Sewage treatment plant drainage scheduling method and system based on hydrodynamic model

Abstract

The invention provides a sewage treatment plant drainage scheduling method and system based on a fluid dynamic model, and relates to the technical field of sewage treatment and drainage scheduling. According to water quality and water quantity coupling historical data of a water inlet, water inlet load levels are divided, and a matching model of the water inlet load levels and drainage flow and frequency is built; collecting interception quantity and pipeline flow data at a water inlet grid, and performing association processing to obtain an interception influence coefficient reflecting the influence of grid interception on pipe network drainage; calculating an allowable drainage range of the pipe network by using a fluid dynamic model, calculating an optimal drainage parameter in the allowable range by combining a current load matching model and an interception influence coefficient, and identifying a hydraulic unbalance road section at the same time; and finally, the optimal parameter serves as a target, the valve opening degree of the unbalanced road section is adjusted, actual drainage of the pipe network can approach the target parameter, overall hydraulic balance is maintained, and drainage scheduling accuracy and pipe network stability are improved.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment and drainage scheduling technology, and in particular to a wastewater treatment plant drainage scheduling method and system based on a fluid dynamics model. Background Technology

[0002] In the drainage scheduling process of wastewater treatment plants, it is necessary to dynamically coordinate multiple factors such as influent load, biochemical reaction tank status, secondary sedimentation tank settling efficiency, and effluent quality. Traditional experience-based scheduling is difficult to cope with sudden changes in hydraulic load, fluctuations in pollutant concentration, or extreme weather events. It is necessary to use precise fluid dynamics models to simulate the hydraulic distribution, pollutant migration, and biochemical reaction processes within the plant, thereby achieving coordinated and optimized control of execution units such as drainage pumping stations, gates, and return pumps. Ultimately, this ensures stable effluent compliance, minimizes energy consumption, and enhances the system's anti-interference capabilities.

[0003] One targeted solution is an intelligent scheduling system based on the coupling of computational fluid dynamics (CFD) models and sensor data. This system collects data from pH, DO, SS, and flow sensors deployed at key nodes (such as the inlet, aeration tank, and secondary sedimentation tank), driving the CFD model to dynamically simulate the overall plant flow pattern, pollutant diffusion, and sedimentation efficiency. It then combines this data with model predictive control algorithms to generate scheduling commands. For example, by simulating the sludge concentration field distribution in the secondary sedimentation tank using CFD, the system dynamically adjusts the sludge return ratio and sludge discharge frequency to optimize solid-liquid separation and reduce energy consumption.

[0004] The core flaw of this approach lies in the contradiction between the high computational complexity of the CFD model and the real-time requirements. CFD solutions consume significant computational resources, leading to system response delays and making minute-level scheduling decisions difficult. Simultaneously, the model relies on precise boundary conditions and physical property parameters, while real wastewater contains complex and variable components, easily causing simulation results to deviate from actual operating conditions. Furthermore, the CFD model is highly sensitive to mesh quality and the selection of turbulence models, potentially exhibiting numerical instability under drastic fluctuations in operating conditions, requiring frequent manual calibration and weakening the system's autonomy. Summary of the Invention

[0005] The purpose of this application is to provide a wastewater treatment plant drainage scheduling method and system based on a fluid dynamics model, in order to solve the problems of scheduling response delay caused by the complexity of model calculations in the prior art, and the reliance on manual intervention due to the poor adaptability of the model.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides a wastewater treatment plant drainage scheduling method based on a fluid dynamics model, comprising:

[0007] Based on historical monitoring data of water quality and quantity coupled at the inlet of the sewage treatment plant, different inlet load levels are classified, and a matching relationship model between the inlet load level and the drainage flow rate and drainage frequency is established.

[0008] Simultaneously collect the interception volume of the inlet screen and the flow rate of the drainage pipe at the inlet screen of the sewage treatment plant. By correlating the interception volume and the flow rate data, calculate the interception influence coefficient, which reflects the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network.

[0009] The allowable drainage range of the drainage network is calculated using a fluid dynamics model. The matching relationship model corresponding to the current inflow load level and the interception influence coefficient are used as inputs. With the allowable drainage range as a constraint, the optimal drainage flow rate and the optimal drainage frequency that match the current inflow load level and are within the allowable drainage range are calculated.

[0010] Based on the aforementioned flow data, and combined with the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model simulation, hydraulic imbalance sections in the pipeline network with abnormal flow distribution are identified.

[0011] Using the optimal drainage flow rate and the optimal drainage frequency as target control parameters, the flow distribution of each pipe section is adjusted by automatically regulating the opening of valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

[0012] Optionally, the simultaneous collection of interception data of the influent screen and flow data of the drainage pipe at the influent screen of the wastewater treatment plant, and the calculation of an interception influence coefficient reflecting the degree of influence of the influent screen's interception status on the drainage capacity of the pipe network by correlating the interception data and the flow data, includes:

[0013] The interception volume of the inlet grille and the flow rate data of the drainage pipe are paired according to the time series to form multiple data pairs;

[0014] The changes in the interception amount and traffic data in the data pair between adjacent time points are calculated to obtain the interception amount change sequence and the traffic change sequence;

[0015] The changes at corresponding positions in the interception change sequence and the traffic change sequence are correlated and calculated, and the ratio of the interception change to the traffic change is taken as the preliminary influence coefficient for each time point.

[0016] Statistical analysis was performed on the preliminary impact coefficients at all time points. After removing outliers, the average value was calculated and used as the interception impact coefficient, which reflects the degree of influence of the inlet grille interception status on the drainage capacity of the pipe network.

[0017] Optionally, the step of calculating the allowable drainage range of the drainage network using a fluid dynamics model, taking the matching relationship model corresponding to the current influent load level and the interception influence coefficient as input, and using the allowable drainage range as a constraint, to calculate the optimal drainage flow rate and optimal drainage frequency that match the current influent load level and are within the allowable drainage range, includes:

[0018] The flow state of the drainage network is simulated by a fluid dynamics model to determine the maximum and minimum allowable drainage flow rate of the drainage network and define the allowable drainage range.

[0019] Obtain the recommended drainage flow rate and recommended drainage frequency corresponding to the current influent load level, and use the interception influence coefficient as an adjustment factor to calculate the adjusted drainage flow rate and drainage frequency;

[0020] Within the allowable drainage range, an optimization function is established with the adjusted drainage flow rate as the variable, and the optimal drainage flow rate is obtained by solving the optimization function;

[0021] The optimal drainage frequency is calculated based on the ratio between the optimal drainage flow rate and the adjusted drainage flow rate.

[0022] Optionally, the step of identifying hydraulically imbalanced sections in the pipeline network with abnormal flow distribution based on the flow data and combined with the hydraulic loss distribution of each section of the pipeline network obtained by simulation from a fluid dynamics model includes:

[0023] Collect flow data from multiple monitoring points in the drainage pipe network to form a pipe network flow dataset;

[0024] The hydraulic loss values ​​of each section of the pipeline network under different flow conditions are calculated by using a fluid dynamics model, and a hydraulic loss distribution model of the pipeline network is established.

[0025] Based on the hydraulic loss values ​​of each section of the pipeline network in the aforementioned pipeline network hydraulic loss distribution model, and combined with the pipeline network topology and the principle of fluid continuity, the expected flow rate of each section under the theoretical equilibrium state is calculated in reverse.

[0026] Extract the actual flow monitoring values ​​of each pipe segment from the pipeline flow dataset, and calculate the relative deviation rate between the actual flow monitoring values ​​of each pipe segment and the corresponding theoretical expected flow values;

[0027] The relative deviation rate is compared and analyzed with a preset deviation threshold. Based on the comparison and analysis results, all pipe sections with relative deviation rates exceeding the deviation threshold are marked, and the pipe sections are identified as hydraulically unbalanced sections.

[0028] Optionally, the step of using the optimal drainage flow rate and the optimal drainage frequency as target control parameters, and adjusting the flow distribution of each pipe segment by automatically adjusting the valve opening in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains overall hydraulic balance, includes:

[0029] Based on the spatial distribution of the hydraulically unbalanced road section, determine the control valves that need to be adjusted in the hydraulically unbalanced road section and their specific locations;

[0030] For the control valve, based on the difference between the target control parameters and the current actual drainage state, the required change in the opening of the control valve is calculated, and based on the change in opening, the opening of the corresponding control valve is adjusted by the automatic control system, thereby affecting the actual drainage state.

[0031] Collect flow data from each monitoring point in the pipeline network after adjustment, and calculate the degree of matching between the current actual drainage status and the target control parameters;

[0032] When the matching degree does not meet the predetermined requirements, the valve opening change is recalculated and iteratively adjusted until the actual drainage state stabilizes and approaches the target control parameters and maintains overall hydraulic balance.

[0033] Optionally, the step of classifying different influent load levels based on historical monitoring data coupling water quality and quantity at the wastewater treatment plant inlet, and establishing a matching relationship model between the influent load level and the discharge flow rate and discharge frequency, includes:

[0034] Historical water quality monitoring data and historical water volume monitoring data of the inlet are obtained, and the historical water quality parameter value and historical water volume parameter value at the same time point are multiplied to calculate the inlet load value at the time point.

[0035] The influent load value is divided into multiple levels, and each level corresponds to a specific load range;

[0036] For the influent load level, the corresponding drainage flow rate and drainage frequency value are extracted from the historical water volume monitoring data, and its statistical characteristic value is calculated through the drainage flow rate and drainage frequency value.

[0037] Based on the statistical characteristic values, a mapping relationship is established between the influent load level and the drainage flow rate and drainage frequency, wherein the mapping relationship is represented in the form of a lookup table.

[0038] Secondly, this application provides a wastewater treatment plant drainage scheduling system based on a fluid dynamics model, comprising:

[0039] A module is established to classify different influent load levels based on historical monitoring data of water quality and quantity coupled at the influent of a wastewater treatment plant, and to establish a matching relationship model between the influent load level and the discharge flow rate and discharge frequency.

[0040] The calculation module is used to simultaneously collect the interception volume of the inlet screen and the flow rate of the drainage pipe at the inlet screen of the sewage treatment plant. By performing correlation processing on the interception volume and the flow rate data, the interception influence coefficient, which reflects the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network, is calculated.

[0041] The second calculation module is used to calculate the allowable drainage range of the drainage network using a fluid dynamics model. It takes the matching relationship model corresponding to the current inflow load level and the interception influence coefficient as input, and the allowable drainage range as a constraint to calculate the optimal drainage flow rate and the optimal drainage frequency that match the current inflow load level and are within the allowable drainage range.

[0042] The identification module is used to identify hydraulically unbalanced sections in the pipeline network with abnormal flow distribution based on the flow data and the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model.

[0043] The adjustment module is used to take the optimal drainage flow rate and the optimal drainage frequency as target control parameters, and adjust the flow distribution of each pipe section by automatically adjusting the opening of the valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

[0044] Thirdly, this application provides an electronic device, comprising:

[0045] Memory, used to store computer programs;

[0046] A processor is configured to execute the computer program to implement the steps of the wastewater treatment plant drainage scheduling method based on a fluid dynamics model as described in the first aspect above.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the wastewater treatment plant drainage scheduling method based on a fluid dynamics model as described in the first aspect above.

[0048] The wastewater treatment plant drainage scheduling method based on a fluid dynamics model provided in this application establishes a matching relationship model between influent load and drainage strategy based on historical data, providing a scientific and forward-looking benchmark strategy for drainage scheduling and overcoming the lag of simple reaction. By simultaneously collecting data on bar screen interception and pipeline flow and calculating the interception impact coefficient, it can quantify the dynamic impact of physical blockage on drainage capacity, providing key disturbance parameters for optimized scheduling. By using the fluid dynamics model to calculate the safe drainage range and solve for the optimal solution, it can achieve economic optimization of drainage flow and frequency while ensuring the safety of the pipe network. By combining data and model simulation to identify hydraulically unbalanced sections, it can accurately locate pipe sections with abnormal flow distribution in the pipe network, achieving a deepening from macro-scheduling to micro-perception. By automatically adjusting valves with optimal parameters as the target, it can not only achieve the overall drainage target but also proactively eliminate hydraulic imbalance in the pipe network, realizing overall system balance and stable operation.

[0049] Furthermore, by performing time-series correlation and statistical analysis on the interception volume and flow data of the inlet grille, the accurate interception impact coefficient is dynamically calculated; by dynamically coupling the safety boundary, inlet load strategy, and influencing factors defined by the fluid dynamics model, constraint optimization is performed; and by comparing the theoretical values ​​of the model with the measured values, abnormal pipe sections are accurately diagnosed. The corresponding technical effects can be summarized as follows: it achieves accurate perception and quantification of the inlet grille status, ensures rapid convergence and solution of the optimization scheduling algorithm within the absolute safety boundary, and improves the accurate diagnostic capability for hidden faults within the pipeline network (such as blockages and leaks), thereby comprehensively enhancing the adaptability, safety, and reliability of the scheduling system. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A schematic flowchart of a wastewater treatment plant drainage scheduling method based on a fluid dynamics model provided in this application embodiment;

[0052] Figure 2 A flowchart illustrating a wastewater treatment plant drainage scheduling method based on a fluid dynamics model, provided as an embodiment of this application;

[0053] Figure 3 A flowchart illustrating a wastewater treatment plant drainage scheduling method based on a fluid dynamics model, provided as an embodiment of this application;

[0054] Figure 4 This is a schematic diagram of a wastewater treatment plant drainage scheduling system based on a fluid dynamics model, provided as an embodiment of this application. Detailed Implementation

[0055] In the process of wastewater treatment plant drainage scheduling, existing intelligent scheduling methods based on high-precision computational fluid dynamics models, such as CFD models, have improved the standardization of operation to some extent, but still have significant bottlenecks: First, CFD models have high computational complexity, and the solution time is difficult to meet the minute-level scheduling requirements, resulting in system response lag and inability to effectively cope with rapid fluctuations in influent load; Second, the models rely on fixed parameters and ideal boundary conditions, which have poor adaptability in the context of complex and variable actual water quality, and the simulation results are prone to deviating from the actual operating conditions, requiring frequent manual calibration, which seriously restricts the autonomy and reliability of the system.

[0056] To address the aforementioned issues, this application proposes a wastewater treatment plant drainage scheduling method based on a fluid dynamics model. This method establishes a matching model between influent load levels and drainage strategies, providing preset optimal scheduling benchmarks for different operating conditions. Furthermore, it introduces a correlation analysis between bar screen interception and pipeline flow rate, dynamically calculating the interception influence coefficient that characterizes the actual physical blockage level. Finally, these factors are embedded into a lightweight fluid dynamics model, using the hydraulic safety range of the pipe network as a constraint, to solve for the optimal drainage flow rate and frequency, and automatically adjust valves to eliminate hydraulic imbalance in the pipe network. This method, through a technical path of "tiered preset, dynamic sensing, coupled optimization, and closed-loop control," effectively overcomes the shortcomings of traditional CFD models in terms of computational redundancy and poor adaptability. It significantly improves response speed through model lightweighting and strategy pre-setting, and enhances the system's autonomous adaptability to complex operating conditions through the dynamic correction and optimization process of influence factors, thereby achieving safe, efficient, and adaptive drainage scheduling.

[0057] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0058] The core of this application is to provide a wastewater treatment plant drainage scheduling method based on a fluid dynamics model, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0059] S101. Based on the historical monitoring data of water quality and water quantity coupling at the inlet of the sewage treatment plant, different inlet load levels are divided, and a matching relationship model between the inlet load level and the drainage flow rate and drainage frequency is established.

[0060] Optionally, S101 may specifically include the following steps:

[0061] S1011. Obtain historical water quality monitoring data and historical water volume monitoring data of the inlet, and multiply the historical water quality parameter value and the historical water volume parameter value at the same time point to calculate the inlet load value at the time point;

[0062] S1012. Divide the influent load value into multiple levels, each level corresponding to a corresponding load range;

[0063] S1013. For the influent load level, extract the corresponding drainage flow rate and drainage frequency value from the historical water volume monitoring data, and calculate its statistical characteristic value through the drainage flow rate and drainage frequency value.

[0064] S1014. Based on the statistical characteristic values, establish a mapping relationship between the influent load level and the drainage flow rate and drainage frequency, wherein the mapping relationship is represented in the form of a lookup table.

[0065] In the above scheme, historical water quality monitoring data refers to the data obtained by monitoring the concentrations of various pollutants such as chemical oxygen demand (COD), ammonia nitrogen (NH3-N), and total suspended solids (TSS) in the influent of the wastewater treatment plant over a period of time; historical water volume monitoring data refers to the data recorded by monitoring and recording the instantaneous flow rate and cumulative flow rate of the influent during the same period; influent load value refers to the data characterizing the total amount of pollutants entering the wastewater treatment plant per unit time; drainage flow rate value refers to the volume of liquid passing through the drainage pipes per unit time; drainage frequency value refers to the number of times the drainage pump starts and stops per unit time; influent load level refers to the classification label obtained by dividing the historical influent load values ​​into intervals according to certain rules; and the matching relationship model refers to the data model stored in the form of a lookup table, which records the recommended drainage flow rate and recommended drainage frequency values ​​corresponding to different influent load levels, realizing the correspondence between load level and drainage parameters.

[0066] In this embodiment, firstly, historical water quality monitoring data and historical water quantity monitoring data of the inlet are obtained from the monitoring system of the sewage treatment plant via S1011. The water quality parameters and water quantity parameters at the same time are matched by timestamps. The water quality parameters are converted to kg / m³ by unit conversion. Then, the influent load value at that time is obtained by multiplying the water quality parameters and water quantity parameters. For example, at a certain time, the COD concentration is 300 mg / L (converted to 0.3 kg / m³) and the influent flow rate is 1000 m³ / h. The influent load value is 0.3 kg / m³ × 1000 m³ / h = 300 kg / h.

[0067] Secondly, all historical influent load values ​​are collected through S1012, and statistical analysis techniques are used to divide the influent load values ​​into intervals to form continuous and non-overlapping influent load levels. For example, the percentile method is used to divide the influent load values ​​into low load (<200kg / h), medium load (200-500kg / h), and high load (>500kg / h).

[0068] Next, based on the influent load level, S1013 uses data filtering technology to extract the drainage flow rate and drainage frequency values ​​corresponding to the time period of the historical water volume monitoring data. Then, statistical calculation technology is used to obtain the statistical characteristic values ​​of the drainage flow rate and drainage frequency values. For example, for the 50 time period data of the "medium load" level, the arithmetic mean method is used to calculate the average drainage flow rate = (total flow rate of each time period) / 50 = 800 m³ / h, and the average drainage frequency = (total frequency of each time period) / 50 = 4 times / h.

[0069] Finally, using data association technology in S1014, a mapping relationship is established between each influent load level and the corresponding drainage flow statistical characteristic value and drainage frequency statistical characteristic value. Then, the mapping relationship is presented in the form of a lookup table through tabular storage technology.

[0070] In practical applications, a wastewater treatment facility, in order to optimize drainage scheduling, retrieved historical water quality and flow monitoring data from the inlet over the past six months from its monitoring system. Data from the same time point was matched according to timestamps, and the COD concentration was converted from mg / L to kg / m³ (e.g., 250 mg / L to 0.25 kg / m³). This was then multiplied by the corresponding influent flow rate to obtain the influent load value: 0.25 kg / m³ × 800 m³ / h = 200 kg / h. Finally, a K-means clustering algorithm was used to classify all influent load values ​​into low load (<180 kg / h) and medium load (180-400 kg / h). The system is divided into three levels: low load (50 kg / h), medium load (>450 kg / h), and high load (>450 kg / h). Drainage data for each level and corresponding time period are selected from historical water monitoring data, and statistical characteristic values ​​are calculated: low load level: average drainage flow 500 m³ / h, average drainage frequency 2 times / hour; medium load level: average drainage flow 750 m³ / h, average drainage frequency 3 times / hour; high load level: average drainage flow 1000 m³ / h, average drainage frequency 5 times / hour. Finally, a mapping relationship is established between the three load levels and their corresponding drainage parameters, forming a lookup table stored in the scheduling system for subsequent scheduling use.

[0071] The overall scheme of S101 above calculates a comprehensive influent load index by integrating influent water quality and quantity data, and classifies it into multiple levels. Then, it analyzes the statistical laws of drainage operations under different load levels, and finally establishes a mapping relationship between load levels and drainage parameters. The whole method realizes the efficient transformation from raw data to regular knowledge, forming a drainage operation guidance tool based on data that can quickly respond to changes in influent conditions, which significantly improves the systematicness and foresight of drainage management.

[0072] S102. Simultaneously collect the interception volume of the inlet screen and the flow rate data of the drainage pipe at the inlet screen of the sewage treatment plant. By correlating the interception volume and the flow rate data, calculate the interception influence coefficient, which reflects the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network.

[0073] Optionally, S102 may specifically include the following steps:

[0074] S1021. Pair the interception volume of the inlet bar and the flow rate data of the drainage pipe according to the time series to form multiple data pairs;

[0075] S1022. Calculate the changes in the interception amount and traffic data in the data pair between adjacent time points to obtain the interception amount change sequence and the traffic change sequence.

[0076] S1023. Correlate the changes at corresponding positions in the interception change sequence and the traffic change sequence, and take the ratio of the interception change to the traffic change as the preliminary influence coefficient for each time point.

[0077] S1024. Perform statistical analysis on the preliminary influence coefficients at all time points, remove outliers and calculate the average value, and use the average value as the interception influence coefficient that reflects the degree of influence of the inlet grille interception status on the drainage capacity of the pipe network.

[0078] In the above scheme, the interception volume of the inlet screen refers to the mass of suspended solids, impurities, and other solid matter intercepted by the inlet screen per unit time; the flow rate data of the drainage pipe refers to the volume of liquid passing through the drainage pipe per unit time; the interception influence coefficient is a parameter obtained by correlating the interception volume and flow rate data. A data pair refers to a combination formed by pairing the interception volume and flow rate data at the same time point to ensure the time synchronization of the two types of data; the interception volume change sequence refers to a continuous data set formed after calculating the change in interception volume at adjacent time points; the flow rate change sequence refers to a continuous data set formed after calculating the change in flow rate data at adjacent time points; the preliminary influence coefficient is a temporary coefficient obtained by calculating the ratio of the change in interception volume to the change in flow rate at the same location; outliers are extreme values ​​in the preliminary influence coefficient that deviate from the majority of the data and need to be removed using statistical methods to ensure the accuracy of the results.

[0079] In the embodiments of this application, such as Figure 2 As shown, firstly, using time-series pairing technology via S1021, interception data and flow data arranged by timestamps are extracted from the bar screen monitoring sensor and pipeline flow sensor of the sewage treatment plant. Based on the collection timestamps of the two types of data, the interception data and flow data corresponding to the same timestamp are paired to form multiple data pairs. For example, a sewage treatment plant in a certain area collects data once per hour. On a certain day, the interception data is 5 kg and the flow rate is 800 m³ / h at 10:00, 6 kg and 780 m³ / h at 11:00, and 7 kg and 760 m³ / h at 12:00. After pairing by timestamp, three data pairs are formed: (10:00, 5 kg, 800 m³ / h), (11:00, 6 kg, 780 m³ / h), and (12:00, 7 kg, 760 m³ / h).

[0080] Secondly, using the difference calculation method in S1022, the interception amount and traffic data in the data pair are processed separately. Taking two adjacent time points as a group, the value of the later time point is subtracted from the value of the previous time point to obtain the interception amount change and traffic change of each group. All changes are arranged by timestamp to form the interception amount change sequence and traffic change sequence. For example, based on the above data pair, the interception amount change from 10:00 to 11:00 is 6kg-5kg=1kg, and the traffic change is 780m³ / h-800m³ / h=-20m³ / h; the interception amount change from 11:00 to 12:00 is 7kg-6kg=1kg, and the traffic change is 760m³ / h-780m³ / h=-20m³ / h, thus forming the interception amount change sequence [1kg, 1kg] and the traffic change sequence [-20m³ / h, -20m³ / h].

[0081] Next, in step S1023, the ratio calculation method is used to correlate the changes in the interception volume change sequence with the changes at corresponding positions in the flow rate change sequence. The result is used as the preliminary influence coefficient for each time point, and the formula is as follows: For example, the preliminary impact coefficient of a wastewater treatment plant during the time interval from 10:00 to 11:00. The preliminary influence coefficient for the 11:00-12:00 time interval = This forms a preliminary influence coefficient sequence [-0.05, -0.05].

[0082] Finally, the mean of all preliminary influence coefficients is calculated using S1024. and standard deviation This will exceed " "Values ​​within the range are identified as outliers and deleted. The remaining preliminary impact coefficients are then summed and divided by the number of data points. The resulting average value is used as the interception impact coefficient."

[0083] In practical applications, to quantify the impact of the influent bar screen on the drainage capacity of the pipe network, a wastewater treatment plant first installed a weighing interception sensor at the influent bar screen and an electromagnetic flow sensor in the downstream drainage pipe. After continuously collecting data for three days, the interception and flow data at the same time point were paired using time series pairing technology, forming 72 data pairs (e.g., (Day 1, 8:00 AM, 4.2 kg, 810 m³ / h), (Day 1, 9:00 AM, 4.8 kg, 800 m³ / h), etc.). Then, these data pairs were grouped according to adjacent time points. By subtracting the value from the previous time point from the value at the later time point, 71 changes in interception volume (e.g., 4.8kg - 4.2kg = 0.6kg, interception volume at 10:00 AM on day 1: 5.3kg - 4.8kg = 0.5kg, etc.) and 71 changes in flow rate (e.g., 800m³ / h - 810m³ / h = -10m³ / h, flow rate at 10:00 AM on day 1: 795m³ / h - 800m³ / h = -5m³ / h, etc.) are calculated, forming a corresponding change sequence; then, the preliminary influence coefficient for each interval is calculated according to the positional correspondence (e.g., ... , (etc.), resulting in 71 preliminary impact coefficients; finally, the mean of the preliminary impact coefficients was calculated to be -0.07 kg・h / m³ and the standard deviation was 0.02. Using the 3σ rule, the three extreme values ​​(such as -0.15 and -0.005) that exceeded the range of -0.07±0.06 (i.e. -0.13 to -0.01) were identified as outliers and deleted. The remaining 68 preliminary impact coefficients were summed and divided by 68 to obtain the final interception impact coefficient of -0.068 kg・h / m³, which was stored in the scheduling system for subsequent use.

[0084] The overall scheme of S102 described above effectively eliminates the interference of extreme data caused by accidental factors on the results through statistical analysis and outlier removal, ensuring the stability and reliability of the final interception impact coefficient. By using the mean calculation method to integrate dynamic impact data from multiple time intervals, the final coefficient can reflect the long-term average impact of grid interception on drainage capacity, avoiding the limitations of data from a single time interval. This allows the drainage scheduling strategy to fully adapt to changes in the grid interception status, reduces the deviation in the assessment of pipeline drainage capacity caused by ignoring the impact of the grid, and improves the scientificity and accuracy of the overall scheduling.

[0085] S103. Calculate the allowable drainage range of the drainage network using a fluid dynamics model. Take the matching relationship model corresponding to the current influent load level and the interception influence coefficient as inputs, and use the allowable drainage range as constraints to calculate the optimal drainage flow rate and optimal drainage frequency that match the current influent load level and are within the allowable drainage range.

[0086] Optionally, S103 may specifically include the following steps:

[0087] S1031. Simulate the water flow state of the drainage network using a fluid dynamics model, determine the maximum and minimum allowable drainage flow of the drainage network, and define the allowable drainage range;

[0088] S1032. Obtain the recommended drainage flow rate and recommended drainage frequency corresponding to the current influent load level, and use the interception influence coefficient as an adjustment factor to calculate the adjusted drainage flow rate and drainage frequency.

[0089] S1033. Within the allowable drainage range, establish an optimization function with the adjusted drainage flow rate as the variable, and obtain the optimal drainage flow rate by solving the optimization function;

[0090] S1034. The optimal drainage frequency is calculated based on the ratio between the optimal drainage flow rate and the adjusted drainage flow rate.

[0091] In the above scheme, the fluid dynamics model refers to a mathematical model that can simulate the state of water flow velocity, pressure, water level, etc. in the drainage network; the allowable drainage range refers to the drainage flow range calculated by the fluid dynamics model to ensure the safe operation of the network; the interception influence coefficient is a parameter that quantifies the degree of influence of the inlet bar interception state on the drainage capacity of the network; the adjustment factor refers to the correction term that converts the interception influence coefficient into a correction term that can be directly used to adjust the drainage parameters; the optimization function refers to a mathematical function established with the adjusted drainage flow as a variable and a specific operating objective as the core; the optimal drainage flow refers to the drainage flow value that best meets the preset objective by solving the optimization function within the allowable drainage range; and the optimal drainage frequency refers to the drainage frequency value that is adapted to the optimal drainage flow, calculated based on the ratio between the optimal drainage flow and the adjusted drainage flow.

[0092] In the embodiments of this application, such as Figure 3As shown, firstly, using fluid dynamics simulation technology in S1031, basic data of the drainage network is collected and input into the fluid dynamics model to construct a digital model consistent with the actual network. Then, different drainage flow conditions are set in the model to simulate the water flow state in the network under each condition. Based on the network's safe operation standards, the maximum and minimum allowable drainage flow rates are determined, and the interval between the maximum and minimum allowable drainage flow rates is defined as the allowable drainage range. For example, for the drainage network of a sewage treatment plant in a certain area, after inputting parameters such as pipe diameter 300mm, length 500m, and pipe material HDPE, simulation using the SWMM model reveals that when the drainage flow rate exceeds 850m³ / h, the water level in the upstream No. 1 inspection well exceeds the top of the pipe by 0.1m. When the drainage flow rate is below 300m³ / h, the flow velocity in the downstream No. 3 pipe section is only 0.4m / s. Therefore, the maximum allowable drainage flow rate is determined to be 850m³ / h, the minimum allowable drainage flow rate is 300m³ / h, and the allowable drainage range is defined as 300-850m³ / h.

[0093] Secondly, in step S1032, data query technology is used to extract the recommended drainage flow rate and recommended drainage frequency corresponding to the current influent load level from the matching relationship model. Then, parameter retrieval technology is used to obtain the interception influence coefficient. Using the interception influence coefficient as an adjustment factor, a parameter correction algorithm is employed to calculate the adjusted drainage flow rate and drainage frequency. The calculation formula is as follows: , The recommended drainage flow rate and recommended drainage frequency are derived from the matching relationship model, and the interception influence coefficient is used to correct the impact of the bar screen interception on the drainage parameters. For example, if the current influent load is medium load, the recommended drainage flow rate extracted from the matching relationship model is 800 m³ / h, the recommended drainage frequency is 4 times / hour, and the interception influence coefficient is -0.068. Substituting these values ​​into the formula to calculate the adjusted drainage flow rate, first calculate the value in parentheses as 1 + (-0.068) / 10 = 0.9932, then multiply the recommended drainage flow rate of 800 m³ / h by 0.9932 to obtain the adjusted drainage flow rate as 800 × 0.9932 = 794.56 m³ / h. Similarly, the adjusted drainage frequency is 4 × 0.9932 ≈ 3.97 times / hour.

[0094] First, the optimization objectives are clearly defined through S1033. Based on the operational needs of the wastewater treatment plant (such as reducing energy consumption, improving drainage efficiency, and ensuring effluent quality), the core optimization direction is determined. Based on the determined optimization objectives and the characteristic parameters of the drainage equipment (such as energy consumption-flow curves and efficiency-flow curves), an optimization function is established. If the optimization objective is "minimum energy consumption," then based on the correlation between equipment energy consumption and flow rate, a function is constructed with drainage flow rate as the independent variable and energy consumption as the dependent variable. The formula is: Q represents the drainage flow rate (Q∈[minimum drainage flow rate, maximum drainage flow rate]), and a, b, and c are coefficients obtained by fitting based on equipment characteristics. A numerical solution algorithm (such as gradient descent) is selected, and the adjusted drainage flow rate is used as the initial value for iteration and substituted into the optimization function to calculate the objective function value corresponding to the initial value. Then, iterative calculation is started: the flow rate value is gradually adjusted according to the algorithm logic. After each adjustment, it is first determined whether the new flow rate is within the allowable range. If it is within the range, a new objective function value is calculated, compared with the previous value, and the flow rate value that makes the objective function better is retained. If it exceeds the range, the adjusted value is discarded and the adjustment direction is replanned. When the iteration stopping condition is met, the iteration is stopped. Finally, the flow rate value corresponding to the termination of iteration is determined as the optimal drainage flow rate, and it is verified whether the value is within the allowable drainage range. If it is within the range, it is directly output. If it slightly exceeds the range due to algorithm error, the closest value within the range is taken as the final result.

[0095] Finally, using the proportional calculation method in S1034, the optimal drainage frequency is calculated based on the ratio between the optimal drainage flow rate and the adjusted drainage flow rate. The calculation formula is as follows: .

[0096] In practical applications, during the drainage scheduling of a wastewater treatment plant, a SWMM model was used to construct a drainage network model for the plant area. Basic parameters such as pipe diameter, pipe length, number of inspection wells, and pipe material were input to simulate the network water level and flow velocity under different flow rates. It was found that when the flow rate exceeded 850 m³ / h, the water level in inspection well No. 1 overflowed; when it was below 300 m³ / h, the flow velocity in pipe section No. 5 was below 0.6 m / s, making it prone to siltation. The allowable drainage range was determined to be 300-850 m³ / h. Next, the matching relationship model was consulted. The current influent load was medium load, corresponding to a recommended drainage flow rate of 800 m³ / h and a recommended drainage frequency of 4 times / hour. The interception influence coefficient of -0.068 was retrieved. Calculate the adjusted parameters: adjusted drainage flow rate = 800 × (1 + (-0.068) / 10) = 794.56 m³ / h; adjusted drainage frequency = 4 × 0.9932 ≈ 3.97 times / hour. Then, establish an optimization function with the objective of "minimizing drainage pump energy consumption". Based on the characteristics of the A-brand drainage pump in the factory, determine the coefficients a = 0.0012, b = 0.45, and c = 95. The optimization function is as follows: Within the range of 300-850 m³ / h, the gradient descent method using Python was employed. Initially, 794.56 m³ / h was used as the initial value, and the initial energy consumption was calculated as 0.0012 × 794.56² + 0.45 × 794.56 + 95 ≈ 1209.83 kW / h. After 20 iterations, when the flow rate was 740 m³ / h, the energy consumption was 0.0012 × 740² + 0.45 × 740 + 95 ≈ 1085.12 kW / h, thus determining 740 m³ / h as the optimal drainage flow rate. Finally, the optimal drainage frequency was calculated proportionally as 3.97 × (740 / 794.56) ≈ 3.7 times / hour. Based on the plant's equipment control practices, this was fine-tuned to 4.0 times / hour, and the optimal parameters of 740 m³ / h and 4 times / hour were sent to the drainage control system for execution.

[0097] The overall scheme of S103 described above uses a fluid dynamics model to simulate the water flow state of the pipe network, clarifying the allowable drainage range to ensure the safe operation of the pipe network and avoiding problems such as siltation and overflow caused by drainage parameters exceeding the pipe network's carrying capacity. The recommended parameters are corrected using an interception influence coefficient, ensuring that the drainage parameters are adapted to the actual impact of the bar screen interception state, thus improving the adaptability of parameters to operating conditions. By establishing and solving an optimization function, parameter optimization guided by preset objectives is achieved, balancing the safety and economy of pipe network operation. The entire process organically combines model simulation, actual influencing factors, and optimization objectives, ensuring that the final optimal drainage parameters meet both pipe network safety requirements and operational efficiency needs, providing a reliable basis for the stable and scientific scheduling of the wastewater treatment plant's drainage system.

[0098] S104. Based on the flow data, and combined with the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model simulation, identify the hydraulically unbalanced sections of the pipeline network with abnormal flow distribution.

[0099] Optionally, S104 may specifically include the following steps:

[0100] S1041. Collect flow data from multiple monitoring points in the drainage pipe network to form a pipe network flow dataset;

[0101] S1042. Calculate the hydraulic loss values ​​of each section of the pipeline network under different flow conditions using a fluid dynamics model, and establish a hydraulic loss distribution model for the pipeline network.

[0102] S1043. Based on the hydraulic loss values ​​of each section of the pipeline network in the pipeline network hydraulic loss distribution model, and combined with the pipeline network topology and the principle of fluid continuity, the expected flow rate of each section under the theoretical equilibrium state is calculated in reverse.

[0103] S1044. Extract the actual flow monitoring value of each pipe segment from the pipeline flow dataset, and calculate the relative deviation rate between the actual flow monitoring value of each pipe segment and the corresponding theoretical expected flow value.

[0104] S1045. The relative deviation rate is compared and analyzed with a preset deviation threshold. Based on the comparison and analysis results, all pipe sections with relative deviation rates exceeding the deviation threshold are marked, and the pipe sections are identified as hydraulically unbalanced sections.

[0105] In the above scheme, flow data refers to the actual drainage flow rate collected by each monitoring point in the drainage network; fluid dynamics model refers to a mathematical model that can simulate water flow movement and calculate hydraulic losses within the network; hydraulic loss distribution in each segment of the network refers to the distribution of energy losses caused by friction, local resistance, etc., in each pipe segment of the network, calculated by the fluid dynamics model; hydraulically unbalanced sections refer to pipe segments in the network where the actual flow rate deviates from the theoretical expected flow rate by more than a preset threshold; network flow dataset refers to a structured data set containing monitoring point locations, collection times, and flow rates; network topology refers to the connection relationship between pipe segments and nodes in the network; theoretical expected flow rate refers to the benchmark flow rate of a pipe segment under ideal equilibrium conditions, calculated based on hydraulic losses and fluid principles; and relative deviation rate refers to an indicator that quantifies the degree of deviation between actual and expected flow rates.

[0106] In this embodiment of the application, firstly, multi-point flow monitoring technology is adopted through S1041 to install flow sensors at key nodes of the pipeline network, collect flow data at a fixed frequency, remove invalid data, and organize it into a pipeline network flow dataset containing "monitoring point number - collection time - flow value"; for example, a sewage treatment plant in a certain area installs electromagnetic flow sensors at 5 key monitoring points, collects data continuously for 24 hours, and forms a dataset of 470 valid data, each record containing the information "No. 1 - 2025-08-28 - 08:00 - 780 m³ / h".

[0107] Secondly, by using S1042 hydraulic loss simulation technology, the parameters of each section of the pipeline network are input into the fluid dynamics model. Multiple flow conditions are set to simulate and calculate the hydraulic loss values ​​of each pipe section under different conditions, and the results are compiled into a hydraulic loss distribution model of the pipeline network. For example, the parameters of 10 pipe sections of the sewage treatment plant in area A are input into the SWMM model, and it is found that the loss of pipe section 1 is 0.3m and the loss of pipe section 2 is 0.25m under the condition of 300m³ / h. Based on this, a loss distribution model is established.

[0108] Next, using reverse calculation technology in S1043, the hydraulic loss distribution model and the pipeline topology are correlated. With the actual total inflow rate as a constraint, the expected flow rate of each pipe section under the theoretical equilibrium state is calculated based on the principle of fluid continuity.

[0109] Then, using deviation calculation technology in S1044, the 24-hour average flow rate of each pipe section is extracted from the flow dataset as the actual flow monitoring value. The expected flow rate value is then retrieved using the formula: Calculate the relative deviation rate, where the actual flow rate monitoring value is the average flow rate of the pipe section, and the theoretical expected flow rate value is the calculated baseline value. For example, the actual flow rate of pipe section 2 is 420 m³ / h, and the expected flow rate is 500 m³ / h. The relative deviation rate is |420-500| / 500×100%=16%.

[0110] Finally, using threshold comparison technology in S1045, a deviation threshold is preset based on pipeline operation experience. The deviation rate of each pipe section is compared with the threshold, the pipe section exceeding the threshold is marked and verified a second time. After confirmation, it is determined to be a hydraulically unbalanced section.

[0111] In practical applications, at a wastewater treatment plant, electromagnetic flow sensors were first installed at five key nodes in the pipe network. Data was collected over 24 hours at a frequency of 15 minutes per data point, resulting in a pipe network flow dataset of 477 valid data points. Next, the parameters of 12 pipe segments (400mm / HDPE main pipe, 300mm / concrete branch pipe) were input into the EPANET model, setting operating conditions of 300 / 500 / 700 m³ / h to establish a hydraulic loss distribution model. Then, a model was established using an average flow rate of 500 m³ / h in the main pipe. Using m³ / h as a constraint, the expected flow rate of each pipe section is calculated according to the principle of fluid continuity (500 m³ / h for pipe section 1, 243 m³ / h for branch pipe 2, etc.); then the actual average flow rate of each pipe section is extracted (420 m³ / h for branch pipe 2, 280 m³ / h for branch pipe 3, etc.), and the deviation rate is calculated (72.8% for branch pipe 2, 8.9% for branch pipe 3, etc.); finally, a preset threshold of 15% is set, and after comparison, pipe sections 2, 6, and 10 are marked. After a second verification that the sensor and the calculation are correct, the three sections are determined to be hydraulically unbalanced sections.

[0112] The overall solution of S104 described above obtains real flow data through multi-point monitoring, providing a reliable basis for analysis; combines fluid model calculation to realize the scientific analysis of the hydraulic characteristics of the pipeline network; calculates the expected flow based on topology and fluid principles to ensure the rationality of theoretical values; accurately identifies unbalanced sections by comparing deviation rate with threshold, avoiding the subjectivity of experience-based judgment; and finally provides clear objectives for pipeline maintenance, reduces blind inspections, and provides a basis for optimizing operating parameters and improving drainage efficiency, thereby alleviating local drainage problems caused by hydraulic imbalance.

[0113] S105. Using the optimal drainage flow rate and the optimal drainage frequency as target control parameters, the flow distribution of each pipe section is adjusted by automatically adjusting the opening of the valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

[0114] Optionally, S105 may specifically include the following steps:

[0115] S1051. Based on the spatial distribution of the hydraulically unbalanced road section, determine the control valves that need to be adjusted in the hydraulically unbalanced road section and their specific locations;

[0116] S1052. For the control valve, based on the difference between the target control parameters and the current actual drainage state, calculate the amount of change in the opening of the control valve that needs to be adjusted, and adjust the opening of the corresponding control valve through the automatic control system according to the amount of change in the opening, thereby affecting the actual drainage state.

[0117] S1053. Collect flow data from each monitoring point in the pipeline network after adjustment, and calculate the degree of matching between the current actual drainage status and the target control parameters;

[0118] S1054. When the matching degree does not meet the predetermined requirements, recalculate the valve opening change and iteratively adjust until the actual drainage state stabilizes and approaches the target control parameters and maintains overall hydraulic balance.

[0119] In the above scheme, the target control parameters are the optimal drainage flow rate and the optimal drainage frequency; the pipe section with hydraulic imbalance where the actual flow rate deviates from the theoretical expected flow rate by more than a preset threshold; the control valve refers to the equipment installed on the pipe section of the pipeline network that has the function of adjusting the opening; the change in opening refers to the percentage increase or decrease in the opening of the control valve to make the actual flow rate of the pipe section approach the target value, which is calculated by a specific algorithm combined with hydraulic characteristics; the automatic control system refers to the system that can receive adjustment commands and drive the valve actuator to act; the current actual drainage status refers to the actual flow rate, actual frequency and other data of each monitoring point in the pipeline network before and after adjustment, reflecting the operation of the pipeline network; the matching degree refers to the index that quantifies the degree of fit between the actual drainage status and the target control parameters; the predetermined requirement refers to the preset qualified threshold of the matching degree.

[0120] In this embodiment, firstly, the control valves and their locations to be adjusted are determined through S1051. Spatial distribution data of hydraulically unbalanced sections are obtained using a pipeline GIS system. The influence weight of pipe sections on flow distribution within the section is analyzed using hydraulic simulation software to screen out key regulating pipe sections. Then, the control valves on the pipe sections and their installation coordinates are located. For example, in the pipeline network of a sewage treatment plant, there is a hydraulic imbalance in the section from node D1 to D3. The GIS system clarifies that the section includes two pipe sections, D1-D2 and D2-D3. Hydraulic simulation analysis shows that the flow deviation of these two pipe sections dominates the overall imbalance. Finally, the regulating valves are determined to be valve V5 (coordinate P1) on the D1-D2 pipe section and valve V6 (coordinate P2) on the D2-D3 pipe section.

[0121] Next, through S1052, the actual data such as the current flow rate and drainage frequency of the corresponding pipe section of the control valve are collected through the pipeline sensor network. The data is compared with the target parameters such as the optimal drainage flow rate and the optimal drainage frequency to obtain the deviation value. Then, the PID control algorithm is used to calculate the valve opening change based on the deviation value. The formula is: opening change = proportional coefficient × deviation + integral coefficient × cumulative deviation + derivative coefficient × deviation change rate. Finally, the automatic control execution system drives the valve to complete the opening adjustment.

[0122] Then, the actual flow rate and frequency data after adjustment are collected by the intelligent sensors at the pipeline monitoring points through S1053, and calculated according to the deviation rate formula ( Calculate the deviation rate of each parameter and take the average value as the overall matching degree.

[0123] Finally, the overall matching degree is compared with the predetermined qualified standard through S1054. If the standard is not met, the process of collecting the current data, calculating the new opening change using the PID algorithm, and automatically adjusting the valve is repeated until the matching degree meets the standard.

[0124] In practical applications, at a wastewater treatment plant, based on the pipeline network drawings and the EPANET model, it was determined that pipe section 2 (A2-A3) is regulated by the central V2 valve (15m from node A2), pipe section 6 (A6-A7) is controlled by the inlet V6 valve, and pipe section 10 (A10-A11) is regulated by the central V10 valve (12m from node A10). The target flow rates were 243 m³ / h for pipe section 2, 310 m³ / h for pipe section 6, and 185 m³ / h for pipe section 10. The actual measured flow rate by the sensor was 420 m³ / h for pipe section 2. The flow rates of pipelines 250 m³ / h for pipeline 6 and 220 m³ / h for pipeline 10 were adjusted automatically using a PID algorithm. After 30 minutes of adjustment, the average values ​​of the three sets of data were taken: 258 m³ / h for pipeline 2, 302 m³ / h for pipeline 6, and 190 m³ / h for pipeline 10. The deviation rates were calculated to be approximately 6.2%, 2.6%, and 2.7% respectively, with an overall matching degree of approximately 3.8%, below the 15% threshold. After continuous monitoring for 2 hours, the matching degree stabilized between 1.5% and 3.0%, confirming that the pipeline network was approaching the target state and the balancing adjustment was complete.

[0125] The overall solution of S105 described above achieves precise control of the distribution of pipeline flow by locating key control valves and intelligently calculating their opening adjustment amount; through the rapid response and closed-loop regulation mechanism of the automatic control system, it promotes the actual drainage state to converge stably to the target value; and finally, through continuous state feedback and iterative optimization, it effectively eliminates the hydraulic imbalance problem and significantly improves the stability, control accuracy and adaptability of the drainage system.

[0126] The following is a complete example for steps 101-105. In a wastewater treatment plant, historical monitoring data of the inlet over the past 6 months is first retrieved, including water quality indicators such as COD and NH3-N, and influent flow rate. Data from the same time point is matched using timestamps, and the water quality concentration unit is converted to kg / m³ before being multiplied by the flow rate to obtain the influent load value (e.g., a COD concentration of 250 mg / L is converted to 0.25 kg / m³, and combined with a flow rate of 800 m³ / h, the calculated load value is 200 kg / h). The K-means clustering algorithm is used to divide all load values ​​into three levels: low load, medium load, and high load. Then, the drainage operation records for each level during the corresponding time period are extracted, and statistical characteristic values ​​are calculated: low load level average drainage flow rate 500 m³ / h, frequency 2 times / hour; medium load level 750 m³ / h, 3 times / hour; high load level 1000 m³ / h, 5 times / hour. Finally, a mapping relationship table between the three load levels and drainage parameters is established and stored in the system.

[0127] Then, the system synchronously collects data on the interception volume of the inlet grille and the flow rate of the drainage pipe. Data pairs are formed by time series pairing, for example, three consecutive hours of data (8:00, 4.2 kg, 810 m³ / h), (9:00, 4.8 kg, 800 m³ / h), and (10:00, 5.3 kg, 795 m³ / h). Then, the changes in adjacent time periods are calculated: at 9:00, the interception volume changes by +0.6 kg and the flow rate changes by -10 m³ / h; at 10:00, the interception volume changes by +0.5 kg and the flow rate changes by -5 m³ / h. Based on this, the preliminary influence coefficient is calculated (9:00: 0.6 / (-10) = -0.06 kg·h / m³; 10:00: 0.5 / (-5) = -0.1 kg·h / m³). After removing outliers that deviate from the range of μ±3σ, the average of the remaining coefficients is used to obtain the interception influence coefficient of -0.068 kg·h / m³.

[0128] Next, the system simulates the pipeline network operation using a fluid dynamics model. Based on parameters such as pipe diameter and material, the safe drainage range is determined to be 300-850 m³ / h. The current influent load is medium load, and recommended parameters of 800 m³ / h and 3 times / hour are extracted from the mapping table. Combined with the interception influence coefficient of -0.068, the adjusted flow rate is 800 × (1 - 0.068 / 10) = 794.56 m³ / h, and the adjusted frequency is 3 × 0.9932 ≈ 2.98 times / hour. An optimization function is established with the goal of "minimizing energy consumption" (coefficients a = 0.0012, b = 0.45, c = 95). Within the safe range, the optimal drainage flow rate of 740 m³ / h is obtained, and the optimal frequency is calculated proportionally to be 2.98 × (740 / 794.56) ≈ 2.78 times / hour.

[0129] Finally, based on the flow data from the pipeline monitoring points and the simulation results of the fluid model, the system identified three hydraulically unbalanced pipeline sections, including branch pipe No. 2 (actual flow rate 420 m³ / h, expected flow rate 243 m³ / h, deviation rate 72.8%). After locating the key regulating valve V5 (inlet of branch pipe No. 2), it was calculated that its opening needed to be increased by 12% to balance the flow. The automatic control system adjusted the valve opening, and monitoring showed that after adjustment, the flow rate of branch pipe No. 2 dropped to 260 m³ / h (deviation rate reduced to 7%), but branch pipe No. 4 showed a new deviation. After iterative calculation, the system adjusted V5 and the associated valve V7 a second time. After three rounds of adjustment, the flow deviation rate of all pipe sections was less than 15%, and the actual total drainage flow rate stabilized at 735 m³ / h, with a matching degree of >99% with the target of 740 m³ / h. The overall pipeline network achieved hydraulic balance.

[0130] Figure 4 This application provides a schematic diagram of a specific implementation of a wastewater treatment plant drainage scheduling system based on a fluid dynamics model, referring to... Figure 4 The system may include:

[0131] Module 41 is established to classify different influent load levels based on historical monitoring data of water quality and quantity coupling at the influent of the sewage treatment plant, and to establish a matching relationship model between the influent load level and the drainage flow rate and drainage frequency.

[0132] The calculation module 42 is used to simultaneously collect the interception amount of the inlet screen and the flow rate data of the drainage pipe at the inlet screen of the sewage treatment plant. By performing correlation processing on the interception amount and the flow rate data, the interception influence coefficient reflecting the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network is calculated.

[0133] The second calculation module 43 is used to calculate the allowable drainage range of the drainage network using a fluid dynamics model. It takes the matching relationship model corresponding to the current inflow load level and the interception influence coefficient as input, and uses the allowable drainage range as a constraint to calculate the optimal drainage flow rate and the optimal drainage frequency that match the current inflow load level and are within the allowable drainage range.

[0134] The identification module 44 is used to identify hydraulically unbalanced sections in the pipeline network with abnormal flow distribution based on the flow data and the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model.

[0135] The adjustment module 45 is used to take the optimal drainage flow rate and the optimal drainage frequency as target control parameters, and adjust the flow distribution of each pipe section by automatically adjusting the opening of the valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

[0136] The wastewater treatment plant drainage scheduling system based on the fluid dynamics model in this application embodiment is used to implement the aforementioned wastewater treatment plant drainage scheduling method based on the fluid dynamics model. Therefore, the specific implementation of the wastewater treatment plant drainage scheduling system based on the fluid dynamics model can be found in the embodiment section of the wastewater treatment plant drainage scheduling method based on the fluid dynamics model above. The specific implementation can be referred to the description of the corresponding embodiments, and will not be repeated here.

[0137] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the wastewater treatment plant drainage scheduling method based on the fluid dynamics model described above.

[0138] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the wastewater treatment plant drainage scheduling method based on the fluid dynamics model described above.

[0139] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0140] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the wastewater treatment plant drainage scheduling method based on a fluid dynamics model.

[0141] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0142] The above provides a detailed description of a wastewater treatment plant drainage scheduling method and system based on a fluid dynamics model, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A wastewater treatment plant drainage scheduling method based on a fluid dynamics model, characterized in that, include: Based on historical monitoring data of water quality and quantity coupled at the inlet of the sewage treatment plant, different inlet load levels are classified, and a matching relationship model between the inlet load level and the drainage flow rate and drainage frequency is established. Simultaneously collect the interception volume of the inlet screen and the flow rate of the drainage pipe at the inlet screen of the sewage treatment plant. By correlating the interception volume and the flow rate data, calculate the interception influence coefficient, which reflects the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network. The allowable drainage range of the drainage network is calculated using a fluid dynamics model. The matching relationship model corresponding to the current inflow load level and the interception influence coefficient are used as inputs. With the allowable drainage range as a constraint, the optimal drainage flow rate and the optimal drainage frequency that match the current inflow load level and are within the allowable drainage range are calculated. Based on the aforementioned flow data, and combined with the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model simulation, hydraulic imbalance sections in the pipeline network with abnormal flow distribution are identified. Using the optimal drainage flow rate and the optimal drainage frequency as target control parameters, the flow distribution of each pipe section is adjusted by automatically regulating the opening of valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

2. The method according to claim 1, characterized in that, Simultaneously, data on the interception volume of the influent screen and the flow rate of the drainage pipe are collected at the influent screen of the wastewater treatment plant. By correlating the interception volume and the flow rate data, an interception influence coefficient reflecting the degree of influence of the influent screen's interception status on the drainage capacity of the pipe network is calculated, including: The interception volume of the inlet grille and the flow rate data of the drainage pipe are paired according to the time series to form multiple data pairs; The changes in the interception amount and traffic data in the data pair between adjacent time points are calculated to obtain the interception amount change sequence and the traffic change sequence; The changes at corresponding positions in the interception change sequence and the traffic change sequence are correlated and calculated, and the ratio of the interception change to the traffic change is taken as the preliminary influence coefficient for each time point. Statistical analysis was performed on the preliminary impact coefficients at all time points. After removing outliers, the average value was calculated and used as the interception impact coefficient, which reflects the degree of influence of the inlet grille interception status on the drainage capacity of the pipe network.

3. The method according to claim 1, characterized in that, The allowable drainage range of the drainage network is calculated using a fluid dynamics model. Taking the matching relationship model corresponding to the current influent load level and the interception influence coefficient as input, and using the allowable drainage range as a constraint, the optimal drainage flow rate and optimal drainage frequency that match the current influent load level and are within the allowable drainage range are calculated, including: The flow state of the drainage network is simulated by a fluid dynamics model to determine the maximum and minimum allowable drainage flow rate of the drainage network and define the allowable drainage range. Obtain the recommended drainage flow rate and recommended drainage frequency corresponding to the current influent load level, and use the interception influence coefficient as an adjustment factor to calculate the adjusted drainage flow rate and drainage frequency; Within the allowable drainage range, an optimization function is established with the adjusted drainage flow rate as the variable, and the optimal drainage flow rate is obtained by solving the optimization function; The optimal drainage frequency is calculated based on the ratio between the optimal drainage flow rate and the adjusted drainage flow rate.

4. The method according to claim 1, characterized in that, Based on the aforementioned flow data, and combined with the hydraulic loss distribution of each section of the pipeline network obtained from the fluid dynamics model simulation, hydraulically unbalanced sections in the pipeline network with abnormal flow distribution are identified, including: Collect flow data from multiple monitoring points in the drainage pipe network to form a pipe network flow dataset; The hydraulic loss values ​​of each section of the pipeline network under different flow conditions are calculated by using a fluid dynamics model, and a hydraulic loss distribution model of the pipeline network is established. Based on the hydraulic loss values ​​of each section of the pipeline network in the aforementioned pipeline network hydraulic loss distribution model, and combined with the pipeline network topology and the principle of fluid continuity, the expected flow rate of each section under the theoretical equilibrium state is calculated in reverse. Extract the actual flow monitoring values ​​of each pipe segment from the pipeline flow dataset, and calculate the relative deviation rate between the actual flow monitoring values ​​of each pipe segment and the corresponding theoretical expected flow values; The relative deviation rate is compared and analyzed with a preset deviation threshold. Based on the comparison and analysis results, all pipe sections with relative deviation rates exceeding the deviation threshold are marked, and the pipe sections are identified as hydraulically unbalanced sections.

5. The method according to claim 4, characterized in that, The process of calculating the hydraulic losses of different sections of the pipeline network under different flow conditions using a fluid dynamics model and establishing a hydraulic loss distribution model for the pipeline network includes: Identify all pipe segments in the drainage network that need to be calculated, and assign a unique identifier to each pipe segment; Multiple different flow rate conditions can be set, and these flow rate conditions cover a range from minimum to maximum flow rate. Based on the pipe diameter, pipe length, and pipe inner wall roughness parameters of the pipe section, calculate the hydraulic loss value of the pipe section under the flow rate condition; The hydraulic loss values ​​of all pipe segments under all flow conditions are organized with the unique identifiers of the pipe segments and the flow condition values ​​to form a structured data set; Based on the structured dataset, a hydraulic loss distribution model for the pipeline network is established. This model can output the corresponding hydraulic loss value by inputting the pipe segment identifier and flow rate value.

6. The method according to claim 1, characterized in that, Using the optimal drainage flow rate and the optimal drainage frequency as target control parameters, the flow distribution of each pipe segment is adjusted by automatically regulating the valve opening in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains overall hydraulic balance, including: Based on the spatial distribution of the hydraulically unbalanced road section, determine the control valves that need to be adjusted in the hydraulically unbalanced road section and their specific locations; For the control valve, based on the difference between the target control parameters and the current actual drainage state, the required change in the opening of the control valve is calculated, and based on the change in opening, the opening of the corresponding control valve is adjusted by the automatic control system, thereby affecting the actual drainage state. Collect flow data from each monitoring point in the pipeline network after adjustment, and calculate the degree of matching between the current actual drainage status and the target control parameters; When the matching degree does not meet the predetermined requirements, the valve opening change is recalculated and iteratively adjusted until the actual drainage state stabilizes and approaches the target control parameters and maintains overall hydraulic balance.

7. The method according to claim 1, characterized in that, The method, based on historical monitoring data coupling water quality and quantity at the inlet of the wastewater treatment plant, classifies different influent load levels and establishes a matching relationship model between the influent load levels and the discharge flow rate and discharge frequency, including: Historical water quality monitoring data and historical water volume monitoring data of the inlet are obtained, and the historical water quality parameter value and historical water volume parameter value at the same time point are multiplied to calculate the inlet load value at the time point. The influent load value is divided into multiple levels, and each level corresponds to a specific load range; For the influent load level, the corresponding drainage flow rate and drainage frequency value are extracted from the historical water volume monitoring data, and its statistical characteristic value is calculated through the drainage flow rate and drainage frequency value. Based on the statistical characteristic values, a mapping relationship is established between the influent load level and the drainage flow rate and drainage frequency, wherein the mapping relationship is represented in the form of a lookup table.

8. A wastewater treatment plant drainage scheduling system based on a fluid dynamics model, characterized in that, include: A module is established to classify different influent load levels based on historical monitoring data of water quality and quantity coupled at the influent of a wastewater treatment plant, and to establish a matching relationship model between the influent load level and the discharge flow rate and discharge frequency. The calculation module is used to simultaneously collect the interception volume of the inlet screen and the flow rate of the drainage pipe at the inlet screen of the sewage treatment plant. By performing correlation processing on the interception volume and the flow rate data, the interception influence coefficient, which reflects the degree of influence of the inlet screen interception status on the drainage capacity of the pipe network, is calculated. The second calculation module is used to calculate the allowable drainage range of the drainage network using a fluid dynamics model. It takes the matching relationship model corresponding to the current inflow load level and the interception influence coefficient as input, and the allowable drainage range as a constraint to calculate the optimal drainage flow rate and the optimal drainage frequency that match the current inflow load level and are within the allowable drainage range. The identification module is used to identify hydraulically unbalanced sections in the pipeline network with abnormal flow distribution based on the flow data and the hydraulic loss distribution of each section of the pipeline network obtained by the fluid dynamics model. The adjustment module is used to take the optimal drainage flow rate and the optimal drainage frequency as target control parameters, and adjust the flow distribution of each pipe section by automatically adjusting the opening of the valves in the hydraulically unbalanced section, so that the actual drainage state of the pipe network approaches the target control parameters and maintains the overall hydraulic balance.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the wastewater treatment plant drainage scheduling method based on a fluid dynamics model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the wastewater treatment plant drainage scheduling method based on a fluid dynamics model as described in any one of claims 1 to 7.

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