioration trend of the system's operating status, achieve early detection and warning of operational anomalies, and avoid system performance degradation caused by incomplete monitoring of single parameters.

[0045] In step 105, when the performance deviation exceeds the preset performance threshold or the single module effluent index exceeds the corresponding discharge standard within the continuous monitoring period, a graded operation adjustment command is generated and each processing module is controlled in conjunction with the operation.

[0046] In some embodiments, when the performance deviation exceeds a preset performance threshold or the effluent index of a single module exceeds the corresponding discharge standard within a continuous monitoring period, generating a graded operation adjustment command and coordinating the control of each treatment module can be achieved through the following steps: Continuously monitor whether the performance deviation exceeds the preset performance threshold, and monitor whether the effluent indicators of each treatment module exceed the corresponding discharge standards within the continuous monitoring period; When any monitoring condition is met, the corresponding control level is determined based on the type and severity of the triggering condition; Based on the control level, an operation adjustment instruction containing specific parameter adjustment amounts is generated, and the operation adjustment instruction is sent to the execution mechanism of the corresponding processing module to realize the linkage control of each module.

[0047] It should be noted that the performance threshold in this invention serves as a benchmark for triggering overall performance degradation-type regulation. It is used to distinguish between normal fluctuations in the system's operating state and performance degradation that requires intervention. It is a key parameter for the responsiveness and stability of the control system. The graded operation adjustment command refers to the regulation command that is divided into different levels according to the severity of the anomaly. Different levels correspond to different parameter adjustment ranges and response speeds, so as to achieve accurate and appropriate response to anomalies of different degrees.

[0048] In specific implementation, continuously monitoring whether the efficiency deviation exceeds a preset efficiency threshold and whether the effluent indicators of each treatment module exceed the corresponding emission standards within a continuous monitoring cycle can be achieved in the following way: In the real-time monitoring process, two independent monitoring threads run synchronously; the first monitoring thread continuously receives and checks the calculated efficiency deviation value each time, compares it with an efficiency threshold set in advance by analyzing historical operating data, and if the efficiency deviation exceeds the efficiency threshold in several consecutive calculation cycles, it is determined that the trigger condition for overall efficiency degradation is met; the second monitoring thread maintains a continuous exceedance counter based on the monitoring cycle for each effluent indicator of each treatment module. Whenever the measured value of an effluent indicator is determined to exceed its corresponding emission standard limit, the counter is incremented by one. If the counter reaches a preset number, such as exceeding the standard for three consecutive monitoring cycles, it is immediately determined that the trigger condition for single module effluent exceedance is met, and the name of the exceedance indicator, the exceedance range, and the module to which it belongs are recorded for subsequent control decisions. Other methods can also be used in other embodiments, which are not limited here.

[0049] It should be noted that the performance threshold can be preset in the following way: After system deployment or process modification, an initial threshold calibration phase needs to be performed first. In this initial threshold calibration phase, historical operating data over a sufficiently long period is extracted from the historical operating database. Using this historical operating data, the performance deviation values ​​for each time period are calculated retrospectively, thereby forming a performance deviation sample set for normal operating periods and a performance deviation sample set for abnormal operating periods. Subsequently, statistical analysis methods are used, such as calculating the high quantile of the normal sample set and the low quantile of the abnormal sample set, and combining them with... Based on the experience of domain experts, an initial performance threshold is determined from two types of sample sets. This ensures that the performance deviation value exceeds the initial performance threshold during most abnormal periods, while the performance deviation value is lower than the initial performance threshold during most normal operating periods. This initial performance threshold will be recorded in the system configuration library as the initial value of the preset performance threshold. In subsequent operation, the preset performance threshold can be recalculated and updated periodically based on newly accumulated operating data and abnormal cases to achieve continuous optimization of the threshold. Other methods can also be used in other embodiments, which are not limited here.

[0050] In specific implementation, when any monitoring condition is met, the corresponding control level can be determined according to the type and severity of the triggering condition in the following way: Once any of the above monitoring threads triggers the judgment condition, the control decision service is immediately awakened. The control decision service first determines the trigger type, i.e., whether the overall performance deviation exceeds the threshold or the single module's effluent indicators continuously exceed the standard, and assesses the severity of the anomaly. For the case of overall performance degradation triggering, it is divided into three levels: mild degradation, moderate degradation, and severe degradation, corresponding to level one, level two, and level three control, respectively, based on the magnitude of the performance deviation exceeding the threshold. For the case of single module effluent exceeding the standard triggering, it is divided into three levels: slight exceeding the standard, general exceeding the standard, and severe exceeding the standard, based on the number of exceeding indicators, the magnitude of exceeding the standard, and the duration of exceeding the standard, also corresponding to level one, level two, and level three control, respectively. Different control levels correspond to different parameter adjustment strategies and adjustment ranges. The higher the level, the larger the adjustment range and the faster the response speed, thereby realizing graded response according to the severity of the anomaly and avoiding over-control or under-response. Other methods can also be used in other embodiments, which are not limited here.

[0051] In practice, an operational adjustment command containing specific parameter adjustment amounts is generated based on the control level, and this command is sent to the execution mechanism of the corresponding processing module. The coordinated control of each module can be achieved in the following way: After determining the control level, the control decision service first identifies the processing module and specific parameters requiring adjustment based on the trigger type and anomaly source. For example, if the ammonia nitrogen in the effluent from the aerobic treatment module exceeds the standard, the aeration intensity and reflux ratio are adjusted, while simultaneously adjusting the operating parameters of the anaerobic treatment module to reduce the load on the aerobic module. Then, based on the control level and historical optimization experience, the specific adjustment amounts for each relevant parameter are calculated. For example, Level 1 control corresponds to a smaller adjustment range, and Level 3 control corresponds to a larger adjustment range. Finally, the target values ​​for each parameter are... The adjustment rate is encapsulated as a structured operational adjustment command, which includes information such as the adjustment object, target value, adjustment rate, and execution time. Subsequently, the operational adjustment command is sent to the field control unit and actuator of the corresponding processing module through the industrial control network. For example, adjusting the blower frequency converter to change the aeration intensity, adjusting the dosing pump frequency to change the dosage of chemicals, and adjusting the return pump speed to change the return ratio. During the execution of the command, the actual changes of each parameter and the response of the effluent water quality are continuously monitored. The adjustment amount is fine-tuned according to the feedback results to ensure that the control effect is accurate and stable, thereby realizing the coordinated control of each processing module and quickly restoring the normal operating efficiency of the system. Other methods can also be used in other embodiments, which are not limited here.

[0052] It should be noted that the above steps can achieve graded response based on dual monitoring criteria, and quickly restore the system's operating efficiency through the linkage and control of each processing module. This can not only cope with the gradual anomaly of overall efficiency deterioration, but also handle the sudden problem of single module effluent exceeding the standard, thereby ensuring the stable, efficient and economical operation of the sewage treatment plant.

[0053] Furthermore, in another aspect of the present invention, in some embodiments, the present invention provides a modular operation and management system for livestock farm wastewater treatment plants, as referenced. Figure 4 The figure is a schematic diagram of the structure of a modular livestock farm wastewater treatment plant operation and management system according to some embodiments of the present invention. The modular livestock farm wastewater treatment plant operation and management system includes: a data acquisition module 401, a modeling module 402, an optimization module 403, an evaluation module 404, and a control module 405, which are described below: The data acquisition module 401 in this invention is mainly used to collect the operating parameters, water quality parameters and influent load parameters of each treatment module of the modular livestock farm sewage treatment station. Modeling module 402: In this invention, modeling module 402 is mainly used to construct digital twin unit models of each processing module and network them to form an overall digital twin model of the sewage treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module. The optimization module 403 in this invention is mainly used to dynamically determine the set of collaborative operation parameters for each treatment module to adaptively adjust with the influent load based on the predicted results of influent water quality and quantity and historical optimization operation data, using a multi-objective optimization algorithm. Evaluation module 404, in this invention, is mainly used to monitor the effluent water quality and operating status of each module in real time, and to determine the performance deviation of the current overall operating status of the sewage treatment plant from the historical best benchmark. The control module 405 in this invention is mainly used to generate graded operation adjustment instructions and coordinate the control of each processing module when the performance deviation exceeds the preset performance threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period.

[0054] The modules in the aforementioned modular livestock farm wastewater treatment plant operation and management system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0055] In another embodiment, the present invention provides a computer device, which may be an edge computing server, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores historical operating data, optimization parameter data, and real-time monitoring data for the modular livestock farm wastewater treatment plant. The network interface communicates with external field data acquisition devices and a cloud server via a network connection. When the processor executes the computer program, it implements a method for the operation and management of a modular livestock farm wastewater treatment plant.

[0056] Those skilled in the art will understand that Figure 5The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0057] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above embodiment of the modular livestock farm wastewater treatment plant operation and management method.

[0058] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps described in the above embodiment of the modular livestock farm wastewater treatment plant operation and management method.

[0059] In one embodiment, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps described in the embodiment of the modular livestock farm wastewater treatment plant operation and management method.

[0060] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0061] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0062] The technical solutions provided by the embodiments disclosed in this invention have the following beneficial effects: The modular livestock farm wastewater treatment plant operation management method and system provided by this invention first collects the operating parameters, water quality parameters, and influent load parameters of each treatment module of the modular livestock farm wastewater treatment plant. This step enables standardized and periodic collection of multi-dimensional operating data throughout the entire wastewater treatment plant process, providing a comprehensive and reliable data foundation for subsequent modeling, optimization, and control, ensuring that the system can perceive the operating status of each treatment module and changes in influent load in real time. Secondly, digital twin unit models of each treatment module are constructed and networked to form an overall digital twin model of the wastewater treatment plant, thereby achieving virtual-real mapping and collaborative visualization of the operating status of each module. This step utilizes digital twin technology to create a precise virtual mirror of the physical wastewater treatment plant, enabling intuitive display and collaborative monitoring of the operational status of each treatment module. This allows operation and management personnel to grasp the overall operational situation from a system-level perspective, while also providing model support for subsequent simulation optimization and virtual debugging. Then, based on influent water quality and quantity prediction results and historical optimized operational data, a multi-objective optimization algorithm is used to dynamically determine the collaborative operational parameter set for each treatment module, which adaptively adjusts with the influent load. This step can predict changes in water quality and quantity in advance through influent load prediction, and combines historical optimization experience with the multi-objective optimization algorithm to consider multiple dimensions such as treatment effect, operating energy consumption, and reagent consumption. The system collaboratively optimizes the operating parameters of each module to achieve adaptive dynamic adjustment of operating parameters according to the influent load. This overcomes the drawbacks of fixed-parameter operating modes that cannot adapt to fluctuations in water quality and quantity, effectively reducing operating costs while ensuring effluent meets standards. Next, the system monitors the effluent quality and operating status of each module in real time to determine the deviation of the overall operating status of the wastewater treatment plant from its historical best performance. This step can quantitatively assess the overall operating efficiency of the wastewater treatment plant by constructing a multi-dimensional performance evaluation system, promptly identifying deterioration trends in system operation, and achieving early detection and warning of operational anomalies, avoiding problems caused by incomplete monitoring of single parameters. The system performance deteriorates; finally, when the efficiency deviation exceeds the preset efficiency threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period, a graded operation adjustment command is generated and the treatment modules are linked for control. This step can achieve graded response based on dual monitoring criteria, and quickly restore the system operation efficiency through the linkage control of each treatment module. It can not only cope with the gradual anomaly of overall efficiency deterioration, but also handle the sudden problem of single module effluent exceeding the standard, thereby ensuring the stable, efficient and economical operation of the sewage treatment plant. In summary, the solution of the present invention can realize intelligent collaborative operation management and adaptive optimization control of modular livestock farm sewage treatment plants.

[0063] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for the operation and management of a modular livestock farm wastewater treatment plant, characterized in that, Includes the following steps: Collect operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant; Construct digital twin unit models of each processing module and network them to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module; Based on the predicted results of influent water quality and quantity and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of collaborative operation parameters for each treatment module to be adaptively adjusted according to the influent load. Real-time monitoring of the effluent quality and operating status of each module to determine the current overall operating status of the wastewater treatment plant and its performance deviation from the historical best benchmark. When the performance deviation exceeds the preset performance threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period, a graded operation adjustment command is generated and each processing module is controlled in conjunction with the operation.

2. The modular livestock farm wastewater treatment plant operation and management method as described in claim 1, characterized in that, The specific data collected on the operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant include: Water quality sensors and operating status sensors are deployed at the inlet, outlet and key process nodes of each treatment module. By periodically collecting detection data from each sensor through an industrial fieldbus, the operating parameters, water quality parameters, and influent load parameters of each processing module are obtained.

3. The modular livestock farm wastewater treatment plant operation and management method as described in claim 1, characterized in that, Constructing digital twin unit models of each processing module and networking them to form an overall digital twin model of the wastewater treatment plant, thereby realizing the virtual-real mapping and collaborative visualization of the operational status of each module. Specifically, this includes: Based on the technological principles and equipment parameters of each processing module, digital twin unit models of the corresponding processing modules are constructed. Based on the actual process flow and hydraulic connection relationship of the sewage treatment plant, the digital twin unit models are networked together to form an overall digital twin model of the sewage treatment plant. The collected operating parameters and water quality parameters are mapped to the corresponding digital twin unit models in real time, driving the operation of the overall digital twin model and realizing the virtual-real mapping and collaborative visualization of the operating status of each module.

4. The modular livestock farm wastewater treatment plant operation and management method as described in claim 1, characterized in that, Based on the predicted influent water quality and quantity and historical optimized operation data, a multi-objective optimization algorithm is used to dynamically determine the set of coordinated operation parameters for each treatment module to adaptively adjust with the influent load. Specifically, this includes: Based on historical water inflow data and the production rhythm of aquaculture farms, a time-series prediction algorithm is used to predict the water quality and quantity of water inflow in future periods, and the water inflow load prediction results are obtained. Extract historical operating condition data and their corresponding optimal operating parameters that are similar to the current water inflow load prediction results from the historical optimized operation database; With treatment effect, operating energy consumption and reagent consumption as optimization objectives, a multi-objective optimization algorithm is used to coordinately optimize the operating parameters of each treatment module, resulting in a set of coordinated operating parameters that adaptively adjust with the influent load.

5. The modular livestock farm wastewater treatment plant operation and management method as described in claim 1, characterized in that, Real-time monitoring of the effluent quality and operational status of each module is used to determine the overall operational status of the wastewater treatment plant relative to its historical best performance. Specifically, this includes: Real-time acquisition of effluent water quality indicators and operating status parameters of each processing module; Extract the historical best benchmark data that matches the current operating conditions from the historical best operating database; Based on the processing efficiency weight of each module and the deviation of the current measured data from the historical best benchmark, the efficiency deviation of the overall operation status of the sewage treatment plant is calculated.

6. The modular livestock farm wastewater treatment plant operation and management method as described in claim 1, characterized in that, When the performance deviation exceeds a preset performance threshold or the effluent index of a single module exceeds the corresponding discharge standard within a continuous monitoring period, a graded operation adjustment command is generated and the various processing modules are linked and controlled. Specifically, this includes: Continuously monitor whether the performance deviation exceeds the preset performance threshold, and monitor whether the effluent indicators of each treatment module exceed the corresponding discharge standards within the continuous monitoring period; When any monitoring condition is met, the corresponding control level is determined based on the type and severity of the triggering condition; Based on the control level, an operation adjustment instruction containing specific parameter adjustment amounts is generated, and the operation adjustment instruction is sent to the execution mechanism of the corresponding processing module to realize the linkage control of each module.

7. The modular livestock farm wastewater treatment plant operation and management method as described in claim 2, characterized in that, The processing module includes a pretreatment module, an anaerobic treatment module, an aerobic treatment module, an advanced treatment module, and a sludge treatment module.

8. The modular livestock farm wastewater treatment plant operation and management method as described in claim 2, characterized in that, The operating parameters include aeration intensity, return ratio, sludge concentration, reagent dosage, and hydraulic retention time; the water quality parameters include chemical oxygen demand (COD), ammonia nitrogen concentration, total phosphorus concentration, suspended solids concentration, and dissolved oxygen concentration; the influent load parameters include influent flow rate, influent COD concentration, and influent ammonia nitrogen concentration.

9. The modular livestock farm wastewater treatment plant operation and management method as described in claim 5, characterized in that, The performance deviation is comprehensively considered in terms of the deviation of each treatment module in three dimensions: the effluent compliance rate, the unit water treatment energy consumption, and the reagent consumption rate.

10. A modular operation and management system for a livestock farm wastewater treatment plant, characterized in that, include: The data acquisition module is used to collect the operating parameters, water quality parameters, and influent load parameters of each treatment module in the modular livestock farm wastewater treatment plant. The modeling module is used to construct digital twin unit models of each processing module and network them to form an overall digital twin model of the wastewater treatment plant, so as to realize the virtual-real mapping and collaborative visualization of the operating status of each module; The optimization module is used to dynamically determine the set of collaborative operating parameters for each treatment module to adapt to the influent load based on the predicted results of influent water quality and quantity and historical optimization operation data, using a multi-objective optimization algorithm. The evaluation module is used to monitor the effluent quality and operating status of each module in real time, and to determine the performance deviation of the current overall operating status of the sewage treatment plant from the historical best benchmark. The control module is used to generate graded operation adjustment instructions and coordinate the control of each processing module when the performance deviation exceeds the preset performance threshold or the effluent index of a single module exceeds the corresponding emission standard within a continuous monitoring period.

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