Wastewater treatment method and system based on a pry-mounted AOA pilot plant device

Abstract

The present application discloses a sewage treatment method and system based on a pry-mounted AOA pilot plant, relating to the technical field of sewage treatment, comprising: generating real-time process data sets through real-time sensing; setting denitrification and phosphorus removal targets, analyzing and constructing real-time control instruction sets in combination with real-time process data sets; performing digital twin analysis based on real-time control instruction sets to build a digital twin space, execute real-time control instruction sets, drive the pry-mounted AOA pilot plant to run and analyze to generate multiple running reaction process parameters, and perform inversion to synchronize and correct real-time control instruction sets, dynamically regulate the distribution of backflow sludge in the anaerobic zone and the anoxic zone through electric regulating valves according to the correction instructions, and construct a sewage treatment strategy. The present application solves the technical problems of inaccurate denitrification and phosphorus removal operation control in the pry-mounted AOA sewage treatment process, and difficulty in effective optimization with working condition changes in the prior art, achieving the technical effect of stable and efficient operation of the denitrification and phosphorus removal process.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and more specifically to a wastewater treatment method and system based on a skid-mounted AOA pilot plant. Background Technology

[0002] Existing AOA wastewater treatment devices typically control the anaerobic, anoxic, and aerobic reaction zones using preset operating parameters or manual experience-based adjustments. In actual operation, however, influent water quality and quantity fluctuate significantly, and the biochemical reaction coupling relationships between the various reaction zones are complex. Due to a lack of real-time sensing of the overall operating status and multi-parameter collaborative analysis capabilities, it is difficult to dynamically adjust key operating parameters such as aeration rate, return ratio, and sludge condition in a timely and accurate manner. This results in insufficient precision in the control of nitrogen and phosphorus removal processes, and the operating effect fluctuates significantly with changes in operating conditions, making it difficult to achieve stable and efficient nitrogen and phosphorus removal treatment. Summary of the Invention

[0003] This application provides a wastewater treatment method and system based on a skid-mounted AOA pilot plant, which is used to address the technical problems of inaccurate nitrogen and phosphorus removal operation control and difficulty in effectively optimizing the skid-mounted AOA wastewater treatment process in the prior art.

[0004] In view of the above problems, this application provides a wastewater treatment method and system based on a skid-mounted AOA pilot plant.

[0005] A first aspect of this application provides a wastewater treatment method based on a skid-mounted AOA pilot plant, the method comprising:

[0006] Real-time water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant are used for real-time sensing to generate a real-time process dataset. Based on this dataset, nitrogen and phosphorus removal targets are set. Multi-factor collaborative dynamic analysis is performed according to these targets and the real-time process dataset to construct a real-time control command set. Digital twin analysis is performed based on this command set to build a digital twin space. The execution of the real-time control command set is simulated within this digital twin space to drive the skid-mounted AOA pilot plant in operational analysis, generating multiple operational reaction process parameters. The real-time control command set is then synchronously corrected based on these multiple operational reaction process parameters. According to the correction commands, the distribution of return sludge in the anaerobic and anoxic zones is dynamically controlled via an electric regulating valve to construct a wastewater treatment strategy.

[0007] A second aspect of this application provides a wastewater treatment system based on a skid-mounted AOA pilot plant, the system comprising:

[0008] The system comprises the following modules: a real-time sensing module for real-time sensing via online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant, generating a real-time process dataset; a dynamic analysis module for setting nitrogen and phosphorus removal targets based on the real-time process dataset, performing multi-factor collaborative dynamic analysis based on these targets, and constructing a real-time control instruction set; a digital twin analysis module for performing digital twin analysis based on the real-time control instruction set, building a digital twin space, simulating the execution of the real-time control instruction set based on the digital twin space, driving the skid-mounted AOA pilot plant to perform operational analysis, and generating multiple operational reaction process parameters; and a control module for synchronously correcting the real-time control instruction set based on the multiple operational reaction process parameters, and dynamically controlling the distribution of return sludge in the anaerobic and anoxic zones via an electric regulating valve according to the correction instructions, thus constructing a wastewater treatment strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application utilizes online water quality monitoring sensors deployed in each reaction zone of a skid-mounted AOA pilot plant for real-time sensing, generating a real-time process dataset. Based on this dataset, nitrogen and phosphorus removal targets are set. Multi-factor collaborative dynamic analysis is performed according to these targets and the real-time process dataset to construct a real-time control command set. Digital twin analysis is conducted based on this command set to build a digital twin space. The execution of the real-time control command set is simulated within this digital twin space, driving the skid-mounted AOA pilot plant to perform operational analysis and generating multiple operational reaction process parameters. The real-time control command set is synchronously corrected based on these multiple operational reaction process parameters. According to the correction commands, the distribution of return sludge in the anaerobic and anoxic zones is dynamically controlled via an electric regulating valve, constructing a wastewater treatment strategy. This invention solves the technical problems of inaccurate nitrogen and phosphorus removal operation control and difficulty in effectively optimizing the skid-mounted AOA wastewater treatment process in existing technologies. Through a dynamic control and correction mechanism based on real-time process data and combined with digital twins, it achieves stable and efficient operation of the nitrogen and phosphorus removal process. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1This is a schematic diagram of a wastewater treatment method based on a skid-mounted AOA pilot plant provided in an embodiment of this application.

[0013] Figure 2 A schematic diagram of a wastewater treatment system based on a skid-mounted AOA pilot plant provided in this application embodiment.

[0014] Figure labeling: Real-time sensing module 11, dynamic analysis module 12, digital twin analysis module 13, control module 14. Detailed Implementation

[0015] This application provides a wastewater treatment method and system based on a skid-mounted AOA pilot plant. It addresses the technical problems of inaccurate nitrogen and phosphorus removal operation control and difficulty in effectively optimizing the skid-mounted AOA wastewater treatment process in the prior art. By using a dynamic control and correction mechanism based on real-time process data and combined with digital twins, it achieves the technical effect of stable and efficient operation of the nitrogen and phosphorus removal process.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a wastewater treatment method based on a skid-mounted AOA pilot plant, the method comprising:

[0019] Step S100: Real-time sensing is performed using online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant to generate a real-time process dataset.

[0020] In this embodiment, in the skid-mounted AOA pilot plant, considering the differences in process functions among the anaerobic, aerobic, anoxic, and sedimentation zones, online water quality monitoring sensors are matched, and sensor groups are deployed in the anaerobic, anoxic, aerobic, and sedimentation zones respectively. Under a unified sampling period, based on the instantaneous water quality data collected at the inlet, the sensor groups in each reaction zone are linked to perform real-time sensing and acquisition of the anaerobic phosphorus release environment, denitrification process, nitrification phosphorus uptake status, and solid-liquid separation status. The multi-source monitoring data is time-aligned and bound to construct a real-time process dataset reflecting the overall operating conditions of the skid-mounted AOA pilot plant.

[0021] Furthermore, the method provided in the application embodiments, which generates a real-time process dataset by using online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant for real-time sensing, also includes:

[0022] The skid-mounted AOA pilot plant includes an anaerobic reaction zone, an aerobic reaction zone, an anoxic reaction zone, and a sedimentation zone. Based on online water quality monitoring sensors, sensor matching is performed on the anaerobic reaction zone, the aerobic reaction zone, the anoxic reaction zone, and the sedimentation zone. According to the matching results, anaerobic zone sensor groups are deployed in the anaerobic reaction zone, the anoxic zone, the aerobic reaction zone, and the sedimentation zone. Instantaneous water quality data is collected at the inlet of the skid-mounted AOA pilot plant, and a sampling period is set. The anaerobic zone sensor groups are triggered to perform real-time sensing according to the sampling period. The system obtains real-time data on the anaerobic phosphorus release environment; it triggers the aerobic zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on the nitrification reaction status; it triggers the anoxic zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on the denitrification process; it triggers the sedimentation zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on solid-liquid separation; and it aligns and binds the instantaneous water quality data, the real-time data on the anaerobic phosphorus release environment, the real-time data on the denitrification process, the real-time data on the nitrification reaction status, and the real-time data on solid-liquid separation according to the collection cycle to construct the real-time process dataset.

[0023] In this embodiment, the skid-mounted AOA pilot plant is divided into an anaerobic reaction zone, an aerobic reaction zone, an anoxic reaction zone, and a sedimentation zone according to the biological nitrogen and phosphorus removal reaction process. Different reaction zones correspond to different types of biochemical reactions, and their key control parameters also differ. Based on the above process structure, online water quality monitoring sensors are first matched for the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone. Specifically, in the anaerobic reaction zone, a redox potential sensor is selected to characterize the stability of the anaerobic environment, and a water quality sensor is selected to monitor phosphorus release characteristics. In the anoxic reaction zone, a nitrate nitrogen sensor and a redox potential sensor are selected to monitor nitrate nitrogen changes. In the aerobic reaction zone, a dissolved oxygen sensor is selected to monitor dissolved oxygen concentration, and an ammonia nitrogen sensor is selected to monitor ammonia nitrogen changes. In the sedimentation zone, a turbidity sensor is selected to monitor changes in effluent suspended solids or turbidity. Using the aforementioned sensor matching method, anaerobic sensor groups are deployed in the anaerobic reaction zone, anoxic sensor groups in the hypoxic reaction zone, aerobic sensor groups in the aerobic reaction zone, and sedimentation sensor groups in the sedimentation zone, respectively, to achieve quantitative monitoring of the key operating status of each reaction zone.

[0024] After deploying the sensor groups in each reaction zone, wastewater quality parameters entering the skid-mounted AOA pilot plant are collected via online sampling and real-time detection at the inlet end, forming instantaneous water quality data. This instantaneous water quality data reflects the current load levels of pollutants such as COD, ammonia nitrogen, total nitrogen, and total phosphorus in the influent. Simultaneously, a uniform sampling cycle is set according to the operating rhythm of the skid-mounted AOA pilot plant. This sampling cycle constrains the sampling frequency of the sensor groups at the inlet end and in each reaction zone, thereby ensuring the consistency of multi-source monitoring data over time.

[0025] Under the sampling cycle control, the anaerobic zone sensor group is triggered to perform real-time sensing. By continuously collecting the oxidation-reduction potential and related water quality parameters in the anaerobic reaction zone, real-time data on the anaerobic phosphorus release environment status that can reflect whether the phosphorus release reaction conditions in the anaerobic reaction zone meet the requirements for biological phosphorus release are obtained, which is used to characterize the operating status of the phosphorus release reaction in the anaerobic reaction zone.

[0026] During the sampling period, the aerobic zone sensor group is synchronously triggered to perform real-time sensing. By continuously collecting dissolved oxygen concentration and ammonia nitrogen concentration in the aerobic reaction zone, real-time data on the nitrification reaction status, which can reflect the operating status of the nitrification reaction and the biological phosphorus uptake process, is obtained to characterize the aerobic reaction zone's ability to remove nitrogen and phosphorus pollutants.

[0027] Within the same sampling period, the sensor group in the anoxic zone is triggered to perform real-time sensing. By continuously monitoring the changes in nitrate nitrogen concentration and redox environmental parameters in the anoxic reaction zone, real-time data on the denitrification process, which reflects the extent of the denitrification reaction, is obtained to characterize the actual operation of the denitrification process in the anoxic reaction zone.

[0028] In addition, under the same sampling period conditions, the sensor group in the sedimentation zone is triggered to perform real-time sensing. By monitoring the changes in turbidity or suspended solids concentration in the effluent in the sedimentation zone, real-time solid-liquid separation data that characterizes the mud-water separation effect is obtained, which reflects the separation performance and operational stability of the sedimentation zone.

[0029] After collecting instantaneous water quality data at the inlet and real-time monitoring data of each reaction zone, the instantaneous water quality data, real-time data of anaerobic phosphorus release environment status, real-time data of denitrification process, real-time data of nitrification reaction status, and real-time data of solid-liquid separation are time-aligned according to the sampling period. Various types of monitoring data are synchronously bound by a unified timestamp, and the data collected from different reaction zones within the same sampling period are correlated and integrated to construct a real-time process dataset reflecting the operating conditions of the skid-mounted AOA pilot plant.

[0030] Step S200: Based on the real-time process dataset, set nitrogen and phosphorus removal targets, and perform multi-factor collaborative dynamic analysis according to the nitrogen and phosphorus removal targets and the real-time process dataset to construct a real-time control instruction set.

[0031] In this embodiment, the effluent discharge standard is first introduced to determine the target upper limit value. Based on this target upper limit value, the effluent concentration value is set, which includes the effluent standard limit value and the total phosphorus concentration standard limit value. Next, based on the effluent standard limit value and the total phosphorus concentration standard limit value, the target value range for nitrogen and phosphorus removal of the skid-mounted AOA pilot plant is defined, thereby forming the nitrogen and phosphorus removal target.

[0032] Under the constraint of nitrogen and phosphorus removal targets, influent fluctuation analysis is performed by combining instantaneous water quality data in the real-time process dataset to obtain influent water quality fluctuation data. Based on the influent water quality fluctuation data, multi-factor collaborative dynamic analysis is performed on the real-time process dataset according to the nitrogen and phosphorus removal targets, thereby constructing a real-time control instruction set to guide the operation and control of the skid-mounted AOA pilot plant.

[0033] Furthermore, in the method provided in the application embodiments, the nitrogen and phosphorus removal targets are set based on the real-time process dataset, and a real-time control instruction set is constructed by performing multi-factor collaborative dynamic analysis according to the nitrogen and phosphorus removal targets and the real-time process dataset. The method further includes:

[0034] A target upper limit value is determined by introducing effluent discharge standards. Based on this upper limit value, an effluent concentration value is set, which includes the effluent standard limit and the total phosphorus concentration standard limit. A nitrogen and phosphorus removal target value range for the skid-mounted AOA pilot plant is defined based on the effluent standard limit and the total phosphorus concentration standard limit, and this range includes the nitrogen and phosphorus removal targets. Influent water quality fluctuation analysis is performed based on the instantaneous water quality data to obtain influent water quality fluctuation data. Multi-factor collaborative dynamic analysis is then conducted based on the influent water quality fluctuation data, the nitrogen and phosphorus removal targets, and the real-time process dataset to construct a real-time control instruction set.

[0035] In this embodiment, an effluent discharge standard applicable to the operation of the AOA pilot plant is first introduced to constrain the permissible effluent water quality control requirements during plant operation, thus determining the target upper limit for nitrogen and phosphorus removal control. This target upper limit includes at least effluent standard limits and total phosphorus concentration standard limits. The effluent standard limits characterize the emission control requirements for nitrogen pollutants in the effluent, including effluent total nitrogen standard limits and effluent ammonia nitrogen standard limits. Based on the effluent standard limits and total phosphorus concentration standard limits, corresponding effluent concentration values ​​are further set, and these effluent concentration values ​​are used as reference benchmarks for nitrogen and phosphorus removal control, thereby constructing a target value range for nitrogen and phosphorus removal. By using both the target upper limit and the effluent concentration values ​​as the range boundary, the nitrogen and phosphorus removal targets clearly define the effluent water quality control range of the skid-mounted AOA pilot plant in the form of a numerical range.

[0036] After setting the target range for nitrogen and phosphorus removal, fluctuation analysis of the influent operating conditions is performed based on instantaneous water quality data. Specifically, the continuously collected instantaneous water quality data are arranged in the order of the sampling period, and the differences between the ammonia nitrogen concentration data, total nitrogen concentration data, and total phosphorus concentration data in adjacent sampling periods are calculated. The differences are used as the influent water quality fluctuation data.

[0037] Finally, based on the influent water quality fluctuation data, a multi-factor collaborative dynamic analysis was conducted according to the nitrogen and phosphorus removal targets and the real-time process dataset. In this process, the sampling period was first used as a unified time reference to align the influent water quality fluctuation data with the real-time process dataset. Instantaneous water quality data, real-time data on anaerobic phosphorus release environment status, real-time data on denitrification process, real-time data on nitrification reaction status, and real-time data on solid-liquid separation collected within the same sampling period were bound to the corresponding influent water quality fluctuation data to ensure temporal consistency. Subsequently, the effluent total nitrogen concentration, effluent ammonia nitrogen concentration, and effluent total phosphorus concentration corresponding to that sampling period were read and compared with the upper limit of the nitrogen and phosphorus removal target value range. The effluent total nitrogen deviation value was obtained by subtracting the effluent total nitrogen standard limit from the effluent total nitrogen concentration; the effluent ammonia nitrogen deviation value was obtained by subtracting the effluent ammonia nitrogen standard limit from the effluent ammonia nitrogen concentration; and the effluent total phosphorus deviation value was obtained by subtracting the effluent total phosphorus standard limit from the effluent total phosphorus concentration, thus clarifying the degree of deviation of the effluent water quality from the target upper limit. Next, the influent water quality fluctuation data within the same sampling period are read. By judging the positive and negative values ​​of the total nitrogen concentration difference, ammonia nitrogen concentration difference, and total phosphorus concentration difference, the direction of influent water quality change is determined. The direction of influent water quality change is then correlated with the effluent total nitrogen deviation value, effluent ammonia nitrogen deviation value, and effluent total phosphorus deviation value to determine whether there is a corresponding relationship between the effluent water quality deviation and the influent water quality fluctuation.

[0038] After establishing the correlation between influent water quality changes and effluent water quality deviations, real-time data on anaerobic phosphorus release environment status, denitrification process, nitrification reaction status, and solid-liquid separation were sequentially read within the same sampling period, and compared with the average values ​​of the corresponding indicators during the continuous stable operation phase. When the real-time data on denitrification process was lower than the average value during its continuous stable operation phase and the effluent total nitrogen deviation was positive, the total nitrogen removal deviation was determined to be related to the operating status of the anoxic reaction zone; when the real-time data on nitrification reaction status was lower than the average value during its continuous stable operation phase and the effluent ammonia nitrogen deviation was positive, the ammonia nitrogen removal deviation was determined to be related to the operating status of the aerobic reaction zone; when the real-time data on anaerobic phosphorus release environment status was lower than the average value during its continuous stable operation phase and the effluent total phosphorus deviation was positive, the phosphorus removal deviation was determined to be related to the operating status of the anaerobic reaction zone; and when the real-time data on solid-liquid separation was lower than the average value during its continuous stable operation phase and the effluent total phosphorus deviation was positive, the phosphorus removal deviation was determined to be related to the operating status of the sedimentation zone.

[0039] After determining the correspondence between the reaction zone and the effluent water quality deviation, the corresponding reaction zone is mapped to its controlled execution equipment, and real-time control commands are generated accordingly. When the total nitrogen removal deviation is related to the operating status of the anoxic reaction zone, a control command is generated for the mixed liquor return pump to increase its set flow rate; when the ammonia nitrogen removal deviation is related to the operating status of the aerobic reaction zone, a control command is generated for the aeration device to increase its set aeration intensity; when the phosphorus removal deviation is related to the operating status of the anaerobic reaction zone, a control command is generated for the sludge return pump to adjust its set return flow rate; when the phosphorus removal deviation is related to the operating status of the sedimentation zone, a control command is generated for the sludge return pump to decrease its set return flow rate. Subsequently, the control amplitude is determined based on the deviation values ​​of total nitrogen, ammonia nitrogen, or total phosphorus in the effluent. First, a preset proportional coefficient is determined. Data on the sludge concentration of the returned sludge in the sedimentation zone, the nitrate nitrogen concentration in the returned sludge, the oxidation-reduction potential of the anaerobic reaction zone, and the pollutant concentration in the influent are collected and compared with the corresponding reference thresholds. When the returned sludge concentration is within the preset normal range and the nitrate nitrogen concentration in the returned sludge is lower than the reference threshold, and the oxidation-reduction potential of the anaerobic reaction zone meets the anaerobic operation requirements, the proportional coefficient is adjusted upward within the preset range to enhance the return flow control amplitude. When the returned sludge concentration or the nitrate nitrogen concentration in the returned sludge is close to the upper limit of the reference range, or the oxidation-reduction potential of the anaerobic reaction zone is at a critical state, the proportional coefficient remains unchanged to avoid disturbance to the operation of the reaction zone caused by the return flow control. When the returned sludge concentration is higher than the upper limit of the reference range, or the nitrate nitrogen concentration in the returned sludge is significantly higher, or the oxidation-reduction potential of the anaerobic reaction zone deviates from the anaerobic requirements, the proportional coefficient is adjusted downward within the preset range to reduce the intensity of the return flow control. The deviation value of total nitrogen in the effluent is then multiplied by the adjusted proportional coefficient to obtain the adjustment amount of the set flow rate of the mixed liquor return pump. The deviation value of ammonia nitrogen in the effluent is multiplied by the adjusted proportional coefficient to obtain the adjustment amount of the set aeration intensity of the aeration device. The deviation value of total phosphorus in the effluent is multiplied by the preset proportional coefficient to obtain the adjustment amount of the set return flow rate of the sludge return pump. The adjustment amounts are then written into the corresponding control instructions, thereby forming a real-time control instruction set that includes the executing equipment, control direction, and control amplitude.

[0040] Step S300: Perform digital twin analysis based on the real-time control instruction set, build a digital twin space, simulate and execute the real-time control instruction set based on the digital twin space, drive the skid-mounted AOA pilot device to perform operation analysis, and generate multiple operation reaction process parameters.

[0041] In this embodiment, digital twin analysis is performed on the skid-mounted AOA pilot plant based on a real-time control instruction set. First, a three-dimensional model of the skid-mounted AOA pilot plant is constructed based on the anaerobic, aerobic, anoxic, and sedimentation zones, building a three-dimensional solid model representing the physical structure and process layout. Online water quality monitoring sensors within each reaction zone are then mapped to the three-dimensional solid model for spatial alignment, forming multiple spatial monitoring points. Subsequently, a digital twin is created between the three-dimensional solid model and the spatial monitoring points, constructing a digital twin space reflecting the structural and monitoring status of the skid-mounted AOA pilot plant.

[0042] Subsequently, the real-time control command set is mapped to the execution level of the skid-mounted AOA pilot plant for positioning, identifying multiple command execution mechanisms corresponding to the real-time control command set, and loading these mechanisms into the digital twin space to form corresponding virtual components. By assigning the real-time control command set to the virtual components, simulation execution is performed in the digital twin space to obtain simulation results. Based on these results, the operational status of the skid-mounted AOA pilot plant is analyzed, thereby generating multiple operational reaction process parameters to characterize the operational changes in each reaction zone.

[0043] Furthermore, in the method provided in the application embodiment, digital twin analysis is performed based on the real-time control instruction set to build a digital twin space. The real-time control instruction set is then simulated and executed based on the digital twin space to drive the skid-mounted AOA pilot plant for operational analysis, generating multiple operational reaction process parameters. The method also includes:

[0044] A 3D model of the skid-mounted AOA pilot plant is constructed based on the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone, forming a 3D solid model. Online water quality monitoring sensors for each reaction zone of the skid-mounted AOA pilot plant are synchronously mapped to the 3D solid model for spatial alignment, creating multiple spatial monitoring points. Digital twins are created between these spatial monitoring points and the 3D solid model to construct a digital twin space. The real-time control command set is mapped to the skid-mounted AOA pilot plant for positioning, identifying multiple command execution mechanisms. These multiple command execution mechanisms are loaded into the digital twin space for identification, identifying multiple virtual components, which correspond to the multiple command execution mechanisms. The real-time control command set is assigned to these virtual components for simulation operation, generating simulation results. Based on the simulation results, the skid-mounted AOA pilot plant is driven to perform operational analysis, generating multiple operational reaction process parameters.

[0045] In this embodiment, based on the process configuration of the skid-mounted AOA pilot plant, the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone are structurally modeled using a three-dimensional geometric modeling method. Specifically, according to the design drawings, structural dimensional parameters, and reaction zone connection relationships of the skid-mounted AOA pilot plant, a geometric contour model of each reaction zone is established one by one. Then, the models of each reaction zone are combined according to the actual water flow direction and process sequence to construct a three-dimensional solid model reflecting the physical structural characteristics of the skid-mounted AOA pilot plant.

[0046] Next, the online water quality monitoring sensors actually deployed in each reaction zone of the skid-mounted AOA pilot plant will be synchronously mapped into the three-dimensional solid model. The synchronous mapping adopts the position calibration method. Based on the installation coordinates, installation height and reaction zone information of the online water quality monitoring sensors in the physical device, the corresponding virtual installation positions are set in the three-dimensional solid model, thereby realizing a one-to-one correspondence between the online water quality monitoring sensors and the three-dimensional solid model in spatial position, and thus constructing multiple spatial monitoring points for carrying monitoring data.

[0047] Then, the spatial monitoring points are digitally twinned with the three-dimensional solid model. By using the method of fusing structural information and monitoring information, the structural information represented by the three-dimensional solid model is bound to the monitoring data interface carried by the spatial monitoring points. This allows each spatial monitoring point to not only have a definite spatial location, but also to receive monitoring data from the corresponding online water quality monitoring sensor in real time. This creates a digital twin space that can simultaneously reflect the structural status and monitoring status of the skid-mounted AOA pilot plant.

[0048] After the digital twin space is built, the skid-mounted AOA pilot plant is mapped and located based on the real-time control instruction set. By using the equipment matching method, each control instruction in the real-time control instruction set is parsed to identify the corresponding control object. The control object is then matched with the actual execution equipment in the skid-mounted AOA pilot plant to determine multiple instruction execution mechanisms. These instruction execution mechanisms include key equipment used to regulate the operating status of the plant, such as mixed liquor return pumps, sludge return pumps, and aeration devices.

[0049] After determining the instruction execution mechanism, multiple instruction execution mechanisms are loaded into the digital twin space for identification. By using the virtual object construction method, a virtual component with the same functional attributes and control parameters is created for each instruction execution mechanism in the digital twin space. This ensures that the virtual component is consistent with the corresponding instruction execution mechanism in terms of type, control parameters, and response mode, and ensures that a one-to-one correspondence is formed between the virtual component and the instruction execution mechanism, thereby enabling the digital twin space to execute control instructions.

[0050] Next, the real-time control command set is assigned to multiple virtual components for simulation operation. During this process, after assigning the real-time control command set to multiple virtual components, hydraulic residence analysis is performed on the virtual components, and a simulation time step is set. Under the control of the simulation time step, virtual flow calculations are performed on the virtual components to obtain virtual three-dimensional flow parameters characterizing the hydraulic characteristics of each reaction zone. Then, based on the virtual three-dimensional flow parameters, the distribution and change process of pollutant concentration in each reaction zone of the skid-mounted AOA pilot plant are analyzed, and a pollutant concentration change trend diagram is generated. Simultaneously, a virtual clock corresponding to the simulation time step is constructed, driving the virtual components to run continuously under the control of the virtual clock until the simulation duration threshold. During the operation, monitoring data from multiple spatial monitoring points are extracted, thereby generating simulation results characterizing the simulation process and results.

[0051] Finally, the skid-mounted AOA pilot plant was analyzed based on the simulation results. During this process, the actuators of the skid-mounted AOA pilot plant were driven by the simulation results. Feedforward-feedback composite control was implemented by adjusting the opening of the electric regulating valves from the sludge return pump to the anoxic zone, the opening of the electric regulating valves from the sludge return pump to the anaerobic zone, and the aeration device in the aerobic reaction zone. First, second, and third operating reaction parameters were collected. These parameters were then mapped to the corresponding actuators of the skid-mounted AOA pilot plant for process identification, generating operating process identifiers. The operating status of each reaction zone was synchronously verified based on these identifiers, obtaining synchronous verification results. When there were no conflicts in the synchronous verification results, the first, second, and third operating reaction parameters were integrated to generate multiple operating reaction process parameters characterizing the operating process of the skid-mounted AOA pilot plant.

[0052] Furthermore, in the method provided in the application embodiments, assigning the real-time control instruction set to the plurality of virtual components for simulation operation and generating simulation operation results further includes:

[0053] The real-time control command set is assigned to the multiple virtual components for hydraulic residence analysis, and a simulation time step is set. Virtual water flow calculations are performed on the multiple virtual components according to the simulation time step to obtain virtual three-dimensional water flow parameters. Based on the virtual three-dimensional water flow parameters, pollutant concentration analysis is performed on each reaction zone of the skid-mounted AOA pilot plant, and a pollutant concentration change trend diagram is plotted. A virtual clock is constructed based on the simulation time step, and the multiple virtual components run continuously according to the virtual clock until the simulation duration threshold is reached. Monitoring data from multiple spatial monitoring points are extracted to generate the simulation operation results.

[0054] In this embodiment, after assigning the real-time control command set to multiple virtual components, a hydraulic retention analysis is first performed on the skid-mounted AOA pilot plant. The reaction zone volumes of the anaerobic, aerobic, anoxic, and sedimentation zones are read from the three-dimensional solid model, along with the influent flow rate, mixed liquor return pump flow rate, and sludge return pump flow rate corresponding to the real-time control command set. Subsequently, the corresponding flow rate for each reaction zone is determined. The flow rate for the anaerobic reaction zone is the sum of the influent flow rate and the sludge return pump flow rate; the flow rate for the anoxic reaction zone is the sum of the anaerobic reaction zone outlet flow rate and the mixed liquor return pump flow rate; the flow rate for the aerobic reaction zone is the difference between the anoxic reaction zone outlet flow rate and the mixed liquor return pump flow rate; and the flow rate for the sedimentation zone is the aerobic reaction zone outlet flow rate. After determining the corresponding flow rate, the hydraulic retention times (HJ) of the anaerobic reaction zone, anoxic reaction zone, aerobic reaction zone, and sedimentation zone are calculated separately, using the formula that the HJ equals the reaction zone volume divided by the corresponding flow rate. After obtaining each HJ, the minimum value is selected as the baseline HJ. The simulation time step is then set by multiplying the baseline HJ by a preset ratio. The preset ratio is set to a value between 0.01 and 0.1 to ensure that each HJ is divided into at least 10 to 100 simulation time steps, thus completing the hydraulic retention analysis and simulation time step setting.

[0055] Next, according to the simulation time step, virtual water flow calculations are performed on the multiple virtual components. A computational grid is established in the three-dimensional solid model, dividing the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone into multiple spatial units. Water flow velocity components and pressure variables are set in each spatial unit. Then, at the beginning of each simulation time step, the influent flow rate, mixed liquor return pump return flow rate, and sludge return pump return flow rate from the real-time control command set are written as boundary conditions into the corresponding influent and return boundaries, and the connection points of each reaction zone are used as internal connectivity boundaries. Next, a mass conservation calculation is performed on each spatial unit. The sum of the flow rates entering the spatial unit is subtracted from the sum of the flow rates leaving the spatial unit, and set to zero to form a flow balance equation. Then, the water flow velocity components of each spatial unit are iteratively updated based on the connectivity relationships between spatial units. The stopping condition for iterative updates is that the difference between the water flow velocity components obtained from two adjacent iterations is less than a preset convergence threshold. After completing the iterative update within each simulation time step, the magnitude and direction of the water flow velocity of all spatial units under that simulation time step are obtained, and the set of water flow velocity vectors of each spatial unit is output as virtual three-dimensional flow parameters. The virtual three-dimensional flow parameters include at least the velocity vector, streamline distribution and flow distribution results of each spatial unit.

[0056] Subsequently, based on the virtual three-dimensional flow parameters, pollutant concentration analysis was performed on each reaction zone of the skid-mounted AOA pilot plant. Specifically, pollutant concentration variables were set in each spatial cell of the three-dimensional solid model, including ammonia nitrogen, nitrate nitrogen, total nitrogen, and total phosphorus concentrations. At the beginning of each simulation time step, the instantaneous water quality data at the inlet boundary was written into the inlet spatial cell as the initial boundary condition for pollutant concentration. Then, based on the virtual three-dimensional flow parameters, convective migration calculations were performed on the pollutants. For each spatial cell, the pollutant mass flux entering and leaving the cell was calculated according to the flow velocity vector of the current simulation time step. The concentration change in that spatial cell due to water migration was obtained by dividing the difference in pollutant mass flux by the volume of the spatial cell. Next, biochemical reaction concentration updates were performed for each spatial unit. In the anaerobic reaction zone, the total phosphorus concentration was updated incrementally according to the release direction; in the anoxic reaction zone, the nitrate nitrogen concentration was updated incrementally according to the denitrification direction; and in the aerobic reaction zone, the ammonia nitrogen concentration was updated incrementally according to the nitrification direction and the total phosphorus concentration was updated incrementally according to the phosphorus uptake direction. The increment or decrement value was obtained by multiplying the preset reaction rate by the simulation time step. Subsequently, the concentration changes caused by water flow migration and the concentration changes caused by biochemical reactions were superimposed to obtain the pollutant concentration of each spatial unit at the end of the simulation time step. The above process was repeated until the simulation duration threshold was reached. After completing the calculation of all simulation time steps, the pollutant concentration of all spatial units in each reaction zone was volume-weightedly averaged to obtain the representative pollutant concentration of the reaction zone at each simulation time step. The representative pollutant concentration was then plotted as a pollutant concentration change trend graph over the simulation time.

[0057] Finally, a virtual clock is constructed based on the simulated time step. The initial time of the virtual clock is set to zero, and the virtual clock time is incremented by one simulated time step after each simulation time step calculation is completed, until the virtual clock time reaches the simulation duration threshold. At each time node corresponding to the virtual clock increment, monitoring data is extracted from multiple spatial monitoring points. The extraction process involves locating the spatial cell containing each spatial monitoring point in the 3D solid model and reading the ammonia nitrogen concentration, nitrate nitrogen concentration, dissolved oxygen concentration, total nitrogen concentration, and total phosphorus concentration of that spatial cell at the end of the current simulation time step as the monitoring data for that spatial monitoring point. When the virtual clock time reaches the simulation duration threshold, the monitoring data extracted from each spatial monitoring point across all simulation time steps are summarized in chronological order to form the simulation results.

[0058] Furthermore, in the method provided in the application embodiments, the skid-mounted AOA pilot plant is driven to perform operational analysis based on the simulation results to generate the multiple operational reaction process parameters, and the method further includes:

[0059] The actuators of the skid-mounted AOA pilot plant are driven based on the simulation results. The actuators adjust the opening of the electric regulating valve connecting the sludge return pump to the anoxic zone, collecting first operating reaction parameters. The actuators also adjust the opening of the electric regulating valve connecting the sludge return pump to the anaerobic zone, collecting second operating reaction parameters. The actuators adjust the aeration device in the aerobic reaction zone for feedforward-feedback composite control, collecting third operating reaction parameters. The first, second, and third operating reaction parameters are mapped to the actuators of the skid-mounted AOA pilot plant for process identification, generating an operating process identifier. The operating process identifiers are used to perform synchronous verification of each reaction zone of the skid-mounted AOA pilot plant, generating synchronous verification results. Conflict analysis is performed based on the synchronous verification results. When there are no conflicts in the synchronous verification results, the first, second, and third operating reaction parameters are integrated to generate the multiple operating reaction process parameters.

[0060] In this embodiment, when driving the actuator of the skid-mounted AOA pilot plant based on the simulation results, the target return flow rate of the sludge return pump to the anoxic zone, the target return flow rate of the sludge return pump to the anaerobic zone, and the target aeration intensity of the aeration device in the aerobic reaction zone are read from the simulation results. The target return flow rates to the anoxic zone and the target return flow rates to the anaerobic zone are converted into control settings for the sludge return pump, the opening settings for the electric regulating valve to the anoxic zone, and the opening settings for the electric regulating valve to the anaerobic zone, respectively. The target aeration intensity is written into the control settings for the aeration device. Within the same sampling period, execution commands are issued to the sludge return pump, the electric regulating valve to the anoxic zone, the electric regulating valve to the anaerobic zone, and the aeration device, so that the return sludge concentrated after sedimentation in the sedimentation zone is transported to the anoxic zone and the anaerobic zone respectively by the same sludge return pump, and the aeration device is put into operation according to the target aeration intensity, thus completing the driving of the actuator of the skid-mounted AOA pilot plant based on the simulation results.

[0061] Next, by adjusting the opening of the electric regulating valve leading to the anoxic zone via the sludge return pump and collecting the first operating reaction parameters, the returned sludge, concentrated in the sedimentation zone, is transported to the anoxic zone via the sludge return pump and the return branch leading to the anoxic zone, thus forming a sludge return channel between the sedimentation zone and the anoxic zone. This sludge return channel is used to replenish the sludge concentration in the anoxic zone, maintaining the amount of activated sludge required for denitrification. During the return operation, the return flow rate of the branch leading to the anoxic zone, the opening of the electric regulating valve leading to the anoxic zone, and the operating status parameters of the anoxic zone are collected in real time. The sludge return flow rate to the anoxic zone is adjusted based on these operating status parameters to match the sludge load in the anoxic zone with its reaction requirements. The real-time return flow rate of the branch leading to the anoxic zone, the opening of the electric regulating valve leading to the anoxic zone, and the corresponding operating status parameters of the anoxic zone are collected and recorded as the first operating reaction parameters to characterize the operating status of the sludge when it is returned to the anoxic zone and its impact on the reaction process in the anoxic zone.

[0062] Subsequently, by adjusting the opening of the electric regulating valve leading to the anaerobic zone via the sludge return pump and collecting the second operating reaction parameters, the returned sludge, concentrated after sedimentation in the sedimentation zone, is transported to the anaerobic zone via the sludge return pump and the return branch leading to the anaerobic zone, thus forming a sludge return channel between the sedimentation zone and the anaerobic zone. The sludge return channel is used to replenish activated sludge while utilizing the internal carbon source in the returned sludge. Combined with the short hydraulic retention time formed by the compact flow channel of the skid-mounted AOA pilot plant, it rapidly establishes an anaerobic environment to promote phosphorus release by polyphosphate-accumulating bacteria in the anaerobic zone. During the return operation, the return flow rate of the branch leading to the anaerobic zone, the opening of the electric regulating valve leading to the anaerobic zone, and the operating status parameters of the anaerobic zone are collected in real time, and the sludge return flow rate to the anaerobic zone is adjusted according to the operating status parameters of the anaerobic zone. The real-time return flow rate of the branch leading to the anaerobic zone, the opening degree of the electric regulating valve leading to the anaerobic zone, and the corresponding anaerobic zone operating status parameters are collected and recorded as the second operating reaction parameters to characterize the operating status of sludge when it is returned to the anaerobic zone and its impact on the anaerobic zone reaction process.

[0063] Subsequently, the aeration devices in the aerobic reaction zone are adjusted via an actuator to implement feedforward-feedback composite control, and a third operational response parameter is collected. In this process, the aeration devices in the aerobic reaction zone are controlled via feedforward-feedback composite control by an actuator. Feedforward and feedback loops are set separately. In the feedforward loop, aeration demand is matched based on real-time influent data to determine the basic aeration demand. In the feedback loop, a terminal monitoring dataset containing dissolved oxygen and ammonia nitrogen concentration data is obtained by monitoring the aerobic reaction zone. Based on the dissolved oxygen and ammonia nitrogen concentration data, an aeration reduction command or aeration enhancement command is generated as a feedback adjustment. The basic aeration demand and the feedback adjustment are superimposed to construct an aeration intensity command. Then, based on the aeration intensity command, the aeration devices are controlled in a composite manner, thereby generating a third operational response parameter characterizing the operating status of the aerobic reaction zone.

[0064] Next, based on the first, second, and third operating reaction parameters, the process is identified by mapping them to the actuators of the skid-mounted AOA pilot plant. By mapping the first operating reaction parameters to the current operating status of the sludge return pump to the branch leading to the anoxic zone, mapping the second operating reaction parameters to the current operating status of the sludge return pump to the branch leading to the anaerobic zone, and mapping the third operating reaction parameters to the current operating status of the aeration device, the action sequence and completion status mark of each actuator within the same operating cycle are formed, and the operating process identifier is generated.

[0065] Subsequently, the operation of each reaction zone of the skid-mounted AOA pilot plant was synchronously verified according to the operation process identifier. The operation status of each reaction zone was checked using a method based on time alignment and state alignment. The completion time of the actuator action recorded in the operation process identifier was used as the time reference. Real-time data of the anaerobic phosphorus release environment status of the anaerobic reaction zone, the denitrification process of the anoxic reaction zone, the nitrification reaction status of the aerobic reaction zone, and the solid-liquid separation of the sedimentation zone were extracted within the same operating cycle and correlated with the change time points of the first, second, and third operating reaction parameters, respectively. When the first operating reaction parameter changed, the real-time data of the denitrification process of the anoxic reaction zone was updated within the preset time window. When the nitrate nitrogen concentration shows a decreasing trend, it is determined that the sludge return adjustment action to the anoxic zone is consistent with the anoxic zone's operating status in terms of time and function. When the second operating reaction parameter changes, and the real-time data of the anaerobic phosphorus release environment in the anaerobic reaction zone shows an increasing trend in phosphorus release within the preset time window, it is determined that the sludge return is consistent with the anaerobic reaction zone's operating status. When the third operating reaction parameter changes, and the real-time data of the nitrification reaction in the aerobic reaction zone shows a decreasing ammonia nitrogen and dissolved oxygen stabilizing within the target range within the preset time window, it is determined that the aeration adjustment is consistent with the aerobic reaction zone's operating status. When all the above correspondences are valid within the same operating cycle, the synchronous operation verification is completed and the synchronous operation verification results are generated.

[0066] Finally, conflict analysis was conducted based on the results of synchronous operation verification. The actuator adjustments were checked using a method based on parameter direction consistency and process response consistency. Within the same operating cycle, the change directions of the first, second, and third operating reaction parameters were compared, and the change directions of the corresponding reaction zone operating status data were checked simultaneously. When the first operating reaction parameter was increased and the real-time data of the denitrification process showed an improving trend, without causing a deterioration in the real-time data of the nitrification reaction in the aerobic reaction zone, it was determined that there was no conflict in the sludge return adjustment to the anoxic zone. When the second operating reaction parameter was increased and the real-time data of the anaerobic phosphorus release environment showed an improving trend, it was determined that there was no conflict. When the sludge return adjustment shows an improving trend and does not cause a deterioration in the real-time solid-liquid separation data in the sedimentation zone, it is determined that there is no conflict. When the dissolved oxygen concentration stabilizes within the target range after the third operating reaction parameter is adjusted and the real-time nitrification reaction status data improves, and does not cause a deterioration in the real-time denitrification process data in the anoxic reaction zone, it is determined that there is no conflict. When all the above checks are met within the same operating cycle, it is determined that there is no conflict in the synchronous verification results. Based on this, the first, second, and third operating reaction parameters are integrated according to the same operating cycle to complete the integration of operating reaction parameters and generate multiple operating reaction process parameters.

[0067] Furthermore, in the method provided in the application embodiment, the feedforward-feedback composite control is performed by adjusting the aeration device in the aerobic reaction zone through the actuator, and the third operating reaction parameter is collected, which further includes:

[0068] The aeration device in the aerobic reaction zone is adjusted by the actuator, and a feedforward and feedback loops are set. A real-time influent dataset is calculated based on the feedforward loop, and aeration demand is matched according to the real-time influent dataset to determine the basic aeration demand. The aerobic reaction zone is monitored at the end of the aerobic reaction zone based on the feedback loop to generate an end-of-pipe monitoring dataset, which includes dissolved oxygen data and ammonia nitrogen concentration data. Based on the dissolved oxygen data and the ammonia nitrogen concentration data, the following judgments are made: S1: Oxygen concentration analysis is performed based on the dissolved oxygen data to obtain dissolved oxygen concentration data. If the dissolved oxygen concentration data is continuously higher than a preset upper limit threshold, an aeration reduction command is generated. S2: If the ammonia nitrogen concentration data is higher than a preset concentration threshold, an aeration enhancement command is generated. The aeration reduction command or the aeration enhancement command is used as a feedback adjustment amount. The basic aeration demand is superimposed with the feedback adjustment amount to construct an aeration intensity command. Composite control is performed based on the aeration intensity command to generate the third operating reaction parameter.

[0069] In this embodiment of the application, when the aeration device in the aerobic reaction zone is adjusted by the actuator, the feedforward and feedback loops are activated simultaneously in the control logic. The feedforward loop is used to predetermine the aeration demand based on the changes in the influent load, and the feedback loop is used to correct the aeration demand based on the actual operating state of the aerobic reaction zone, thereby forming a control method that combines feedforward and feedback.

[0070] In the feedforward stage, the basic aeration demand is determined based on the actual load of pollutants entering the device from the influent. During the sampling period, the total nitrogen concentration, ammonia nitrogen concentration, and COD concentration of the influent are acquired at the influent end, and the influent flow rate is acquired simultaneously. The ammonia nitrogen concentration is multiplied by the influent flow rate to obtain the ammonia nitrogen load entering the device per unit time; the COD concentration is multiplied by the influent flow rate to obtain the COD load entering the device per unit time; and the total nitrogen concentration is multiplied by the influent flow rate to obtain the total nitrogen load entering the device per unit time, thus forming a real-time influent dataset. Subsequently, the required oxygen supply is calculated according to a pre-set proportional relationship: the ammonia nitrogen load is multiplied by a proportional coefficient representing the amount of oxygen required for nitrification per unit of ammonia nitrogen to obtain the oxygen supply required for nitrification; the COD load is multiplied by a proportional coefficient representing the amount of oxygen required for oxidation per unit of COD to obtain the oxygen supply required for organic matter oxidation; and the oxygen supply required for nitrification and the oxygen supply required for organic matter oxidation are added together to obtain the total required oxygen supply. Finally, the basic aeration requirement is determined based on the oxygen supply capacity of the aeration device. That is, given the amount of oxygen that the aeration device can provide per unit aeration intensity, the total required oxygen supply is divided by the oxygen supply per unit aeration intensity to obtain the basic aeration requirement of the aeration device in this sampling period.

[0071] In the feedback process, end-point monitoring is conducted on the aerobic reaction zone. Online water quality monitoring sensors deployed at the end of the aerobic reaction zone continuously collect dissolved oxygen and ammonia nitrogen concentration data according to the sampling cycle. The collected dissolved oxygen and ammonia nitrogen concentration data are then compiled in chronological order to form an end-point monitoring dataset, which reflects the real-time operating status of the aerobic reaction zone under aeration.

[0072] When making judgments based on dissolved oxygen and ammonia nitrogen concentration data from the end-point monitoring dataset, the dissolved oxygen data is first analyzed by comparing the dissolved oxygen concentration data within the current sampling period with a preset upper limit threshold. If the dissolved oxygen concentration data is higher than the preset upper limit threshold for multiple consecutive sampling periods, it is determined that there is over-aeration in the aerobic reaction zone, and an aeration reduction instruction is generated accordingly. Subsequently, the ammonia nitrogen concentration data is judged by comparing the ammonia nitrogen concentration data within the current sampling period with a preset concentration threshold. If the ammonia nitrogen concentration data is higher than the preset concentration threshold, it is determined that the nitrification reaction is insufficient, and an aeration enhancement instruction is generated accordingly.

[0073] After completing the above determination, the generated aeration reduction command or aeration enhancement command is used as a feedback adjustment amount. The feedback adjustment amount is used to characterize the correction direction and correction magnitude that need to be made relative to the basic aeration demand. Then, the basic aeration demand and the feedback adjustment amount are superimposed using an additive superposition method to obtain the final aeration intensity command. The aeration intensity command clearly specifies the target aeration intensity of the aeration device in the current operating cycle.

[0074] Finally, based on the aeration intensity command, a composite control combining feedforward and feedback is implemented on the aeration device in the aerobic reaction zone. This allows the aeration device to adjust its operating state according to the aeration intensity command, thereby ensuring complete nitrification of ammonia nitrogen in the aerobic reaction zone while avoiding excessive dissolved oxygen levels caused by excessive aeration intensity, which would inhibit phosphorus uptake by polyphosphate-accumulating bacteria. During this composite control process, actual operating status data of the aeration device are collected to form a third operating reaction parameter used to characterize the aeration regulation effect.

[0075] Step S400: Based on the multiple operating reaction process parameters, the real-time control instruction set is synchronously corrected by inversion. Based on the correction instructions, the distribution of return sludge in the anaerobic and anoxic zones is dynamically controlled by an electric regulating valve to construct a wastewater treatment strategy.

[0076] In this embodiment, based on multiple operational reaction process parameters, the reaction behavior and control response exhibited by the skid-mounted AOA pilot plant during actual operation are quantitatively analyzed. Multiple deviations of the operational reaction process parameters relative to the expected control effect and their corresponding deviation directions are identified through deviation calculation, and inversion analysis is performed to construct an inversion correction vector. Subsequently, the inversion correction vector is superimposed and fused with the real-time control command set to achieve synchronous correction of the real-time control command set, generating a correction command that includes the command execution priority.

[0077] Subsequently, when dynamically regulating the distribution of return sludge in the anaerobic and anoxic zones via an electric regulating valve according to the calibration command, the calibration command is executed according to the command execution priority, and a sludge assessment time window is activated to collect high-frequency data on the operating status of the skid-mounted AOA pilot plant, obtaining parameters reflecting the changes in characteristics before and after the execution of the calibration command. Finally, based on the parameters reflecting the changes before and after the regulation, the sludge migration, distribution, and reaction status are dynamically assessed. The assessment results are then comprehensively summarized with the calibration command and operating reaction process parameters to construct a wastewater treatment strategy to guide the skid-mounted AOA pilot plant in stably achieving nitrogen and phosphorus removal targets.

[0078] Furthermore, in the method provided in the application embodiments, the real-time control instruction set is synchronously corrected by inversion based on the multiple operating reaction process parameters, and the distribution of return sludge in the anaerobic and anoxic zones is dynamically controlled through an electric regulating valve according to the correction instructions to construct a wastewater treatment strategy, and further includes:

[0079] Based on the multiple operational reaction process parameters, deviation calculations are performed to identify multiple deviation items, each containing multiple deviation directions, and these deviation items correspond to the multiple deviation directions. Inversion analysis is then performed based on the multiple deviation items and directions to construct an inversion correction vector. This inversion correction vector is then superimposed and fused with the real-time control instruction set to generate a correction instruction, which includes an instruction execution priority. The correction instruction is executed according to the instruction execution priority, and a sludge assessment time window is initiated to perform high-frequency data acquisition on the skid-mounted AOA pilot plant, generating parameters showing changes before and after control. Dynamic evaluation is then performed based on these parameters to construct a wastewater treatment strategy.

[0080] In this embodiment, when calculating deviations based on multiple operational reaction process parameters, a parameter difference calculation method is used. Each operational reaction process parameter is mapped one-to-one with its corresponding target parameter in the real-time control instruction set. The values ​​of both are read within the same operational cycle, and the deviation value is obtained by subtracting the target parameter from the operational reaction process parameter. Subsequently, the deviation direction is determined based on the sign of the deviation value: a positive deviation is identified as a positive deviation, and a negative deviation as a negative deviation. This forms multiple deviation items, and each deviation item is labeled with its corresponding deviation direction, thereby establishing a clear correspondence between multiple deviation items and multiple deviation directions, completing the deviation calculation and deviation item identification.

[0081] Next, an inversion analysis is performed based on multiple deviation terms and multiple deviation directions. In this process, the operational deviation of the skid-mounted AOA pilot plant is analyzed based on multiple deviation terms, and the main deviation term with the greatest impact on the current operating state is identified through principal deviation analysis. Then, the main deviation term is matched with multiple deviation directions to obtain the principal deviation direction corresponding to the main deviation term. Subsequently, an inversion analysis is performed based on the main deviation term and principal deviation direction to calculate the inversion deviation value reflecting the degree of correction required for the deviation. The inversion deviation value is then directionally corrected according to the principal deviation direction to obtain the corresponding correction direction. Finally, the inversion deviation value and the correction direction are integrated to generate an inversion correction vector.

[0082] Next, the inversion correction vector is superimposed and fused with the real-time control command set for correction. By employing a parameter summation method, the original control parameters in the real-time control command set, such as mixed liquor return flow, sludge return flow, and aeration intensity, are added item by item to the corresponding correction values ​​in the inversion correction vector to obtain updated control parameter values. These updated control parameter values ​​then replace the original parameters in the real-time control command set, thereby generating correction commands. Subsequently, the correction commands are sorted according to the absolute value of the correction values ​​in each command, forming a set of correction commands that includes command execution priorities.

[0083] When executing correction instructions according to the specified priority, the correction instructions are issued to the corresponding execution agencies in descending order of priority, and a sludge assessment time window is activated when each correction instruction begins execution. Specifically, when executing correction commands related to sludge return distribution, the sludge return pump is kept running, and the opening of the electric regulating valves leading to the anoxic zone and the anaerobic zone is adjusted according to the correction commands. This allows the sludge returned from the sedimentation zone to be redistributed between the return branches leading to the anoxic zone and the anaerobic zone according to the corrected distribution ratio. When the correction command corresponds to an increased denitrification demand in the anoxic zone, the opening of the electric regulating valve leading to the anoxic zone is increased and / or decreased to increase the amount of sludge returned to the anoxic zone. When the correction command corresponds to an increased phosphorus release demand in the anaerobic zone, the opening of the electric regulating valve leading to the anaerobic zone is increased and / or decreased to increase the amount of sludge returned to the anaerobic zone. This achieves dynamic control of the distribution of sludge returned to the anaerobic and anoxic zones through the electric regulating valves. Within the sludge assessment time window, high-frequency data acquisition was conducted on the skid-mounted AOA pilot plant. This involved continuously collecting data at intervals shorter than the conventional sampling period, including the return sludge flow rate to the anoxic zone, the return sludge flow rate to the anaerobic zone, the opening degree of the electric regulating valve to the anoxic zone, the opening degree of the electric regulating valve to the anaerobic zone, the aeration intensity, and the operating status data of each reaction zone. The data collected before and after the execution of the correction command were recorded in chronological order, thereby generating parameters reflecting the changes before and after the execution of the correction command.

[0084] Finally, a dynamic evaluation is performed based on the changes in parameters before and after the adjustment. During this process, within the sludge evaluation time window, the pre-adjustment changes formed before the correction command was executed and the post-adjustment changes formed after the correction command were executed were time-aligned using the same sampling period. The changes in return sludge flow rate to the anoxic zone, return sludge flow rate to the anaerobic zone, the corresponding electric regulating valve opening, and the aeration intensity before and after the adjustment were calculated, and the operational reaction process parameters within the corresponding operating cycle were extracted simultaneously. Subsequently, by comparing the differences and directions of the parameters before and after regulation, the response effects of each regulation parameter adjustment on real-time data of anaerobic phosphorus release environment, denitrification process, and nitrification reaction status were determined. When the distribution of return sludge between the anaerobic and anoxic zones was adjusted by the electric regulating valve, and the amount of return sludge allocated to the anoxic zone increased while the real-time data of denitrification process showed a continuous improvement trend, the distribution adjustment to the anoxic zone was deemed to have a positive regulation effect. When the amount of return sludge allocated to the anaerobic zone increased while the real-time data of anaerobic phosphorus release environment showed an enhancing trend, the distribution adjustment to the anaerobic zone was deemed to have a positive regulation effect. When the real-time data of nitrification reaction status steadily improved after the aeration intensity was adjusted without causing deterioration of the denitrification process in the anoxic zone, the aeration adjustment was deemed to have a positive regulation effect. The consistency of the above positive regulation effects was then assessed over multiple consecutive sludge evaluation time windows. The fluctuation range of the operating reaction process parameters within the continuous operating cycle was statistically analyzed. When the fluctuation amplitude remained within the preset range and no reverse deviation occurred, the regulation effect was deemed to have operational stability. After determining the effectiveness and stability of the control effect, the correction commands that meet the judgment conditions, the corresponding inversion correction vector characteristics, and the change patterns of parameters before and after control are sorted out and summarized to form the target allocation ratio of return sludge between the anaerobic and anoxic zones, the target opening range of the electric regulating valve from the sludge return pump to the anoxic zone, the target opening range of the electric regulating valve from the sludge return pump to the anaerobic zone, and the corresponding execution sequence rules. Thus, a wastewater treatment strategy is constructed to guide the continuous and stable operation of the skid-mounted AOA pilot plant.

[0085] Furthermore, in the method provided in the application embodiments, the inversion correction vector is constructed based on the inversion analysis performed on the plurality of deviation terms and the plurality of deviation directions, and further includes:

[0086] Based on the multiple deviation terms, perform principal deviation analysis to define principal deviation terms; match the multiple deviation directions according to the principal deviation terms to extract principal deviation directions; perform inversion analysis according to the principal deviation directions and principal deviation terms to generate inversion deviation values; correct the inversion deviation values ​​according to the principal deviation directions to generate correction directions; integrate the inversion deviation values ​​and correction directions to construct the inversion correction vector.

[0087] In this embodiment of the application, when performing main deviation analysis based on multiple deviation items, a deviation amplitude sorting method is adopted. The deviation values ​​of multiple deviation items obtained in the same running cycle are read one by one, and the absolute value of the deviation value is taken to eliminate the influence of positive and negative signs. Then, the multiple deviation items are sorted in descending order of the absolute value of the deviation, and the deviation item with the largest absolute value of the deviation in the sorting result is selected and defined as the main deviation item.

[0088] After determining the main deviation item, multiple deviation directions are matched based on the main deviation item. The deviation direction that the main deviation item has determined in the deviation calculation stage is read and used as the main deviation direction.

[0089] After obtaining the main deviation terms and their directions, an inversion analysis is performed based on the main deviation directions and main deviation terms. The inversion deviation value is generated using a proportional back-calculation method. Specifically, first, the type of control parameter corresponding to the main deviation term is determined. When the main deviation term corresponds to the mixed liquor return flow rate of the mixed liquor return pump, the first operating reaction parameter and the set value of the mixed liquor return pump's return flow rate are read. The deviation value of the main deviation term is divided by the first operating reaction parameter to obtain the normalized deviation ratio. This normalized deviation ratio is then multiplied by the set value of the mixed liquor return pump's return flow rate to obtain the inversion deviation value of the mixed liquor return flow rate. When the main deviation term corresponds to the sludge return flow rate of the sludge return pump, the second operating reaction parameter and the set value of the sludge return pump's return flow rate are read. The inversion deviation value is then calculated by multiplying the main deviation term by the first operating reaction parameter to obtain the normalized deviation ratio. The deviation value of the main deviation item is divided by the second operating reaction parameter to obtain the normalized deviation ratio. The normalized deviation ratio is then multiplied by the sludge return pump return flow rate setting value to obtain the sludge return flow rate inversion deviation value. When the main deviation item corresponds to the aeration intensity of the aeration device, the third operating reaction parameter and the aeration intensity setting value of the aeration device are read. The deviation value of the main deviation item is divided by the third operating reaction parameter to obtain the normalized deviation ratio. The normalized deviation ratio is then multiplied by the aeration intensity setting value of the aeration device to obtain the aeration intensity inversion deviation value, thus completing the calculation and generation of the inversion deviation value.

[0090] After generating the inversion bias value, it is corrected according to the main bias direction. The correction direction is determined according to the positive or negative attribute of the main bias direction. When the main bias direction is positive, the correction direction is determined to be negative; when the main bias direction is negative, the correction direction is determined to be positive, thus generating the correction direction corresponding to the inversion bias value.

[0091] After determining the correction direction, the inversion deviation value and the correction direction are integrated. Specifically, the inversion deviation value is used as the correction magnitude, the correction direction is used as the correction sign, and the correction magnitude and correction sign are combined to form a unified data expression result, thereby constructing the inversion correction vector.

[0092] In summary, the embodiments of this application have at least the following technical effects:

[0093] This application utilizes online water quality monitoring sensors deployed in each reaction zone of a skid-mounted AOA pilot plant for real-time sensing, generating a real-time process dataset. Based on this dataset, nitrogen and phosphorus removal targets are set. Multi-factor collaborative dynamic analysis is performed according to these targets and the real-time process dataset to construct a real-time control command set. Digital twin analysis is conducted based on this command set to build a digital twin space. The execution of the real-time control command set is simulated within this digital twin space, driving the skid-mounted AOA pilot plant to perform operational analysis and generating multiple operational reaction process parameters. The real-time control command set is synchronously corrected based on these multiple operational reaction process parameters. According to the correction commands, the distribution of return sludge in the anaerobic and anoxic zones is dynamically controlled via an electric regulating valve, constructing a wastewater treatment strategy. This invention solves the technical problems of inaccurate nitrogen and phosphorus removal operation control and difficulty in effectively optimizing the skid-mounted AOA wastewater treatment process in existing technologies. Through a dynamic control and correction mechanism based on real-time process data and combined with digital twins, it achieves stable and efficient operation of the nitrogen and phosphorus removal process.

[0094] Example 2, based on the same inventive concept as the wastewater treatment method based on the skid-mounted AOA pilot plant in the foregoing examples, such as... Figure 2 As shown, this application provides a wastewater treatment system based on a skid-mounted AOA pilot plant. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0095] The real-time sensing module 11 is used to perform real-time sensing through online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant, generating a real-time process dataset; the dynamic analysis module 12 is used to set nitrogen and phosphorus removal targets based on the real-time process dataset, and to perform multi-factor collaborative dynamic analysis according to the nitrogen and phosphorus removal targets combined with the real-time process dataset to construct a real-time control instruction set; the digital twin analysis module 13 is used to perform digital twin analysis based on the real-time control instruction set, build a digital twin space, simulate and execute the real-time control instruction set based on the digital twin space, drive the skid-mounted AOA pilot plant to perform operation analysis, and generate multiple operating reaction process parameters; the control module 14 is used to perform inversion on the real-time control instruction set according to the multiple operating reaction process parameters to synchronously correct it, and to dynamically control the distribution of return sludge in the anaerobic and anoxic zones through electric regulating valves according to the correction instructions, thereby constructing a wastewater treatment strategy.

[0096] Furthermore, the system is also used to implement the following functions:

[0097] The skid-mounted AOA pilot plant includes an anaerobic reaction zone, an aerobic reaction zone, an anoxic reaction zone, and a sedimentation zone. Based on online water quality monitoring sensors, sensor matching is performed on the anaerobic reaction zone, the aerobic reaction zone, the anoxic reaction zone, and the sedimentation zone. According to the matching results, anaerobic zone sensor groups are deployed in the anaerobic reaction zone, the anoxic zone, the aerobic reaction zone, and the sedimentation zone. Instantaneous water quality data is collected at the inlet of the skid-mounted AOA pilot plant, and a sampling period is set. The anaerobic zone sensor groups are triggered to perform real-time sensing according to the sampling period. The system obtains real-time data on the anaerobic phosphorus release environment; it triggers the aerobic zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on the nitrification reaction status; it triggers the anoxic zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on the denitrification process; it triggers the sedimentation zone sensor group to perform real-time sensing according to the sampling cycle to obtain real-time data on solid-liquid separation; and it aligns and binds the instantaneous water quality data, the real-time data on the anaerobic phosphorus release environment, the real-time data on the denitrification process, the real-time data on the nitrification reaction status, and the real-time data on solid-liquid separation according to the collection cycle to construct the real-time process dataset.

[0098] Furthermore, the system is also used to implement the following functions:

[0099] A target upper limit value is determined by introducing effluent discharge standards. Based on this upper limit value, an effluent concentration value is set, which includes the effluent standard limit and the total phosphorus concentration standard limit. A nitrogen and phosphorus removal target value range for the skid-mounted AOA pilot plant is defined based on the effluent standard limit and the total phosphorus concentration standard limit, and this range includes the nitrogen and phosphorus removal targets. Influent water quality fluctuation analysis is performed based on the instantaneous water quality data to obtain influent water quality fluctuation data. Multi-factor collaborative dynamic analysis is then conducted based on the influent water quality fluctuation data, the nitrogen and phosphorus removal targets, and the real-time process dataset to construct a real-time control instruction set.

[0100] Furthermore, the system is also used to implement the following functions:

[0101] A 3D model of the skid-mounted AOA pilot plant is constructed based on the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone, forming a 3D solid model. Online water quality monitoring sensors for each reaction zone of the skid-mounted AOA pilot plant are synchronously mapped to the 3D solid model for spatial alignment, creating multiple spatial monitoring points. Digital twins are created between these spatial monitoring points and the 3D solid model to construct a digital twin space. The real-time control command set is mapped to the skid-mounted AOA pilot plant for positioning, identifying multiple command execution mechanisms. These multiple command execution mechanisms are loaded into the digital twin space for identification, identifying multiple virtual components, which correspond to the multiple command execution mechanisms. The real-time control command set is assigned to these virtual components for simulation operation, generating simulation results. Based on the simulation results, the skid-mounted AOA pilot plant is driven to perform operational analysis, generating multiple operational reaction process parameters.

[0102] Furthermore, the system is also used to implement the following functions:

[0103] The real-time control command set is assigned to the multiple virtual components for hydraulic residence analysis, and a simulation time step is set. Virtual water flow calculations are performed on the multiple virtual components according to the simulation time step to obtain virtual three-dimensional water flow parameters. Based on the virtual three-dimensional water flow parameters, pollutant concentration analysis is performed on each reaction zone of the skid-mounted AOA pilot plant, and a pollutant concentration change trend diagram is plotted. A virtual clock is constructed based on the simulation time step, and the multiple virtual components run continuously according to the virtual clock until the simulation duration threshold is reached. Monitoring data from multiple spatial monitoring points are extracted to generate the simulation operation results.

[0104] Furthermore, the system is also used to implement the following functions:

[0105] The actuators of the skid-mounted AOA pilot plant are driven based on the simulation results. The actuators adjust the opening of the electric regulating valve connecting the sludge return pump to the anoxic zone, collecting first operating reaction parameters. The actuators also adjust the opening of the electric regulating valve connecting the sludge return pump to the anaerobic zone, collecting second operating reaction parameters. The actuators adjust the aeration device in the aerobic reaction zone for feedforward-feedback composite control, collecting third operating reaction parameters. The first, second, and third operating reaction parameters are mapped to the actuators of the skid-mounted AOA pilot plant for process identification, generating an operating process identifier. The operating process identifiers are used to perform synchronous verification of each reaction zone of the skid-mounted AOA pilot plant, generating synchronous verification results. Conflict analysis is performed based on the synchronous verification results. When there are no conflicts in the synchronous verification results, the first, second, and third operating reaction parameters are integrated to generate the multiple operating reaction process parameters.

[0106] Furthermore, the system is also used to implement the following functions:

[0107] The aeration device in the aerobic reaction zone is adjusted by the actuator, and a feedforward and feedback loops are set. A real-time influent dataset is calculated based on the feedforward loop, and aeration demand is matched according to the real-time influent dataset to determine the basic aeration demand. The aerobic reaction zone is monitored at the end of the aerobic reaction zone based on the feedback loop to generate an end-of-pipe monitoring dataset, which includes dissolved oxygen data and ammonia nitrogen concentration data. Based on the dissolved oxygen data and the ammonia nitrogen concentration data, the following judgments are made: S1: Oxygen concentration analysis is performed based on the dissolved oxygen data to obtain dissolved oxygen concentration data. If the dissolved oxygen concentration data is continuously higher than a preset upper limit threshold, an aeration reduction command is generated. S2: If the ammonia nitrogen concentration data is higher than a preset concentration threshold, an aeration enhancement command is generated. The aeration reduction command or the aeration enhancement command is used as a feedback adjustment amount. The basic aeration demand is superimposed with the feedback adjustment amount to construct an aeration intensity command. Composite control is performed based on the aeration intensity command to generate the third operating reaction parameter.

[0108] Furthermore, the system is also used to implement the following functions:

[0109] Based on the multiple operational reaction process parameters, deviation calculations are performed to identify multiple deviation items, each containing multiple deviation directions, and these deviation items correspond to the multiple deviation directions. Inversion analysis is then performed based on the multiple deviation items and directions to construct an inversion correction vector. This inversion correction vector is then superimposed and fused with the real-time control instruction set to generate a correction instruction, which includes an instruction execution priority. The correction instruction is executed according to the instruction execution priority, and a sludge assessment time window is initiated to perform high-frequency data acquisition on the skid-mounted AOA pilot plant, generating parameters showing changes before and after control. Dynamic evaluation is then performed based on these parameters to construct a wastewater treatment strategy.

[0110] Furthermore, the system is also used to implement the following functions:

[0111] Based on the multiple deviation terms, perform principal deviation analysis to define principal deviation terms; match the multiple deviation directions according to the principal deviation terms to extract principal deviation directions; perform inversion analysis according to the principal deviation directions and principal deviation terms to generate inversion deviation values; correct the inversion deviation values ​​according to the principal deviation directions to generate correction directions; integrate the inversion deviation values ​​and correction directions to construct the inversion correction vector.

[0112] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A wastewater treatment method based on a skid-mounted AOA pilot plant, characterized in that, The method includes: Real-time process datasets are generated by real-time sensing through online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant. The skid-mounted AOA pilot plant includes an anaerobic reaction zone, an aerobic reaction zone, an anoxic reaction zone, and a sedimentation zone. Based on the online water quality monitoring sensors, sensor matching is performed on the anaerobic reaction zone, the aerobic reaction zone, the anoxic reaction zone, and the sedimentation zone. According to the matching results, anaerobic reaction zone sensor groups are deployed for the anaerobic reaction zone, anoxic reaction zone sensor groups are deployed for the anoxic reaction zone, aerobic reaction zone sensor groups are deployed for the aerobic reaction zone, and sedimentation zone sensor groups are deployed for the sedimentation zone. Based on the real-time process dataset, nitrogen and phosphorus removal targets are set, and multi-factor collaborative dynamic analysis is performed in accordance with the nitrogen and phosphorus removal targets and the real-time process dataset to construct a real-time control instruction set. Digital twin analysis is performed based on the real-time control command set to construct a digital twin space. The real-time control command set is then simulated and executed within this digital twin space to drive the skid-mounted AOA pilot plant for operational analysis, generating multiple operational reaction process parameters. These parameters include: 3D modeling of the skid-mounted AOA pilot plant based on the anaerobic reaction zone, aerobic reaction zone, anoxic reaction zone, and sedimentation zone to construct a 3D solid model; synchronously mapping the online water quality monitoring sensors of each reaction zone of the skid-mounted AOA pilot plant to the 3D solid model for spatial alignment, constructing multiple spatial monitoring points; and configuring these multiple spatial monitoring points... A digital twin is constructed by creating a digital twin space between the points and the three-dimensional entity model; the real-time control instruction set is mapped to the skid-mounted AOA pilot plant for positioning, and multiple instruction execution mechanisms are identified; the multiple instruction execution mechanisms are loaded into the digital twin space for identification, and multiple virtual components are identified, with a corresponding relationship between the multiple virtual components and the multiple instruction execution mechanisms; the real-time control instruction set is assigned to the multiple virtual components for simulation operation, and simulation operation results are generated; the simulation operation results drive the skid-mounted AOA pilot plant to perform operation analysis, and generate multiple operation reaction process parameters; Based on the inversion of the multiple operational reaction process parameters, the real-time control instruction set is synchronously corrected. Based on the correction instructions, the distribution of return sludge in the anaerobic reaction zone and the anoxic reaction zone is dynamically controlled through an electric regulating valve to construct a wastewater treatment strategy.

2. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 1, characterized in that, The method also includes real-time sensing using online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant to generate a real-time process dataset. Instantaneous water quality data is collected at the inlet of the skid-mounted AOA pilot plant. A sampling period is set, and the sensor group in the anaerobic reaction zone is triggered to perform real-time sensing according to the sampling period to obtain real-time data on the anaerobic phosphorus release environment. The aerobic reaction zone sensor group is triggered to perform real-time sensing according to the sampling cycle to obtain real-time data on the nitrification reaction status. The sensor group in the anoxic reaction zone is triggered to perform real-time sensing according to the sampling period to obtain real-time data on the denitrification process. The sensor group in the sedimentation zone is triggered to perform real-time sensing according to the sampling period to obtain real-time solid-liquid separation data. The real-time process dataset is constructed by aligning and binding the instantaneous water quality data, the real-time anaerobic phosphorus release environment status data, the real-time denitrification process data, the real-time nitrification reaction status data, and the real-time solid-liquid separation data according to the acquisition cycle.

3. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 2, characterized in that, Based on the real-time process dataset, nitrogen and phosphorus removal targets are set. Multi-factor collaborative dynamic analysis is performed according to these targets in conjunction with the real-time process dataset to construct a real-time control instruction set. The method includes: The effluent discharge standard is introduced to determine the target upper limit value, and the effluent concentration value is set according to the target upper limit value. The effluent concentration value includes the effluent standard limit value and the total phosphorus concentration standard limit value. Based on the effluent standard limit and the total phosphorus concentration standard limit, the target value range for nitrogen and phosphorus removal of the skid-mounted AOA pilot plant is defined, and the target value range for nitrogen and phosphorus removal includes the nitrogen and phosphorus removal targets; Based on the instantaneous water quality data, influent fluctuation analysis is performed to obtain influent water quality fluctuation data; Based on the influent water quality fluctuation data, and in accordance with the nitrogen and phosphorus removal targets and the real-time process dataset, a multi-factor collaborative dynamic analysis is performed to construct a real-time control instruction set.

4. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 1, characterized in that, The method of assigning the real-time control instruction set to the multiple virtual components for simulation operation and generating simulation results includes: The real-time control instruction set is assigned to the multiple virtual components for hydraulic residence analysis, and the simulation time step is set. The water flow is virtually calculated for the multiple virtual components according to the simulation time step to obtain the three-dimensional flow parameters of the virtual water flow. Based on the virtual three-dimensional water flow parameters, the pollutant concentration in each reaction zone of the skid-mounted AOA pilot plant was analyzed, and a pollutant concentration change trend diagram was drawn. A virtual clock is constructed based on the simulated time step. The multiple virtual components run continuously according to the virtual clock until the simulated duration threshold is reached. Monitoring data from multiple spatial monitoring points are extracted to generate the simulation results.

5. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 1, characterized in that, Based on the simulation results, the skid-mounted AOA pilot plant is driven to perform operational analysis, generating the multiple operational reaction process parameters. The method includes: The actuator of the skid-mounted AOA pilot plant is driven based on the simulation results. The opening of the electric regulating valve leading from the sludge return pump to the anoxic reaction zone is adjusted by the actuator, and the first operating reaction parameters are collected. The actuator adjusts the opening of the electric regulating valve leading from the sludge return pump to the anaerobic reaction zone, and collects the second operating reaction parameters. The aeration device in the aerobic reaction zone is adjusted by the actuator to perform feedforward-feedback composite control, and the third operating reaction parameters are collected. Based on the first operating reaction parameters, the second operating reaction parameters, and the third operating reaction parameters, the process identification is performed on the actuator of the skid-mounted AOA pilot plant to generate an operating process identifier; The operation synchronization verification of each reaction zone of the skid-mounted AOA pilot plant is performed according to the operation process identifier, and the operation synchronization verification results are generated. Based on the running synchronization verification results, conflict analysis is performed. When there is no conflict in the running synchronization verification results, the first running reaction parameter, the second running reaction parameter, and the third running reaction parameter are integrated to generate the multiple running reaction process parameters.

6. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 5, characterized in that, The aeration device in the aerobic reaction zone is adjusted by the actuator to perform feedforward-feedback composite control, and the third operating reaction parameter is collected. The method includes: The aeration device in the aerobic reaction zone is adjusted by the actuator, and feedforward and feedback links are set. The real-time influent dataset is calculated based on the feedforward link, and the aeration demand is matched according to the real-time influent dataset to determine the basic aeration demand. Based on the feedback loop, end-point monitoring is performed on the aerobic reaction zone to generate an end-point monitoring dataset, which includes dissolved oxygen data and ammonia nitrogen concentration data. The determination is made based on the dissolved oxygen data and the ammonia nitrogen concentration data: S1: Based on the dissolved oxygen data, perform oxygen concentration analysis to obtain dissolved oxygen concentration data. When the dissolved oxygen concentration data continues to be higher than the preset upper limit threshold, generate an aeration reduction command. S2: When the ammonia nitrogen concentration data is higher than the preset concentration threshold, an aeration enhancement command is generated; The aeration reduction command or the aeration enhancement command is used as a feedback adjustment amount; The basic aeration requirement is superimposed with the feedback adjustment amount to construct an aeration intensity command; The third operating reaction parameter is generated by performing composite control based on the aeration intensity command.

7. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 1, characterized in that, Based on the inversion of the multiple operational reaction process parameters, the real-time control command set is synchronously corrected. Based on the correction commands, the distribution of return sludge in the anaerobic and anoxic reaction zones is dynamically controlled via an electric regulating valve to construct a wastewater treatment strategy. The method includes: Based on the multiple operational reaction process parameters, deviation calculation is performed to identify multiple deviation items, which include multiple deviation directions, and there is a corresponding relationship between the multiple deviation items and the multiple deviation directions. Inversion analysis is performed based on the multiple deviation terms and multiple deviation directions to construct an inversion correction vector; The inversion correction vector is superimposed and fused with the real-time control instruction set to generate a correction instruction, which includes the instruction execution priority. Execute the correction command according to the priority of the command, start the sludge assessment time window to perform high-frequency data acquisition on the skid-mounted AOA pilot plant, and generate parameters that change before and after the control. Based on the dynamic evaluation of the changes in parameters before and after the regulation, a wastewater treatment strategy is constructed.

8. The wastewater treatment method based on a skid-mounted AOA pilot plant as described in claim 7, characterized in that, Inversion analysis is performed based on the multiple deviation terms and multiple deviation directions to construct an inversion correction vector. The method includes: Based on the multiple deviation terms, perform principal deviation analysis and define the principal deviation terms; Based on the main deviation item, the multiple deviation directions are matched to extract the main deviation direction; Inversion analysis is performed according to the main deviation direction and main deviation terms to generate inversion deviation values; Based on the inversion deviation value, a correction direction is generated according to the principal deviation direction; The inversion deviation value and the correction direction are integrated to construct the inversion correction vector.

9. A wastewater treatment system based on a skid-mounted AOA pilot plant, characterized in that, The system is used to perform the wastewater treatment method based on a skid-mounted AOA pilot plant as described in any one of claims 1-8, the system comprising: The real-time sensing module is used to perform real-time sensing through online water quality monitoring sensors deployed in each reaction zone of the skid-mounted AOA pilot plant, and generate real-time process datasets. The dynamic analysis module is used to set nitrogen and phosphorus removal targets based on the real-time process dataset, and to perform multi-factor collaborative dynamic analysis according to the nitrogen and phosphorus removal targets and the real-time process dataset to construct a real-time control instruction set. The digital twin analysis module is used to perform digital twin analysis based on the real-time control instruction set, build a digital twin space, simulate and execute the real-time control instruction set based on the digital twin space, drive the skid-mounted AOA pilot device to perform operation analysis, and generate multiple operation reaction process parameters. The control module is used to synchronously correct the real-time control instruction set by inverting the multiple operating reaction process parameters, and dynamically control the distribution of return sludge in the anaerobic reaction zone and the anoxic reaction zone through an electric regulating valve according to the correction instructions, so as to construct a wastewater treatment strategy.

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