Multi-section sewage treatment integrated system

Abstract

The invention relates to a multi-section sewage treatment integrated system applied to the technical field of sewage treatment, which comprises a treatment section arranged along the flow direction, and a standard function module capable of dynamically gating is arranged in the treatment section; the intelligent fluid routing layer comprises a central multi-dimensional distributor and is used for dynamically reconstructing fluid connection between modules; and a space intelligent control hub. The space intelligent control center constructs a digital twinborn body and calculates a module connection topology with optimal efficiency based on real-time data through a process topology optimization model, and the model calculates a topology reconstruction comprehensive index by distributing dynamically configurable weights for a process efficiency memory index, a connection efficiency prediction index and a cross-module synergistic effect index. And selecting the topology with the highest comprehensive index as the optimal scheme. According to the invention, online intelligent dynamic reconstruction of a treatment process chain is realized, and the self-adaptive capability, the treatment efficiency and the operation energy efficiency of the system to water quality fluctuation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and more specifically, to a multi-stage integrated wastewater treatment system. Background Technology

[0002] Traditional wastewater treatment systems, whether classic stationary processes (such as A...), 2 Whether it's an oxidation ditch or a modular integrated system that has emerged in recent years, the connection relationship (i.e., process topology) of its core processing units is determined during construction or installation, or selected from a few preset paths. Optimization during system operation is mainly limited to adjusting operating parameters such as dissolved oxygen, reflux ratio, and dosage, which is a local optimization within a fixed architecture.

[0003] This fundamental limitation results in existing systems lacking sufficient adaptability when facing dynamic fluctuations in water quality and quantity, or when switching treatment objectives (such as from "standard compliance" to "energy-saving operation"). They cannot dynamically "assemble" optimal treatment paths based on real-time demands, like reconfigurable building blocks or software-defined networks. Therefore, developing a wastewater treatment system capable of dynamically reconfiguring its internal process topology during operation to achieve global and adaptive optimization has become a pressing technical problem in this field. Summary of the Invention

[0004] The present invention aims to overcome the fundamental defects of existing sewage treatment systems, such as fixed process topology and inability to be dynamically reconfigured, and to provide a multi-stage integrated sewage treatment system and its control method that can intelligently and dynamically reconfigure the treatment process according to real-time influent conditions and operating objectives.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a multi-stage integrated wastewater treatment system, comprising: multiple treatment sections arranged sequentially along the treatment flow direction, wherein at least one treatment section is composed of multiple standardized functional modules that can be dynamically selected;

[0007] The intelligent fluid routing layer includes at least one central multidimensional distributor, which is a multi-inlet and multi-outlet fluid switch matrix used to dynamically reconstruct the fluid connection path between standardized functional modules according to control commands, so as to change the process topology within its processing section.

[0008] The spatial intelligent control center is used for:

[0009] Build and update a digital twin that is synchronized with the system in real time. The digital twin integrates the real-time status and performance data of standardized functional modules.

[0010] Based on the data from the digital twin, the optimal module connection topology and module status commands are dynamically calculated through a process topology optimization model.

[0011] Among them, the process topology optimization model evaluates and selects the optimal topology by calculating the topology reconfiguration comprehensive index. The topology reconfiguration comprehensive index is calculated by assigning dynamically configurable weights to the process efficiency memory index, connection efficiency prediction index and cross-module synergy effect index and then comprehensively calculating it. The optimal topology refers to the topology with the highest topology reconfiguration comprehensive index.

[0012] Furthermore, the process efficiency memory index is generated based on the historical operation database and is used to quantify the historical average treatment efficiency of each candidate module connection topology under the current influent water quality conditions. Each candidate topology is a connection sequence composed of standardized functional modules.

[0013] Furthermore, the connectivity efficiency prediction index is generated based on the current state of the intelligent fluid routing layer, the switching actions required for the target topology, and pipeline cleanliness prediction, and is used to quantify the speed and stability costs of the topology switching process.

[0014] Furthermore, the cross-module synergy index is calculated based on historical synergy data or theoretical biochemical models of module combinations, and is used to quantify the processing efficiency gain generated when a specific combination of modules is used together.

[0015] Furthermore, the comprehensive index of topology reconstruction The following formula is used to calculate:

[0016] ;

[0017] in, , , Candidate topologies The process efficiency memory index, connection efficiency prediction index, and cross-module synergy index; The weighting coefficients are dynamically configurable and associated with the real-time operation objectives of the system (in this invention, "real-time operation objectives of the system" refers to specific performance indicators that need to be prioritized for optimization, which include, for example, the lowest energy consumption, the fastest pollutant removal rate (to cope with shock loads), and the highest operational stability; the control center drives the dynamic configuration of the weighting coefficients according to the selected objectives).

[0018] Furthermore, the standardized functional modules are connected to the central multi-dimensional distributor via hydraulic self-sealing quick connectors, and the standardized functional modules include at least three of the following: intelligent pretreatment module, anaerobic / anoxic switchable module, aerobic / short-range nitrification module, bioelectrochemical targeted module, advanced deep treatment module, membrane separation barrier module, energy recovery module, and sludge resource utilization module.

[0019] Furthermore, the intelligent pretreatment module integrates an internal carbon source intelligent fermentation zone, and an anaerobic / anoxic switchable module is used to receive the carbon source provided by the internal carbon source intelligent fermentation zone and perform denitrification treatment.

[0020] Furthermore, the space intelligent control center is also used for:

[0021] Real-time monitoring of sudden changes in influent water quality or the decline in the treatment efficiency of the current topology;

[0022] When the rate of change exceeds a preset threshold or the performance degradation reaches a preset level, the process topology optimization model is automatically recalculated and reconstructed.

[0023] Furthermore, the dynamically configurable weights are dynamically adjusted according to the real-time operating objectives of the system, including energy efficiency optimization objectives (e.g., the lowest energy consumption) or shock load resistance objectives (e.g., the fastest pollutant removal rate or the highest operating stability).

[0024] Secondly, the present invention provides a control method for the above-mentioned multi-stage integrated wastewater treatment system, comprising the following steps:

[0025] S1: Real-time collection of influent and effluent water quality, operating status and health data of each standardized functional module in the system, and updating of the digital twin;

[0026] S2: Based on the historical operating data of the digital twin and the system, calculate the process efficiency memory index of each candidate topology under the current conditions;

[0027] S3: Based on the current physical state of the digital twin, intelligent fluid routing layer, and standardized functional modules, predict the connection efficiency forecast index for performing topology switching.

[0028] S4: Evaluate the cross-module synergy index among module combinations based on digital twins, historical synergy data, or theoretical biochemical models;

[0029] S5: Assign dynamically configurable weights to the process efficiency memory index, connection efficiency prediction index, and cross-module synergy effect index, and calculate the comprehensive topology reconstruction index for each candidate topology.

[0030] S6: Select the optimal topology based on the topology reconstruction comprehensive index and generate instructions to drive the central multidimensional allocator in the intelligent fluid routing layer to perform reconstruction.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. This invention, through the combination of standardized functional modules and a central multi-dimensional distributor, realizes the software definition of the wastewater treatment process on an engineering scale. It can change the process chain connection within minutes according to instructions, fundamentally solving the problem of system rigidity and giving the system physical flexibility.

[0033] 2. Building upon dynamic reconfiguration capabilities, this invention upgrades reconfiguration decisions from simple sequential switching to a multi-objective quantitative optimization process through a comprehensive topology reconfiguration index model. The system can comprehensively consider historical experience, switching costs, and the biochemical synergies between modules, automatically selecting the topology scheme with the lowest overall cost or highest efficiency, thus achieving a leap from simply being able to reconfigure to intelligent and optimized reconfiguration.

[0034] 3. The combination of the above capabilities enables the system to proactively detect water quality shocks or performance degradation and automatically trigger optimization and reconfiguration, significantly improving its adaptability to complex operating conditions and its reliability in achieving stable compliance.

[0035] 4. Because it can always assemble a near-optimal treatment path for the current water quality and utilize the synergistic effect of modules, the system can significantly reduce unnecessary energy and material consumption while ensuring the quality of the effluent. Attached Figure Description

[0036] Figure 1 This is a system structure block diagram of the multi-stage integrated wastewater treatment system of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0038] Figure 1 This paper presents a multi-stage integrated wastewater treatment system that physically follows the classic wastewater treatment process. The system consists of multiple functionally defined treatment sections arranged sequentially along the treatment flow direction, such as pretreatment, biological treatment, and advanced treatment sections. At least one treatment section (e.g., the biological treatment section) is not a traditional single pool, but rather comprises multiple dynamically selectable standardized functional modules.

[0039] I. System Physical Structure

[0040] The core of the system lies in the following three levels:

[0041] 1. Standardized functional module library

[0042] The system is based on a standardized functional module library deployed in the processing sections. This library consists of various types and multiple copies of standardized functional modules. Each module has a unified fluid interface (inlet and outlet) and communication interface, forming a "standard Lego unit" for the variable process chain. Modules are connected to the main system pipeline via hydraulic self-sealing quick-connect couplings, allowing for commissioning, disconnection, or replacement without interrupting the water supply. The hydraulically driven self-sealing quick-connect couplings rely on fluid pressure to tighten the sealing ring during connection, achieving quick connection and reliable sealing, and supporting pressurized insertion and removal.

[0043] The standardized functional module library includes, but is not limited to, the following types of modules:

[0044] The intelligent pretreatment module (M-PRE) is implemented in one way as follows: it integrates a mechanical bar screen, a grit chamber, and an internal carbon source intelligent fermentation zone. This fermentation zone is equipped with temperature (T), pH, and oxidation-reduction potential (ORP) sensors. Through feedback control of the stirring rate and the addition of a small amount of readily degradable organic matter (such as glucose), the fermentation process is stabilized in the acid-producing phase to produce volatile fatty acids (VFAs). This "intelligence" is manifested in the dynamic adjustment of the fermentation intensity based on the influent carbon-nitrogen ratio.

[0045] Anaerobic / Anoxic Switchable Module (M-ANO): The module is filled with porous sintered ceramic particles modified with cationic polymers as enrichment packing for anaerobic ammonia oxidizing bacteria (Anammox). By controlling the dissolved oxygen in the influent (DO < 0.5 mg / L) and using an air-lift device for internal mixing, it can switch between anaerobic ammonia oxidation mode and anoxic denitrification mode.

[0046] Aerobic / Short-cut Nitrification Module (M-AER): The module is equipped with a microporous aeration disc and a precise dissolved oxygen (DO) probe, and the aeration rate is adjusted by a PID controller. It can be loaded with a TiO2 photocatalytic-microbial composite carrier to enhance the decomposition of recalcitrant organic matter under the assistance of ultraviolet light. By controlling DO at 0.5-1.0 mg / L and maintaining pH at 7.5-8.0, it can be stabilized in a short-cut nitrification state.

[0047] The bioelectrochemical targeted module (M-BIO-E) consists of one or more bioelectrochemical reactors equipped with carbon brushes or graphene-modified electrodes and inoculated with specific degrading microbial communities. By applying a low-intensity DC electric field (e.g., 0.3-0.8V), the co-metabolic degradation process of specific target pollutants (such as antibiotics and dyes) by microorganisms is enhanced, while simultaneously recovering some electrical energy.

[0048] Advanced Deep Treatment Module (M-AD): This module is designed to be switchable. When the influent toxicity sensor detects a value exceeding the limit, the electrocatalytic oxidation unit is automatically triggered, using boron-doped diamond electrodes to generate hydroxyl radicals for deep oxidation; otherwise, the water flow switches to an adsorption tower filled with gradient-pore-size ion exchange resin for selective adsorption.

[0049] Membrane Separation Barrier Module (M-MBR): Employs hollow fiber polyvinylidene fluoride ultrafiltration membranes with a graphene-titanium dioxide composite antifouling coating on the membrane surface. The membrane tank is divided into two zones, A and B, which can be operated alternately and chemically cleaned online via automatic valve control to ensure stable flux.

[0050] Sludge Resource Utilization Module (M-SRU): Integrates an ultrasonic crusher (frequency 20kHz, power density 0.5W / mL) and an ozone aerator (50 mg O3 / g SS) to jointly treat the remaining sludge (ultrasonic-ozone combined cell lysis). The supernatant after cell lysis (rich in carbon source) is pumped back to the front end of the system, and the residue is dewatered and phosphorus is recovered.

[0051] Energy recovery module (M-ER): includes anaerobic biogas production unit (connected to M-ANO or independent anaerobic module), micro hydroelectric turbine generator (utilizing water head difference) and rooftop photovoltaic panel to achieve diversified energy recovery.

[0052] 2. Intelligent Fluid Routing Layer

[0053] The core component of this layer is the central multi-dimensional distributor. One specific implementation uses an N×M fluid switching matrix composed of high-speed solenoid valves. For example, an 8-in, 8-out matrix is ​​constructed from 64 two-position, two-way high-speed solenoid valves connected according to a "fully interlocked logic." This logic ensures that at any given time, the control software or hardware circuitry allows each inlet to connect to at most one outlet, and each outlet to connect to at most one inlet, fundamentally preventing fluid cross-mixing or short circuits. The distributor connects to each module via quick-connect fittings through stainless steel piping; all connecting pipes are kept as short and straight as possible to reduce dead volume. The switching time (valve action + system response) can be controlled to within 10 seconds.

[0054] 3. Spatial Intelligent Control Center

[0055] The control center consists of an industrial computer, data acquisition cards, control software, and network equipment. Its software layer is implemented as follows:

[0056] Digital Twin Engine: Built upon a real-time database and 3D modeling software. Sensors (water quality, flow rate, pressure, temperature, current, vibration) deployed in various modules and pipelines collect data in real time via the OPC protocol and map it onto the corresponding virtual components in the twin. The twin not only displays the status but also integrates a real-time updated system performance dataset, which includes at least: instantaneous influent and effluent water quality for each module, current pollution load removal rate, real-time energy consumption (kW·h), and health scores for key equipment.

[0057] Process topology optimization model: This is the core decision-making algorithm running within the control center.

[0058] II. Core Algorithm Model

[0059] Unless otherwise stated, the term "process topology" as used below refers to a complete wastewater treatment path consisting of multiple standardized functional modules connected in a specific sequence.

[0060] The intelligence of this invention is reflected in the process topology optimization model, the core of which is the topology reconfiguration comprehensive index decision model. The specific implementation methods of its various components are described in detail below.

[0061] 1. Model Input and Candidate Topology Generation

[0062] When the system is triggered to make a decision, the model first uses a path generation algorithm to quickly enumerate all technically feasible candidate process topologies based on the currently available standardized functional modules and their performance characteristics, combined with the current processing objective. To clearly illustrate the core principle, this embodiment mainly describes the topology formed by connecting modules in a series sequence (i.e., the effluent from the previous module becomes the influent from the next module). For example, if the system currently has four available modules: M-PRE, M-ANO, M-AER, and M-MBR, the possible candidate topologies include: T1: M-PRE→M-AER→M-MBR; T2: M-PRE→M-ANO→M-AER→M-MBR. It can be understood that the intelligent fluid routing layer of this invention has the ability to construct more complex connections (such as diversion and merging), and the model can support the optimization of such topologies by extending the generation and evaluation rules.

[0063] 2. Specific calculation methods for the three core indices

[0064] For each candidate topology T, the model computes three indices in parallel:

[0065] (1) Process efficiency memory index calculate:

[0066] This index aims to quantify the average performance of topology T under historically similar conditions.

[0067] Data preparation: Historical operating database Each record in Fields included: timestamp, influent water quality vector (like The process topology coding used , Execution result vector (like (These represent COD removal rate, unit energy consumption coefficient, and operational stability score, respectively).

[0068] Similarity matching: Calculate the current influent water quality vector With all of history Euclidean distance Select the K historical records with the smallest distance to form a set of similar records. .

[0069] Performance extraction and averaging: From In the process, select all historical data subsets that use topology T. .calculate The average runtime vector of all records .

[0070] Exponentiation and normalization: Converting averages into scalar fractions. For example, using weighted sums: ;

[0071] in For preset weights (such as ). All candidate topologies Linear normalization to the [0,1] interval yields the respective process efficiency memory indexes. .

[0072] Calculation example: Assume that for candidate topology T1, three similar records are found from the historical database, and their average COD removal rate is... Average unit energy consumption Average stability .but If another topology T2 After normalization .

[0073] (2) Connection efficiency prediction index calculate:

[0074] This index is used to quantify the time and reliability costs required to switch to topology T.

[0075] Switching cost modeling: Define the cost function as: ;

[0076] in:

[0077] The number of valves in the central multidimensional distributor that need to change their state can be calculated directly by comparing the current connection matrix with the target matrix.

[0078] : Estimated pipeline flushing time. . The cumulative volume of the pipe section to be flushed is obtained from the pipe network geometry model of the digital twin; To design the flushing flow rate.

[0079] Risk factor during the switchover process. Calculated based on recent equipment failure history, for example: , Let j represent the probability of the j-th valve operating without failure over the past 30 days.

[0080] : These are the weighting coefficients obtained through system calibration, used to standardize the units (e.g.) ).

[0081] Exponential Calculation: The connectivity efficiency prediction exponent is inversely proportional to the cost, defined as follows: Similarly, for all candidate topologies Normalization is performed.

[0082] (3) Cross-module synergy index calculate:

[0083] This index is used to quantify the "1+1>2" gain produced by combining specific modules in topology T.

[0084] Historical data-based methods: using historical databases The study analyzed the actual removal rate of key pollutants (such as total nitrogen TN) when module A and module B were used in a specific order. Compared to the theoretical cumulative removal rate when the two modules process independently, Gain percentage : Typical of a specific combination The value can be used as the basic value of its synergistic effect. .

[0085] Theoretical model-based approach: For new combinations, kinetic models can be used for evaluation. For example, for recalcitrant organic compounds, a first-order reaction kinetic model can be established for the "module A → module B" combination: ,in The apparent rate constant when processed alone. The additional rate constant resulting from the synergistic effect. This can be obtained through fitting a small batch of experiments. The synergistic gain can be estimated as follows: .

[0086] For entirely new module combinations or systems in the initial operational phase where historical data is lacking, the baseline synergistic effect of each module combination can be set to a preset empirical value (e.g., 0, indicating no additional gain), or quickly determined through small-scale batch experiments. The system will automatically accumulate data and update this value during subsequent operation.

[0087] Topology-level exponential synthesis: For a process topology T containing n modules, its overall synergy exponent. This can be obtained by calculating the cooperative fundamental values ​​of all adjacent module pairs and then taking the geometric mean:

[0088] ;

[0089] in, Let be the basic cooperative value (normalized to the [0,1] interval) of the i-th module in topology T and its subsequent adjacent modules. Finally, Normalize to the [0,1] interval.

[0090] 3. Comprehensive decision-making and dynamic weight allocation

[0091] Weighted dynamic configuration rules: To achieve different real-time system operation goals, the system has several preset operation strategy configuration files, each strategy defining three indices. weight vector For example, in one embodiment, the system has the following preset policy configuration file:

[0092] To achieve the goal of "lowest energy consumption" (corresponding to the energy efficiency optimization goal), an extreme energy-saving strategy is adopted: (Pay close attention to historical energy efficiency and switching energy consumption);

[0093] To achieve the goal of "maximum operational stability" (corresponding to the shock load resistance target), a stability-first strategy is adopted: (Balanced consideration);

[0094] To achieve the goal of "fastest pollutant removal rate (responding to shock loads)" (corresponding to the shock load resistance target), a rapid response strategy is adopted: (High importance is attached to synergy in order to achieve the goals quickly);

[0095] When the operational objective changes, the system automatically switches strategies. The aforementioned operational objective (i.e., which strategy to employ) can be manually set by operators according to management needs, or it can be automatically determined and switched by the system based on preset advanced rules. For example, when the digital twin detects that the influent toxicity or concentration of key pollutants continuously exceeds the threshold within a short period, it can automatically switch the operational objective from "lowest energy consumption" to "fastest pollutant removal rate" and invoke the corresponding strategy configuration. Furthermore, smooth transition is supported: when the influent toxicity value... At the threshold and In between, the current weight Stability priority can be applied. And "rapid response" weight Interlinear interpolation:

[0096] ;

[0097] Calculation and selection of topology remodeling composite index:

[0098] For each candidate topology T, calculate its topology reconstruction comprehensive index: The model compares all candidate topologies. Value, selection The topology with the highest value is designated as the current "optimal performance" process topology. Subsequently, the connection instructions corresponding to this topology are output to the central multi-dimensional distributor, and status instructions for each module are simultaneously issued.

[0099] It should be noted that the specific parameters of the topology reconstruction comprehensive index decision model (such as weights) Cost coefficient Methods such as normalization can be used to calibrate and optimize wastewater treatment plants based on their water quality characteristics, equipment conditions, and operational experience. The core of this invention lies in proposing a multi-factor quantitative decision-making framework based on dynamic weights. The determination of specific parameters within this framework does not constitute a limitation of this invention.

[0100] Example

[0101] The following two specific embodiments further illustrate the operation process of the system of the present invention. The embodiments will strictly demonstrate the entire process from data perception to decision execution.

[0102] Example 1: Set the system operation target to "lowest energy consumption," and then adopt the corresponding "extreme energy-saving strategy."

[0103] S1: Real-time data collection and updating:

[0104] The inlet water sensor detected the following water quality: COD = 300 mg / L, NH3-N = 60 mg / L, C / N ≈ 5. The digital twin updates this data and displays that all modules are functioning normally.

[0105] S2-S4: Exponent Calculation:

[0106] The model employs an "extreme energy-saving" strategy. Three candidate topologies were generated and their indices were calculated. The index values ​​were calculated based on the historical database and system states from the simulation, according to the aforementioned algorithm. For example, topology T2 showed better historical performance and synergistic effects due to its ability to utilize internal carbon sources. The normalized calculation results are shown in Table 1.

[0107] Table 1: Candidate Topology Indices and Overall Scores in Example 1

[0108] Candidate topology T1:M-PRE→M-AER→M-MBR 0.70 0.90 0.20 0.79 T2:M-PRE→M-ANO→M-AER→M-MBR 0.90 0.60 0.80 0.77 T3: M-PRE→M-AER→M-MBR (different parameters) 0.80 0.85 0.30 0.77

[0109] S5-S6: Decision-making and Execution: The Comprehensive Index of T1 Maximum (0.79). System-generated instructions:

[0110] 1) The central multidimensional distributor reconstructs the flow path as M-PRE→M-AER→M-MBR;

[0111] 2) Instruct the M-AER to operate in high-efficiency nitrification mode (DO=2.0mg / L). The system will complete the switch within 1 minute.

[0112] Effect: In this decision-making process, although topology T2 exhibits a higher synergistic effect index due to its utilization of an internal carbon source ( However, under the preset "extreme energy saving" strategy, the algorithm focuses more on historical energy consumption performance. ) and switching costs ( Topology T1 has a shorter path and requires fewer valve movements for switching. The value is high), and in similar historical water quality data, its "direct aerobic treatment" mode has lower overall unit energy consumption because it avoids the mixing and stirring energy consumption of the anaerobic / anoxic stage. (High value), thus winning in the comprehensive score. After the system implemented this topology, the T2 topology, which has more complex measured operating data, saves about 15% energy. This demonstrates the ability of the model of this invention to perform quantitative trade-offs and optimal solution search under multi-objective constraints.

[0113] Example 2: In response to the impact of highly toxic industrial wastewater, the operational objective is automatically switched from "highest operational stability" to "fastest pollutant removal rate," and the corresponding strategy is switched from "stability priority" to "rapid response."

[0114] S1: Real-time data acquisition and updates: The online toxicity analyzer shows that the influent contains phenolic substances, with a concentration exceeding the standard by 50 mg / L, and COD jumps to 800 mg / L. The twin is marked as a "toxic shock event," automatically switching the operating target to "fastest pollutant removal rate" and adopting the corresponding "rapid response strategy." ).

[0115] S2-S4: Exponential Calculation: Model calculation of candidate topologies. Topology T5 (M-BIO-E), containing reinforcing units, exhibits a strong synergistic effect between electrochemical and biological treatments. The value is significantly higher, giving it an advantage under the "rapid response" weight. The calculation results are shown in Table 2.

[0116] Table 2: Candidate Topology Indices and Overall Scores for Example 2

[0117] Candidate topology T4: M-PRE→M-AER→M-MBR 0.30 0.90 0.30 0.48 T5:M-PRE→M-BIO-E→M-AER→M-MBR 0.50 0.70 0.90 0.76 T6:M-PRE→M-BIO-E→M-AER→M-AD→M-MBR 0.60 0.50 0.85 0.695

[0118] S5-S6: Decision-making and Execution: T5 wins. System Instructions:

[0119] 1) The central multidimensional distributor connects the M-BIO-E module to form the M-PRE→M-BIO-E→M-AER→M-MBR flow path;

[0120] 2) Instruct the M-BIO-E to start and apply a 0.6V voltage, and the M-AER to switch to shock-resistant mode.

[0121] Results: The system completed reconstruction within 3 minutes. The electrochemical pretreatment effectively neutralized the toxic substances, reduced the load on subsequent biological treatment, and the toxicity index of the effluent returned to a safe range in a short period of time.

[0122] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A multi-stage wastewater treatment integrated system, characterized in that, Comprise: a plurality of processing sections arranged in sequence along a processing flow direction, at least one of the processing sections being composed of a plurality of dynamically-gated standardized functional modules; an intelligent fluid routing layer comprising at least one central multi-dimensional distributor, the central multi-dimensional distributor being a multi-inlet multi-outlet fluid switch matrix for dynamically reconfiguring fluid connection paths between the standardized functional modules according to control instructions to change a process topology within the processing section where the standardized functional modules are located; a spatial intelligent control hub for: constructing and updating in real time a digital twin synchronized with the system, the digital twin integrating real-time state and performance data of the standardized functional modules; based on data of the digital twin, dynamically calculating a module connection topology and module state instructions that are optimal in performance by a process topology optimization model; wherein the process topology optimization model evaluates and selects an optimal topology by calculating a topology reconfiguration comprehensive index, the topology reconfiguration comprehensive index being calculated by assigning dynamically-configurable weights to a process performance memory index, a connection efficiency prediction index, and a cross-module synergistic effect index, and the optimal topology being a topology with the highest topology reconfiguration comprehensive index.

2. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The process performance memory index is generated based on a historical operation database, for quantifying historical average treatment performance of each candidate module connection topology under current influent water quality conditions, wherein each candidate topology is a connection sequence composed of the standardized functional modules.

3. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The connection efficiency prediction index is generated based on a current state of the intelligent fluid routing layer, switching actions required by a target topology, and a prediction of pipe cleanliness, for quantifying speed and stability costs of a topology switching process.

4. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The cross-module synergistic effect index is generated based on historical synergistic data of module combinations or calculated by a theoretical biochemical model, for quantifying performance gains produced by using specific module combinations.

5. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The topology reconfiguration comprehensive index is calculated according to the following formula: ； wherein, , , are the process performance memory index, the connection efficiency prediction index and the cross-module synergy index of the candidate topology , respectively; is a dynamically configurable weight coefficient associated with the real-time running target of the system.

6. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The standardized functional modules are connected to the central multi-dimensional distributor by hydraulic self-sealing quick couplings, and the standardized functional modules comprise at least three of an intelligent pretreatment module, an anaerobic / anaerobic switchable module, an aerobic / shortcut nitrification module, a biological electrochemical targeted module, an advanced deep treatment module, a membrane separation barrier module, an energy recovery module, and a sludge resourceization module.

7. The multi-stage wastewater treatment integrated system according to claim 6, wherein, The intelligent pretreatment module integrates an internal carbon source intelligent fermentation zone, and the anaerobic / anaerobic switchable module is used to receive carbon sources provided by the internal carbon source intelligent fermentation zone and perform denitrification treatment.

8. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The spatial intelligent control hub is further used for: real-time monitoring of mutations in influent water quality or performance degradation of a current topology; when a change rate exceeds a preset threshold or performance degradation reaches a preset degree, automatically triggering re-computation and topology reconfiguration of the process topology optimization model.

9. The multi-stage wastewater treatment integrated system according to claim 1, wherein, The dynamically-configurable weights are dynamically adjusted according to real-time operation targets of the system, the operation targets including an energy efficiency optimization target or an anti-shock load target.

10. A control method for the multi-stage sewage treatment integrated system according to any one of claims 1 to 9, characterized by, Comprise the following steps: real-time acquisition of influent and effluent water quality, operation state, and health degree data of each standardized functional module in the system, and updating of a digital twin; based on the digital twin and historical operation data of the system, calculation of process performance memory indexes of each candidate topology under current conditions; Based on the digital twin and the current physical state of the intelligent fluid routing layer and the standardized functional modules, a connection efficiency prediction index for performing topology switching is predicted; Based on the digital twin, historical synergistic data or theoretical biochemical models, a cross-module synergistic effect index between module combinations is evaluated; The process performance memory index, the connection efficiency prediction index and the cross-module synergistic effect index are assigned dynamically configurable weights, and a topology reconstruction comprehensive index of each candidate topology is calculated; According to the topology reconstruction comprehensive index, the optimal topology is selected, and instructions are generated to drive the central multidimensional distributor in the intelligent fluid routing layer to perform reconstruction.

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