Wastewater treatment multistage dynamic quantitative control system based on result feedback

Abstract

The invention relates to the technical field of wastewater treatment in the water treatment technology, in particular to a wastewater treatment multistage dynamic quantification control system based on result feedback, which comprises a storage module, an identification module, a quantification module and an execution module, the storage module stores a causal chain in a reaction process, the identification module identifies a matching state of a current operation parameter and the causal chain and enables the quantification module to determine a risk index, and the execution module performs control optimization to execute corresponding measures based on the determined risk index. Tracking intervention from a pollution source to a derivation phenomenon is realized, a pollution migration problem implied in wastewater treatment is converted into an identification item of quantitative operation, and limitation of traditional single-point identification control is broken through through sequential logic verification and a multi-dimensional matching degree fusion mechanism. The accuracy of membrane pollution prevention and control and the resource utilization efficiency are improved while the process continuity is guaranteed, and a closed-loop control normal form of traceability pre-judgment dynamic quantification grading interception is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment in water treatment technology, and in particular to a multi-level dynamic quantitative control system for wastewater treatment based on result feedback. Background Art

[0002] In recent years, with the acceleration of industrialization, the generation of high-salinity, highly polluted wastewater has increased significantly. This is particularly true in industries such as new energy materials, electronics and semiconductors, and petrochemicals. The discharge of this type of wastewater not only severely pollutes the environment but also wastes potentially recyclable resources. Existing technologies for treating this type of wastewater typically utilize a multi-stage filtration and adsorption mechanism followed by oxidation and degradation.

[0003] For example, in the prior art, Chinese Patent Publication No. CN105668862A discloses a method for recovering sodium hydroxide-containing wastewater, which uses a membrane technology treatment method after precipitation filtration to recover sodium hydroxide. The membrane technology is to use a microfiltration membrane to remove impurities and then use a reverse osmosis membrane to concentrate to obtain a recovered sodium hydroxide solution; the precipitation filtration is: the sodium hydroxide-containing wastewater is sent to a regulating tank, filtered after precipitation; the regulating tank is a sealed regulating tank, and the regulating tank is a multi-tank combination, with 3 to 5 tanks per group. The regulating tank contains an air purification device, and the sodium hydroxide-containing wastewater enters the regulating tank in batches, with each batch containing 100t to 200t of wastewater per tank. The precipitation filtration uses double-layer filter media for filtration, and the precipitation method is to let it stand for 1 to 2 days.

[0004] To ensure water treatment quality, process control during the recycling process is also crucial. For example, in the prior art, Chinese Patent Publication No. CN119512015A discloses an intelligent wastewater treatment and zero-emission control system, which combines a dynamic Bayesian network and a deep reinforcement learning algorithm to achieve intelligent control and resource utilization of the wastewater treatment process. The system dynamically optimizes operating parameters by monitoring wastewater characteristics in real time.

[0005] However, the existing technical solutions have the following problems: ‌Anomalies in the treatment process are all discovered at single points, and there is a lack of correlation analysis of the anomaly points, which leads to the inability to timely discover the influencing factors of single-point problems and the inability to take effective measures in time, delaying the wastewater treatment process. Summary of the Invention

[0006] The purpose of the present invention is to provide a multi-level dynamic quantitative control system for wastewater treatment based on result feedback, so as to solve the problem in the prior art that anomalies in the treatment process are all discovered at single points, and there is a lack of correlation analysis of the anomaly points, which leads to the inability to timely discover the influencing factors of single-point problems and the inability to take effective measures in time, thus delaying the wastewater treatment process.

[0007] To this end, the present invention provides a multi-level dynamic quantitative control system for wastewater treatment based on result feedback. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback is applied to the following wastewater treatment process: adjusting the pH of wastewater and oxidizing and degrading organic matter through Fenton reaction, adding NaOH and Na2CO3 to the effluent after oxidation to generate CaCO3 and Mg(OH)2 precipitates, the precipitates are intercepted by a tubular ultrafiltration membrane, and the waste liquid after interception is adsorbed by activated carbon and a resin exchange tower respectively. Based on the above wastewater treatment process, the multi-level dynamic quantitative control system for wastewater treatment based on result feedback includes a storage module, an identification module, a quantification module and an execution module; The storage module stores the following causal chain: The first causal chain includes the following identification items in order: abnormal influent pH, excessive residual oxidant, and attenuation of tubular ultrafiltration membrane flux; the second causal chain includes the following identification items in order: abnormal calcium ion saturation, excessive scaling of tubular ultrafiltration membrane, and decreased adsorption capacity of resin exchange tower; The identification module matches the current operating parameters with the identification items in the causal chain, and in response to the number of matches between the operating parameters and the identification items of any causal chain being no less than two, and the order of appearance of the identification items being consistent with the order of the corresponding causal chains, the energy quantification module operates; the quantification module is respectively provided with weight coefficients for the first causal chain and the second causal chain, and the quantification module respectively determines the matching degree of the current operating parameters with the first causal chain and the second causal chain, and generates a risk index of membrane contamination based on the matching degree and the weight coefficient; the execution module respectively executes backwashing of the tubular ultrafiltration membrane, adds scale inhibitor to the sedimentation link, or switches the tubular ultrafiltration membrane in response to the three levels of low, medium or high of the risk index.

[0008] As a preferred technical solution for the multi-level dynamic quantitative control system for wastewater treatment based on result feedback, the storage module is provided with a numerical range of corresponding parameters for each identification item of the first causal chain and the second causal chain, as well as a time interval range of adjacent identification items; Wherein, for the first causal chain, the specific numerical ranges of the corresponding parameters include: the pH value range of the influent, the redox potential range after the completion of the Fenton reaction, and the range of the rate of change of the transmembrane pressure difference of the tubular ultrafiltration membrane; For the second causal chain, the specific numerical ranges of the corresponding parameters include: a calcium ion saturation range, a transmembrane pressure difference range of a tubular ultrafiltration membrane, and an adsorption capacity range of a resin exchange tower.

[0009] As an optimal technical solution for a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the identification module determines that the operating parameter matches the identification item in response to the operating parameter being within the numerical range corresponding to the identification item. The identification module determines the timing interval of the identification item based on the trigger nodes matched by two adjacent identification items, and determines that the order of appearance of the identification items is consistent with the order of the corresponding causal chain in response to the timing interval being within the corresponding timing interval range.

[0010] As a preferred technical solution for the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the quantification module comprehensively determines the matching degree between the current operating parameters and the first causal chain and the second causal chain through parameter matching degree and time sequence matching degree, specifically including the following processes: For each matched identification item, a parameter matching degree is generated based on the degree of deviation between the current operating parameter value and the median of the corresponding value range; For each pair of sequentially matched adjacent identification items, the timing matching degree of the adjacent identification items is generated according to the degree of deviation between the actual trigger timing interval and the median of the corresponding timing interval range; The single parameter matching degrees of all identification items in the same causal chain are weighted and fused with the temporal matching degrees between adjacent identification items to generate the matching degrees between the operating parameters and the causal chain.

[0011] As an optimal technical solution for the wastewater treatment multi-stage dynamic quantitative control system based on result feedback, the weight coefficient of the second causal chain set by the quantification module is greater than the weight coefficient of the first causal chain.

[0012] As a preferred technical solution for the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the process of determining the matching degree of a single parameter by the quantification module specifically includes: Determine the difference between the current operating parameter and the median of its corresponding numerical range; Output the matching degree of the identified item according to the preset deviation matching degree mapping relationship; The mapping relationship satisfies that: when the difference is zero, the matching degree is at its maximum value, and when the difference approaches the range boundary, the matching degree approaches zero.

[0013] As an optimal technical solution for the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the process of determining the temporal matching degree of the adjacent identification items by the quantization module specifically includes: Determine the difference between the timing interval of the trigger nodes of two adjacent identification items matching each other and the median of the corresponding timing interval range; If the difference is within the allowable fluctuation range, the timing matching degree is determined to be the maximum value. If the difference exceeds the allowable fluctuation range but does not reach the boundary of the timing interval range, the timing matching degree decreases linearly. If the difference exceeds the boundary of the timing interval range, the timing matching degree is zero.

[0014] As an optimal technical solution for a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the quantification module determines the time interval range from the abnormal pH of the influent to the excessive oxidant residue based on the Fenton reaction rate for the first causal chain, and determines the time interval range of the two matching items from the abnormal calcium ion saturation to the excessive scaling of the tubular ultrafiltration membrane based on the calcium carbonate crystal nucleus growth rate.

[0015] As a preferred technical solution for a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the quantification module performs weighted fusion of the individual parameter matching degrees of all identified items in the same causal chain with the temporal matching degrees between adjacent identified items to generate the matching degree of the operating parameters and the causal chain. The module assigns a first initial weight to the first parameter matching degree and a second initial weight to the first temporal matching degree. Subsequent items are ranked in descending order based on the first initial weight and the second preset weight. The sum of the weights assigned to the matching degrees of each parameter and the sum of the weights assigned to the matching degrees of each time sequence are both one.

[0016] As an optimal technical solution for the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the identification module records the operating parameters that match the identification items.

[0017] The beneficial effects of the present invention are: The present invention's multi-level dynamic quantitative control system for wastewater treatment, based on result feedback, achieves comprehensive tracking and proactive intervention from the source of pollution to its derivative phenomena by constructing a causal chain model for pollution transmission. Its core lies in transforming the pollution migration patterns implicit in wastewater treatment processes into a sequence of quantifiable identification items. Through a fusion mechanism of sequential logic verification and multi-dimensional matching, it overcomes the limitations of traditional single-point control. While ensuring process continuity, the system significantly improves the accuracy and resource utilization efficiency of membrane fouling prevention and control, forming a closed-loop control paradigm that combines source tracing, dynamic quantification, and hierarchical interception.

[0018] Furthermore, the present invention uses a pre-set causal chain within the storage module to link previously discrete process parameters into an ordered contamination transmission chain. The identification module not only verifies that a single parameter has exceeded its limit, but also requires that the identified items appear in a pre-set order and adhere to a time sequence, ensuring that the alarm signal truly reflects the contamination migration process. This mechanism triggers control actions before contamination reaches critical downstream equipment, interrupting the contamination chain at its source.

[0019] Furthermore, the quantification module of this invention introduces dual evaluation indicators: parameter matching and time series matching, generating a comprehensive risk index through weighted fusion. Parameter matching reflects the proximity of current parameters to the pollution threshold, while time series matching verifies the rationality of causal relationships. This fusion of the two avoids the risk of misjudgment associated with traditional single-threshold control, making the risk index more objective in representing the combined threat intensity of multi-level pollution.

[0020] Furthermore, the present invention implements hierarchical response and cross-unit collaborative optimization. The execution module dynamically initiates differentiated prevention and control strategies based on the risk index (low, medium, and high). For low-risk levels, membrane backwash is initiated to address early-stage contamination and prevent overuse of reagents. For medium-risk levels, antiscalant is injected into the precipitation process to suppress the spread of scaling. For high-risk levels, a backup membrane module is switched and subsequent units are adjusted to prevent cross-process contamination transmission. This hierarchical mechanism precisely directs prevention and control resources to the weakest links, ensuring treatment efficiency while minimizing energy and material consumption.

[0021] Furthermore, the present invention assigns differentiated weights to different causal chains and enhances sensitivity to the root causes of pollution by maximizing the weight of the first identified item, ensuring that identification and control are tilted towards the core contradiction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 1 is a structural block diagram of a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback in an embodiment of the present invention; Figure 2 Flowchart of the wastewater treatment process in an embodiment of the present invention; Figure 3 This is a workflow diagram of a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0025] like Figure 2As shown, in order to better understand the present invention, this embodiment first describes the wastewater treatment process of the control system of the present invention. The wastewater treatment process specifically includes: S1, adjusting the pH of the inlet liquid to 2-4, adding H2O2 and Fe 2+ Fenton reaction is carried out to oxidize and degrade organic matter; S2, NaOH and Na2CO3 are added to the effluent after oxidation to generate CaCO3 and Mg(OH)2 precipitates, which are concentrated and retained by a tubular ultrafiltration membrane; S3, part of the organic matter is adsorbed by activated carbon; S4, the pH is adjusted to 6-8, and the residual Ca is adsorbed by the first resin exchange tower. 2+ Mg 2+ and heavy metals; adjust the pH to 5-6, and adsorb fluorine and silicon to the standard concentration through the second resin exchange tower; S5, intercept the Na2SO4 enriched liquid through salt separation and concentration, and the permeate is the NaCl enriched liquid; S6, obtain NaCl crystals and anhydrous Na2SO4 through evaporation and crystallization.

[0026] Based on the above wastewater treatment process, please refer to Figure 1 As shown, this embodiment also provides a wastewater treatment multi-stage dynamic quantitative control system based on result feedback, which includes a storage module, an identification module, a quantification module and an execution module; The storage module stores the following causal chain: The first causal chain includes the following identification items in order: abnormal influent pH, excessive residual oxidant, and attenuation of tubular ultrafiltration membrane flux; the second causal chain includes the following identification items in order: abnormal calcium ion saturation, excessive scaling of tubular ultrafiltration membrane, and decreased adsorption capacity of resin exchange tower; The identification module matches the current operating parameters with the identification items in the causal chain. In response to the operating parameters matching at least two identification items in any causal chain, and the order of appearance of the identification items being consistent with the order of the corresponding causal chains, the quantification module is activated. The identification module records the operating parameters that match the identification items and feeds them back to the user. The quantification module is configured with weight coefficients for the first and second causal chains (the weight coefficient for the second causal chain is greater than the weight coefficient for the first causal chain; in this embodiment, the weight coefficient for the first causal chain is 0.24, and the weight coefficient for the second causal chain is 0.76). The quantification module determines the degree of match between the current operating parameters and the first and second causal chains, and generates a membrane fouling risk index based on the degree of match and the weight coefficients. In response to the risk index being low, medium, or high, the execution module executes backwashing of the tubular ultrafiltration membrane, adding antiscalant to the sedimentation stage, or switching the tubular ultrafiltration membrane. In the above embodiment, by constructing a pollution transmission causal chain model, full tracking and proactive intervention from the source of pollution to its derivative phenomena are achieved. Its core lies in transforming the implicit pollution migration patterns in wastewater treatment processes into a sequence of quantifiable identification items. Through sequential logic verification and a multi-dimensional matching fusion mechanism, it overcomes the limitations of traditional single-point control. While ensuring process continuity, the system significantly improves the accuracy and resource utilization efficiency of membrane fouling prevention and control, forming a closed-loop control paradigm that combines source tracing, dynamic quantification, and hierarchical interception.

[0027] Based on the above embodiment, the storage module is provided with a numerical range of a corresponding parameter for each identification item of the first causal chain and the second causal chain, as well as a time interval range of adjacent identification items; Wherein, for the first causal chain, the specific numerical ranges of the corresponding parameters include: the pH value range of the influent, the redox potential range after the completion of the Fenton reaction, and the range of the rate of change of the transmembrane pressure difference of the tubular ultrafiltration membrane; For the second causal chain, the specific numerical ranges of the corresponding parameters include: calcium ion saturation range, transmembrane pressure difference range of tubular ultrafiltration membrane and adsorption capacity range of resin exchange tower. In detail, for the first causal chain, its cause is that in the Fenton reaction, the pH is too low, causing divalent Fe ions to form inert complexes, inhibiting the generation of hydroxyl radicals, and undecomposed or ozone penetrates into the tubular ultrafiltration membrane, attacking the surface bonding of the tubular ultrafiltration membrane, resulting in the exposure of hydrophobic groups. After the hydrophobic groups are exposed, the adsorption of organic matter is accelerated to form a dense filter cake layer. For the second causal chain, its cause is Ca 2+ Supersaturation induces homogeneous nucleation to generate amorphous CaCO3 crystals, which are deposited on the surface of the film. 2+ Penetrate into the resin tower to compete for chelation sites, reducing the resin adsorption capacity. For example, the setting of the numerical range in this embodiment is shown in the following table, which can be adjusted in combination with the actual reaction rate during implementation. Identification item value range matching table Identification Item Numeric Types Numerical range Abnormal inlet water pH pH 1.7-2.0 Oxidant residues exceed the standard Oxidation-reduction potential 430mV-480mV Tubular ultrafiltration membrane flux attenuation The rate of change of transmembrane pressure difference of tubular ultrafiltration membrane relative to the previous one +4% / min~+10% / min Abnormal calcium saturation Calcium saturation 0.78~0.92 Tubular ultrafiltration membrane scaling exceeds the standard Transmembrane pressure difference of tubular ultrafiltration membrane 65kPa~78kPa The adsorption capacity of the resin exchange tower decreases Heavy metal retention rate relative to the previous attenuation percentage 15% In this embodiment, the time interval range of adjacent identification items is as follows. Of course, in practice, it can also be calibrated by statistics of limited experiments of corresponding phenomena and corresponding process standards. Temporal interval range matching table of adjacent identification items Adjacent identification items Value range / min Abnormal pH of influent water ~ Oxidant residue exceeds the standard 2~10 Oxidant residue exceeds the standard ~ Tubular ultrafiltration membrane flux attenuation 5~15 Abnormal calcium ion saturation ~ tubular ultrafiltration membrane scaling exceeds the standard 10~35 Tubular ultrafiltration membrane fouling exceeds standards ~ resin exchange tower adsorption capacity decreases 15~45 In the above table, the time interval range from abnormal influent pH to excessive oxidant residue is determined based on the Fenton reaction rate, and the time interval range of the two matching items from abnormal calcium ion saturation to excessive scaling of the tubular ultrafiltration membrane is determined based on the calcium carbonate crystal nucleus growth rate; the remaining adjacent identification items are determined based on historical data of the wastewater treatment process.

[0028] Based on the above-mentioned parameter range and timing range configuration, the identification module determines that the operating parameter matches the identification item in response to the operating parameter being within the numerical range corresponding to the identification item. The identification module determines the timing interval of the identification item based on the trigger nodes that match two adjacent identification items, and determines that the order of appearance of the identification items is consistent with the order of the corresponding causal chain in response to the timing interval being within the corresponding timing interval range.

[0029] Specifically, the quantification module determines the matching degree between the current operating parameters and the first causal chain and the second causal chain by comprehensively determining the parameter matching degree and the time sequence matching degree, which specifically includes the following processes: 1) For each matched identification item, generate a parameter matching degree based on the degree of deviation between the current operating parameter value and the median of the corresponding numerical range, including: determining the difference between the current operating parameter and the median of the corresponding numerical range; Output the matching degree of the identified item according to the preset deviation-matching degree mapping relationship; The mapping relationship satisfies that: when the difference is zero, the matching degree reaches a maximum value of 1, and when the difference approaches the range boundary, the matching degree approaches zero.

[0030] 2) For each pair of sequentially matched adjacent identification items, the timing matching degree of the adjacent identification items is generated according to the degree of deviation between the actual trigger timing interval and the median of the corresponding timing interval range; Determine the difference between the timing interval of the trigger nodes of two adjacent identification items matching each other and the median of the corresponding timing interval range; If the difference is within the allowable fluctuation range (in this embodiment, 50% of the original timing interval range centered on the median of the above-mentioned timing interval range), the timing matching degree is determined to be a maximum value of 1. If the difference exceeds the allowable fluctuation range but does not reach the boundary of the timing interval range, the timing matching degree decreases linearly. If the difference exceeds the boundary of the timing interval range, the timing matching degree is zero.

[0031] 3) The single parameter matching degree of all identification items in the same causal chain is weighted and fused with the temporal matching degree between adjacent identification items to generate the matching degree between the operating parameters and the causal chain.

[0032] In the process of generating the matching degree between the operating parameters and the causal chain by weightedly fusing the single parameter matching degrees of all identified items in the same causal chain with the temporal matching degrees between adjacent identified items, the quantification module in 3) assigns a first initial weight (0.62 in this embodiment) to the first parameter matching degree, and a second initial weight (0.38 in this embodiment) to the first temporal matching degree. The parameter matching degree weights of subsequent items increase based on the first initial weight, and the temporal matching degree weights of subsequent items increase based on the second initial weight. The sum of the weights assigned to each parameter matching degree and the sum of the weights assigned to each temporal matching degree are both one.

[0033] After determining the degree of match, the quantification module weights the matching degrees of the two causal chains based on their weight coefficients (in this embodiment, the weight coefficient of the first causal chain is 0.7, and the weight coefficient of the second causal chain is 0.3) and adds them together to determine the risk index. It should be understood that the weight coefficients for the matching degrees of the first and second causal chains, as well as the first and second initial weights, can be adjusted based on the number of occurrences of the identification item in actual operating conditions. A higher number of occurrences of the identification item corresponds to a higher initial weight and weight coefficient. Users can perform continuous calibration and optimization based on actual scenarios to ensure that the execution module's execution judgment conditions are sufficiently aligned with the actual scenario, making the actions corresponding to the three degree ranges more targeted and compatible.

[0034] After the quantification module completes its work, the execution module, in response to the risk index's low (0-0.2), medium (0.2-0.5), or high (0.5-1) levels, initiates backflushing of the tubular ultrafiltration membrane, adds antiscalant to the sedimentation stage, or switches the tubular ultrafiltration membrane. Specifically, a low risk index indicates that the system has detected early signs of a contamination chain, but has not yet posed a substantial threat. The purpose of membrane backflushing at this stage is to physically reverse the flow and promptly remove loose contaminant deposits from the membrane surface. This intervention effectively interrupts the initial accumulation stage of the contamination chain, preventing the progression from reversible adsorption to irreversible fouling. Backflushing minimizes intervention, maintaining continuous system operation while preventing the continued accumulation of fine contaminants on the membrane surface. Its core advantage is that it interrupts the contamination process with minimal energy consumption and maintains stable membrane flux. A medium risk index indicates that the contamination chain has entered the advanced stage, with the system identifying clear risk signals such as fouling tendencies or accumulation of oxidation byproducts. During this stage, scale inhibitors are added to the precipitation process to chemically stabilize the supersaturated scale-forming ions in the solution. Scale inhibitors alter the crystal growth dynamics, disrupting the regular arrangement of microcrystals like calcium carbonate, resulting in a loose, easily detachable, amorphous structure. This intervention intervenes in the contamination transfer process at the material conversion level, alleviating scaling pressure on subsequent membrane units while preventing damage to the membranes caused by direct chemical cleaning. Essentially, this involves establishing a chemical barrier midway through the contamination chain through precise chemical intervention. A high risk index indicates that the contamination chain is fully activated, with the system detecting a multi-stage, severe contamination situation. Switching to a backup membrane module at this point is a critical measure to prevent systemic failure, its core value being to achieve process fault tolerance. By rapidly isolating the contaminated membrane element, the diffusion path to the subsequent resin adsorption and nanofiltration units is blocked. This response not only ensures continuous processing capacity but also, through physical isolation, provides a window for chemical cleaning of the contaminated membrane module.

[0035] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. A person skilled in the art would be able to make other variations or modifications based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, applied to the following wastewater treatment process: regulating the pH of wastewater and oxidizing and degrading organic matter through Fenton reaction, adding NaOH and Na2CO3 to the effluent after oxidation to generate CaCO3 and Mg(OH)2 precipitates, which are intercepted by a tubular ultrafiltration membrane, and the wastewater after interception is adsorbed by activated carbon and resin exchange towers respectively, characterized in that: The wastewater treatment multi-stage dynamic quantitative control system based on result feedback includes: Storage module, stores the following causal chains: The first causal chain includes the following identification items in order: abnormal influent pH, excessive oxidant residues, and tubular ultrafiltration membrane flux attenuation; The second causal chain includes the following identification items in order: abnormal calcium ion saturation, excessive scaling of tubular ultrafiltration membranes, and decreased adsorption capacity of resin exchange towers; an identification module that matches current operating parameters with identification items in the causal chain, and activates the energy quantization module in response to the operating parameters matching at least two identification items in any causal chain, and the order of appearance of the identification items is consistent with the order of the corresponding causal chain; a quantification module, each provided with a weight coefficient of the first causal chain and the second causal chain, the quantification module determining a matching degree between the current operating parameters and the first causal chain and the second causal chain, and generating a membrane fouling risk index based on the matching degree and the weight coefficient; The execution module executes backflushing of the tubular ultrafiltration membrane, adding antiscaling agent to the sedimentation link, or switching the tubular ultrafiltration membrane in response to the three levels of low, medium or high of the risk index.

2. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 1 is characterized in that: The storage module is provided with a numerical range of a corresponding parameter for each identification item of the first causal chain and the second causal chain, as well as a time interval range of adjacent identification items; Wherein, for the first causal chain, the specific numerical ranges of the corresponding parameters include: the pH value range of the influent, the redox potential range after the completion of the Fenton reaction, and the range of the rate of change of the transmembrane pressure difference of the tubular ultrafiltration membrane; For the second causal chain, the specific numerical ranges of the corresponding parameters include: a calcium ion saturation range, a transmembrane pressure difference range of a tubular ultrafiltration membrane, and an adsorption capacity range of a resin exchange tower.

3. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 2 is characterized in that: The identification module determines that the operating parameter matches the identification item in response to the operating parameter being within the numerical range corresponding to the identification item. The identification module determines the timing interval of the identification item based on the trigger nodes that match two adjacent identification items. In response to the timing interval being within the corresponding timing interval range, the identification module determines that the order of appearance of the identification items is consistent with the order of the corresponding causal chain.

4. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 3 is characterized in that: The quantification module determines the matching degree between the current operating parameters and the first causal chain and the second causal chain by comprehensively determining the parameter matching degree and the time sequence matching degree, specifically including the following processes: For each matched identification item, a parameter matching degree is generated based on the degree of deviation between the current operating parameter value and the median of the corresponding value range; For each pair of sequentially matched adjacent identification items, the timing matching degree of the adjacent identification items is generated according to the degree of deviation between the actual trigger timing interval and the median of the corresponding timing interval range; The single parameter matching degrees of all identification items in the same causal chain are weighted and fused with the temporal matching degrees between adjacent identification items to generate the matching degrees between the operating parameters and the causal chain.

5. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 1 is characterized in that: The weight coefficient of the second causal chain set by the quantization module is greater than the weight coefficient of the first causal chain.

6. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 4 is characterized in that: The process of determining the matching degree of a single parameter by the quantization module specifically includes: Determine the difference between the current operating parameter and the median of its corresponding numerical range; Output the matching degree of the identified item according to the preset deviation matching degree mapping relationship; The mapping relationship satisfies that: when the difference is zero, the matching degree is at its maximum value, and when the difference approaches the range boundary, the matching degree approaches zero.

7. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 4 is characterized in that: The process of determining the temporal matching degree of the adjacent identified items by the quantization module specifically includes: Determine the difference between the timing interval of the trigger nodes of two adjacent identification items matching each other and the median of the corresponding timing interval range; If the difference is within the allowable fluctuation range, the timing matching degree is determined to be the maximum value. If the difference exceeds the allowable fluctuation range but does not reach the boundary of the timing interval range, the timing matching degree decreases linearly. If the difference exceeds the boundary of the timing interval range, the timing matching degree is zero.

8. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 2 is characterized in that: The quantification module determines the time interval range of the two matching items from abnormal influent pH to excessive oxidant residue based on the Fenton reaction rate for the first causal chain, and determines the time interval range of the two matching items from abnormal calcium ion saturation to excessive scaling of the tubular ultrafiltration membrane based on the calcium carbonate crystal nucleus growth rate.

9. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 4 is characterized in that: The quantification module weights and fuses the individual parameter matching degrees of all identified items in the same causal chain with the temporal matching degrees between adjacent identified items to generate the matching degree of the operating parameters and the causal chain. The quantification module assigns a first initial weight to the first parameter matching degree and a second initial weight to the first temporal matching degree. Subsequent items are assigned a decreasing weight based on the first initial weight and the second preset weight based on the position order. The sum of the weights assigned to the matching degrees of each parameter and the sum of the weights assigned to the matching degrees of each time sequence are both one.

10. The multi-level dynamic quantitative control system for wastewater treatment based on result feedback according to claim 1, characterized in that: The identification module records the operating parameters that match the identification items.

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