A method for synergistically removing pollutants in printing and dyeing wastewater

Abstract

The application discloses a kind of printing and dyeing wastewater pollutant collaborative removal processing methods, it is related to wastewater treatment technical field, including, tracking printing and dyeing wastewater pollutant collaborative grouping in the concentration change linkage situation of pollutant, and according to pollution load and key pollutant concentration Division coupling intensity gradient, coupling intensity gradient configuration set is generated;According to coupling intensity gradient configuration set, pollution load range reading, key pollutant concentration interval matching and collaborative strategy type screening are carried out, and setting control intensity range, multiple-stage collaborative regulation rule chain is generated;Multi-stage collaborative regulation rule chain is combined with printing and dyeing wastewater multi-source monitoring record, real-time scheduling is carried out, collaborative removal processing strategy is obtained and is executed, and collaborative processing water quality state set is generated.The application realizes accurate identification linkage pollutant, improves water, wastewater, sewage or sludge processing pertinence, simultaneously realizes processing strategy and water quality state dynamic matching, improves water, wastewater, sewage or sludge processing efficiency and stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater treatment, in particular to a printing and dyeing wastewater pollutant collaborative removal treatment method. BACKGROUND

[0002] Under the dual driving of the continuous expansion of the printing and dyeing industry and the increasingly stringent environmental protection standards, printing and dyeing wastewater treatment technology has become a key research direction in the field of water, wastewater, sewage or sludge treatment. Printing and dyeing wastewater usually has high color, high chemical oxygen demand (COD), complex composition and strong volatility, etc. It contains not only a large amount of degradable and refractory organic matter, but also a variety of residual auxiliaries, dye intermediates and inorganic salts, showing non-steady state and multi-source isomerism characteristics. In order to cope with this challenge, the related technology system has been evolving in recent years, gradually developing from single physical-chemical or biological treatment process to multi-technology coupling and process intelligentization.

[0003] The existing printing and dyeing wastewater treatment technology still has deficiencies in pollutant collaborative identification and dynamic regulation. Firstly, most systems cannot effectively mine the time sequence correlation and working condition dependence of pollutant behavior characteristics in multi-source monitoring data, resulting in the inability to accurately construct the collaborative grouping mechanism between pollutants, and thus making it difficult to implement targeted combined treatment strategies. Secondly, the existing regulation logic usually uses static threshold or empirical rules, lacks quantitative evaluation of the dynamic coupling strength of pollutant load and key pollutant concentration, and thus the treatment process cannot realize multi-stage and adaptive collaborative scheduling according to the actual water quality changes. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a printing and dyeing wastewater pollutant collaborative removal treatment method to solve the problems of difficult pollutant collaborative identification and inaccurate dynamic regulation.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The present application provides a printing and dyeing wastewater pollutant collaborative removal treatment method, which comprises,

[0008] Collecting multi-source monitoring records of printing and dyeing wastewater, extracting behavior characteristics, solidifying as working condition behavior benchmark template, and generating pollutant behavior characteristics;

[0009] Based on the pollutant behavior characteristics, a multi-dimensional behavior similarity matrix is constructed, and progressive aggregation and soft association labeling are performed to generate printing and dyeing wastewater pollutant collaborative grouping;

[0010] Track the concentration change linkage of pollutants in the dyeing wastewater pollutant collaborative grouping, and divide the coupling strength gradient according to the pollution load and the concentration of key pollutants, and generate a coupling strength gradient configuration set;

[0011] According to the coupling strength gradient configuration set, pollution load range reading, key pollutant concentration interval matching and collaborative strategy type screening are carried out, and the regulation strength range is set, and a multi-stage collaborative regulation rule chain is generated;

[0012] The multi-stage collaborative regulation rule chain is combined with the dyeing wastewater multi-source monitoring record to carry out real-time scheduling, obtain a collaborative removal treatment strategy and execute, and generate a collaborative treatment water quality state set.

[0013] As a preferred scheme of the dyeing wastewater pollutant collaborative removal treatment method, wherein: the dyeing wastewater multi-source monitoring record includes chroma data, degradable organic matter data, refractory organic matter data, auxiliary agent residue data, water, wastewater, sewage or sludge treatment related data.

[0014] As a preferred scheme of the dyeing wastewater pollutant collaborative removal treatment method, wherein: the step of generating the pollutant behavior characteristics is as follows,

[0015] The concentration change amplitude, change direction, change rhythm characteristics and working condition response characteristics are extracted from the dyeing wastewater multi-source monitoring record to generate an initial pollutant behavior characteristic record set;

[0016] The initial pollutant behavior characteristic record set is grouped according to the working condition and production formula, and behavior trajectory comparison and behavior mode aggregation are performed in the grouping to generate a working condition behavior mode set;

[0017] The working condition behavior mode set is solidified as a working condition behavior reference template, and the remaining behavior characteristic records are integrated to generate pollutant behavior characteristics.

[0018] As a preferred scheme of the dyeing wastewater pollutant collaborative removal treatment method, wherein: the step of constructing a multi-dimensional behavior similarity matrix based on the pollutant behavior characteristics is as follows,

[0019] The pollutant behavior characteristic data set is screened and reorganized, and the missing behavior characteristic fields are filled to generate a behavior characteristic comparison candidate set;

[0020] The behavior characteristic comparison candidate set is subjected to multi-dimensional comparison, and the comparison difference is set as a multi-dimensional behavior similarity index of the pollutant category to generate a multi-dimensional behavior similarity matrix.

[0021] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the steps for generating synergistic groups of pollutants in dyeing and printing wastewater through progressive aggregation and soft association labeling are as follows:

[0022] Based on the multi-dimensional behavioral similarity matrix, the pollutant category with the highest multi-dimensional behavioral similarity index is selected, and progressive aggregation is performed to generate an initial aggregated group record set of pollutants.

[0023] A soft association weighting was performed on the initial clustering record set of pollutants, and a collaborative grouping of pollutants in dyeing and printing wastewater was constructed by combining isolated pollutant categories.

[0024] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the steps for tracking the concentration changes of pollutants in the synergistic group of dyeing and printing wastewater pollutants are as follows:

[0025] The behavior characteristics of pollutants belonging to the same dyeing and printing wastewater pollutant co-group are aggregated according to a unified time axis, and the pollution load and key pollutant concentrations are calculated to generate a concentration linkage tracking dataset.

[0026] Based on the concentration-linked tracking dataset, the pollutant behavior characteristics in the co-grouping of pollutants in dyeing and printing wastewater are compared and paired with the concentrations of key pollutants for storage, generating concentration change linkage features.

[0027] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the step of dividing the coupling strength gradient according to the pollution load and the concentration of key pollutants to generate a coupling strength gradient configuration set is as follows:

[0028] The pollution load and key pollutant concentration in the concentration change linkage characteristics are dynamically divided and paired to generate pollution load intervals and key pollutant concentration intervals.

[0029] Based on the pollution load range and the concentration range of key pollutants, the degree of change of the linkage characteristics of concentration changes is compared, the coupling strength gradient level is divided, and the coupling strength gradient configuration set is generated.

[0030] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the steps of reading the pollution load range, matching the concentration range of key pollutants, and screening the synergistic strategy type based on the coupling strength gradient configuration set are as follows:

[0031] The pollution load range is read from the coupling strength gradient configuration set, and the concentration range of key pollutants is matched to generate the initial coordinated control range.

[0032] Based on the initial synergistic control range, synergistic strategy types that meet the pollution load range and key pollutant concentration range are selected from the empirical rule base for synergistic treatment of dyeing and printing wastewater.

[0033] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the steps of setting the control intensity range and generating a multi-stage synergistic control rule chain are as follows:

[0034] Based on the coupling strength gradient level, set the control strength range for the collaborative strategy type, and attach trigger and termination conditions to generate a phased collaborative control configuration record.

[0035] The configuration records of phased coordinated regulation are linked together in sequence according to the pollution load range and the concentration range of key pollutants, and phase transition conditions are added to generate a multi-stage coordinated regulation rule chain.

[0036] As a preferred embodiment of the synergistic removal and treatment method for pollutants in dyeing and printing wastewater according to the present invention, the steps of combining a multi-stage synergistic regulation rule chain with multi-source monitoring records of dyeing and printing wastewater for real-time scheduling, obtaining and executing synergistic removal and treatment strategies, and generating a synergistic treatment water quality status set are as follows.

[0037] The real-time collection of multi-source monitoring records of dyeing and printing wastewater is used to recalculate the pollution load and concentration of key pollutants, and then mapped to a multi-stage collaborative control rule chain to generate a real-time status description.

[0038] The real-time status description is compared with the trigger conditions and control intensity range in the stage collaborative control configuration record to generate a collaborative removal real-time scheduling record.

[0039] Based on the real-time scheduling records of collaborative removal, the target collaborative strategy type is selected from the multi-stage collaborative control rule chain, and the applicability constraint is determined to generate a collaborative removal processing strategy.

[0040] The target collaborative strategy type in the collaborative removal treatment strategy is expanded into a collaborative removal scheduling action sequence and executed. Water quality status elements are aggregated and organized to generate a collaborative treatment water quality status set.

[0041] The beneficial effects of this invention are as follows: by constructing a multi-dimensional behavioral similarity matrix and progressively aggregating it, pollutants are collaboratively grouped to accurately identify linked pollutants and improve the targeted treatment of water, wastewater, sewage or sludge; by dividing the coupling strength gradient to generate a multi-stage regulation rule chain, the treatment strategy and water quality status are dynamically matched to improve the treatment efficiency and stability of water, wastewater, sewage or sludge. Attached Figure Description

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Fig. 1 This is a flowchart of a method for the synergistic removal and treatment of pollutants in dyeing and printing wastewater.

[0044] Fig. 2 A flowchart for constructing clusters and collaborative groupings based on pollutant behavior characteristics.

[0045] Fig. 3 This is a flowchart for pollutant concentration linkage tracking and coupling strength gradient division.

[0046] Fig. 4 A flowchart for generating a multi-stage collaborative regulation rule chain. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0050] Reference Figs. 1-4 This is one embodiment of the present invention, which provides a method for the synergistic removal and treatment of pollutants in dyeing and printing wastewater, comprising the following steps:

[0051] S1: Collect multi-source monitoring records of dyeing and printing wastewater, extract behavioral characteristics, solidify them into a benchmark template for operating conditions, and generate pollutant behavioral characteristics;

[0052] S1.1: Multi-source monitoring records for dyeing and printing wastewater include color data, degradable organic matter data, recalcitrant organic matter data, auxiliary agent residue data, and treatment-related data for water, wastewater, sewage, or sludge.

[0053] Furthermore, colorimetric data is generated by periodically detecting changes in the optical properties of wastewater and recording them according to a unified time stamp. Degradable organic matter data is generated by collecting changes in the concentration of organic pollutants in wastewater that are prone to biological or chemical transformation and storing them in a time series manner. Recalcitrant organic matter data is generated by long-term monitoring of changes in the concentration of organic pollutants in wastewater that are structurally stable and not easily transformed, and recording them in conjunction with sampling time stamps. Auxiliary agent residue data is generated by collecting information on the residue of auxiliaries used in the dyeing and printing process in wastewater and organizing it according to production batches and time stamps. Data related to the treatment of water, wastewater, sewage, or sludge is generated by synchronously collecting information on the operating status of wastewater treatment, treatment stage identification, and water quality changes during the treatment process and binding it with the corresponding time stamps.

[0054] S1.2: Extract the concentration change amplitude, change direction, change rhythm characteristics and operating condition response characteristics from the multi-source monitoring records of dyeing and printing wastewater to generate an initial pollutant behavior characteristic record set;

[0055] Furthermore, the multi-source monitoring records of the pretreated dyeing and printing wastewater are continuously read according to a unified time scale, and comparisons are made between adjacent time scales to obtain the amplitude of pollutant concentration changes. At the same time, the direction of concentration change is determined based on the increase or decrease relationship between adjacent time scales. On this basis, the combination relationship between the amplitude and direction of concentration change within multiple consecutive time scales is statistically analyzed to extract the rhythmic characteristics reflecting the speed and periodicity of concentration change. Furthermore, the amplitude, direction, and rhythmic characteristics of concentration change are associated with the operating condition markers and production formula markers under the corresponding time scales. The changes in pollutant behavior characteristics under different operating conditions are compared to extract the response characteristics of pollutants to changes in operating conditions. The rhythmic characteristics of change can be described based on repetitive change patterns to generate an initial pollutant behavior characteristic record set.

[0056] S1.3: Group the initial pollutant behavior characteristic record set according to the working conditions and production formula, and perform behavior trajectory comparison and behavior pattern aggregation within the group to generate a set of working condition behavior patterns;

[0057] Furthermore, the initial pollutant behavior characteristic record set is grouped according to the operating condition label and the production formula label, so that the initial pollutant behavior characteristic records with the same operating condition label and production formula label form corresponding groups, and pollutant behavior trajectories are generated in chronological order within each group; trajectory comparison is performed on the pollutant behavior trajectories formed at different time periods within the same group, and similar trajectory segments that repeatedly appear under the same operating conditions and production formula are identified by comparing the consistency of concentration change amplitude characteristics, change direction characteristics, change rhythm characteristics and operating condition response characteristics on the time axis; trajectory segments with similar change patterns and change rhythms are aggregated and organized to form behavior patterns that can characterize the typical change patterns of pollutants under the corresponding operating conditions and production formula conditions, and a set of operating condition behavior patterns is generated.

[0058] S1.4: Solidify the set of operating condition behavior patterns into an operating condition behavior benchmark template, and integrate the remaining behavior feature records to generate pollutant behavior features.

[0059] Furthermore, each type of behavior pattern in the set of operating condition behavior patterns is organized according to its corresponding operating condition label and production formula label. Behavior patterns that recur and exhibit stable changes over multiple operating cycles are solidified. The typical concentration change amplitude, change direction, change rhythm, and operating condition response characteristics of the behavior patterns are summarized and stored as corresponding operating condition behavior benchmark templates. Applicable operating condition labels, production formula labels, and stability identifiers are added to each operating condition behavior benchmark template. At the same time, the initial pollutant behavior characteristic records that are not included in the operating condition behavior benchmark templates are retained and organized. The remaining behavior characteristic records are integrated with the operating condition behavior benchmark templates according to a unified data structure, so that the operating condition behavior benchmark templates serve as stable reference features, and the remaining behavior characteristic records coexist as dynamic supplementary features. The operating condition behavior benchmark templates and the remaining behavior characteristic records are merged to form pollutant behavior characteristics.

[0060] It should be noted that the pollutant behavior characteristics are a set of features consisting of stable behavior patterns fixed in the operating condition behavior benchmark template and dynamic behavior patterns retained in the remaining behavior characteristic records. They are used to characterize the comprehensive behavior of pollutants in dyeing and printing wastewater under a unified time axis, including the magnitude, direction, rhythm of concentration changes, and operating condition response.

[0061] S2: Based on the pollutant behavior characteristics, a multi-dimensional behavior similarity matrix is ​​constructed, and progressive aggregation and soft association labeling are performed to generate collaborative grouping of pollutants in dyeing and printing wastewater;

[0062] S2.1: Filter and reorganize the pollutant behavior feature dataset, fill in the missing behavior feature fields, and generate a candidate set for behavior feature comparison;

[0063] Furthermore, the pollutant behavior feature dataset is filtered according to pollutant category label, time label, operating condition label, and production formula label. Behavioral feature records with incomplete labels or discontinuous time windows are removed, and adjacent behavioral feature records of the same pollutant category are reorganized in chronological order. For records with missing behavioral feature fields after filtering and reorganization, the fields are filled in according to the trend of behavioral feature changes of the same pollutant category within adjacent time scales (for example, the filling is completed when the trend of changes in adjacent time scales is consistent). The pollutant behavior feature records that have been filtered, reorganized, and filled in are grouped and organized by pollutant category to form a candidate set for behavioral feature comparison.

[0064] S2.2: Perform multi-dimensional comparison on the candidate set of behavioral features, and set the comparison difference as the multi-dimensional behavioral similarity index of pollutant category to generate a multi-dimensional behavioral similarity matrix;

[0065] Furthermore, the pollutant behavior features in the candidate set of behavior feature comparisons are paired according to pollutant categories, and multi-dimensional comparisons are performed on the pollutant dynamic behavior feature groups within a unified time window. The comparison content includes the degree of similarity in change trends, the degree of consistency in change directions, the degree of synchronization in change rhythms, and the degree of similarity in operating condition response features. The comparison results of each dimension are mapped to comparable values. The comparison results of the same pollutant category combination under different dimensions are summarized to form a multi-dimensional behavior similarity index characterizing the degree of similarity in pollutant behavior, and the index values ​​are normalized (e.g., limited to between 0 and 1). The multi-dimensional behavior similarity indices corresponding to all pollutant category combinations are arranged according to the row and column relationship of pollutant categories to generate a multi-dimensional behavior similarity matrix.

[0066] S2.3: Based on the multi-dimensional behavior similarity matrix, select the pollutant category with the highest multi-dimensional behavior similarity index, perform progressive aggregation, and generate an initial aggregated group record set of pollutants;

[0067] Furthermore, based on the multi-dimensional behavioral similarity matrix, the multi-dimensional behavioral similarity indices corresponding to each pollutant category combination are sorted. The pollutant category combination with the highest multi-dimensional behavioral similarity index is selected as the starting point for progressive aggregation, and the corresponding pollutant categories are merged to form the initial pollutant aggregation core. After forming the initial pollutant aggregation core, other pollutant categories with high multi-dimensional behavioral similarity indices to the pollutant categories in the initial pollutant aggregation core are searched in the multi-dimensional behavioral similarity matrix. When the multi-dimensional behavioral similarity index exceeds the aggregation threshold, the corresponding pollutant category is merged into the initial pollutant aggregation core. After completing one aggregation expansion, the pollutant categories that have been included in the initial pollutant aggregation core are removed from the set to be processed, and the above progressive aggregation process is repeated in the remaining pollutant categories. Finally, the initial pollutant aggregation cores formed by each aggregation are uniformly organized to generate the initial pollutant aggregation grouping record set.

[0068] It should be noted that the aggregation threshold (example range: 0.6 to 0.8) is determined based on the minimum acceptable behavioral similarity distribution in historical operation to avoid false aggregation, and the upper limit is determined based on the upper limit of similarity when the behavior of pollutants is highly consistent under the same operating conditions and production formula conditions to prevent excessive convergence.

[0069] S2.4: Perform soft association weighting on the initial clustering record set of pollutants, and construct a collaborative grouping of pollutants in dyeing and printing wastewater by combining isolated pollutant categories.

[0070] Furthermore, for pollutant categories in the initial pollutant clustering record set, the multi-dimensional behavioral similarity index corresponding to each initial pollutant clustering group is read based on the multi-dimensional behavioral similarity matrix, and the multi-dimensional behavioral similarity index is mapped to soft association weights, so that pollutant categories form different degrees of membership in different initial pollutant clustering groups; when the multi-dimensional behavioral similarity index of a pollutant category with multiple initial pollutant clustering groups is close to the clustering threshold, multiple soft association weights are assigned to the pollutant category; for isolated pollutant categories that are not included in the initial pollutant clustering groups, they are merged into the initial pollutant clustering group with the highest similarity according to the size of the multi-dimensional behavioral similarity index in a soft association manner; the soft association weight allocation results are uniformly sorted with the initial pollutant clustering group records to generate collaborative grouping of dyeing and printing wastewater pollutants.

[0071] S3: Track the concentration changes of pollutants in the co-grouping of dyeing and printing wastewater pollutants, and divide the coupling strength gradient according to the pollution load and the concentration of key pollutants to generate a coupling strength gradient configuration set.

[0072] S3.1: Aggregate the pollutant behavior characteristics of pollutants belonging to the same dyeing and printing wastewater co-grouping according to a unified time axis, calculate the pollution load and key pollutant concentrations, and generate a concentration linkage tracking dataset;

[0073] Furthermore, the behavioral characteristics of pollutants belonging to the same co-group of dyeing and printing wastewater pollutants are aligned to a unified time axis according to time markers, and the behavioral characteristics of different pollutant categories within the same time scale are aggregated and organized to form a synchronous record of pollutants in the time dimension. At each time scale, the monitoring concentration values ​​of pollutants within the co-group are read, and the baseline concentration values ​​of the corresponding pollutant categories are retrieved from the operating condition behavior benchmark template. The concentration deviation is formed based on the monitoring concentration values ​​and the baseline concentration values. The missing rate, anomaly removal markers, and time alignment consistency are summarized synchronously to generate a data credibility value. The data credibility value and the pollutant category weight are used together to calculate the pollution load by weighted summarization of the concentration deviation. According to the key pollutant screening rules, the monitoring concentration values ​​of key pollutants within the co-group at the corresponding time scale are read, and the validity is verified by combining the data credibility value to obtain the concentration of key pollutants. The time scale markers, co-group identifiers of dyeing and printing wastewater pollutants, pollution load values, key pollutant concentration values, baseline concentration value reference markers, and data credibility values ​​are combined and organized to form a concentration linkage tracking dataset in chronological order.

[0074] It should be noted that the key pollutant screening rules are used to identify the pollutant categories that have a dominant influence on the pollution status at the current time scale within the co-group of pollutants in dyeing and printing wastewater. The rules are set by comprehensively comparing the pollution load contribution ratio, concentration change stability, and concentration linkage frequency of each pollutant in the co-group during the historical operating cycle.

[0075] The formula for calculating pollution load is:

[0076] ;

[0077] in, Indicates time scale pollution load, Indicates pollutant category exist The monitored concentration values, Indicates pollutant category exist baseline concentration, Indicates the weight of pollutant categories, Indicates time scale Lower pollutant categories Data credibility This represents a robust suppression function, a nonlinear mapping function that limits and smooths the concentration deviation between the monitored concentration value and the baseline concentration value. This indicates the total number of pollutant categories involved in the pollution load calculation.

[0078] The formula for calculating the concentration of key pollutants is:

[0079] ;

[0080] in, Indicates time scale The overall concentration of key pollutants below Indicates the set of key pollutants. Indicates the category of key pollutants On the time scale The corresponding monitoring concentration values ​​are below. Indicates the category of key pollutants On the time scale The key scores below, This indicates the category of key pollutants. On the time scale The key scores below, This represents the weighting adjustment coefficient.

[0081] It should be noted that the pollutant category weights (example range: 0-1) are determined by normalizing the relative contribution ratio of each pollutant to the overall pollution load during the historical operating cycle.

[0082] The weighting adjustment factor (example range: 0.5 to 5) is set according to the dispersion of the critical score distribution.

[0083] S3.2: Based on the concentration linkage tracking dataset, compare the pollutant behavior characteristics in the co-grouping of pollutants in dyeing and printing wastewater, and store them in pairs with the concentrations of key pollutants to generate concentration change linkage features.

[0084] Furthermore, based on the concentration linkage tracking dataset, the pollutant behavior characteristics of each pollutant within the co-group of dyeing and printing wastewater pollutants are read according to a unified time scale. At the same time scale, the characteristics of concentration change amplitude, change direction, change rhythm, and operating condition response of different pollutants are compared horizontally to identify synchronous or reverse change relationships. Linkage description results reflecting the consistency and linkage degree of pollutant changes are extracted. The concentrations of key pollutants at the corresponding time scale are paired and stored with the linkage description results, and time scale markers and dyeing and printing wastewater pollutant co-group identifiers are added. The pairing results within continuous time scales are organized in chronological order to generate concentration change linkage features.

[0085] S3.3: Dynamically divide and pair the pollution load and key pollutant concentration in the concentration change linkage characteristics to generate pollution load range and key pollutant concentration range;

[0086] Furthermore, the pollution load recorded on a time scale in the concentration change linkage feature is jointly read with the concentration of key pollutants, and the distribution of pollution load values ​​is statistically analyzed within a continuous time scale. Multiple pollution load intervals are divided based on the range of pollution load values ​​over time, so that each pollution load interval corresponds to a relatively stable pollution level range (e.g., low, medium, and high pollution level intervals). On this basis, for each pollution load interval, the concentration of key pollutants falling within that interval is extracted, and the corresponding key pollutant concentration intervals are divided according to the actual distribution range of the key pollutant concentrations. Further, the pollution load intervals and key pollutant concentration intervals under the same time scale are paired and labeled, so that each concentration change linkage feature is associated with a clearly defined interval combination, ultimately forming pollution load intervals and key pollutant concentration intervals for subsequent coupling strength gradient division.

[0087] It should be noted that pollution load ranges are divided according to pollution load values. For example, 50–80 is classified as a low pollution load range, 80–140 as a medium pollution load range, and 140–200 as a high pollution load range. Key pollutant concentration ranges are also divided according to the concentration of key pollutants. For example, in the low pollution load range, the concentration of key pollutants is mainly concentrated in the range of 10–25, in the medium pollution load range, the concentration of key pollutants is mainly concentrated in the range of 25–60, and in the high pollution load range, the concentration of key pollutants is mainly concentrated in the range of 60–120.

[0088] S3.4: Based on the pollution load range and the concentration range of key pollutants, compare the degree of change of the linkage characteristics of concentration changes, divide the coupling strength gradient level, and generate the coupling strength gradient configuration set.

[0089] Furthermore, based on the pollution load range and the concentration range of key pollutants, the linkage characteristics of concentration changes at the corresponding time scales are read and organized, and the degree of linkage changes between pollutants is compared within the same range combination. By statistically analyzing the frequency of synchronous changes, the continuity of consistent change direction, and the response relationship between concentration change magnitude, the tightness of the linkage relationship between pollutants is determined. The coupling strength gradient levels corresponding to each pollution load range and the concentration range of key pollutants are organized and marked with range identifiers, and a coupling strength gradient configuration set for subsequent coordinated regulation is formed.

[0090] It should be noted that, in determining the coupling strength gradient level under the same pollution load range and key pollutant concentration range, the synchronous changes of pollutants in the concentration change linkage characteristics are quantitatively judged. For example, within 10 consecutive time scales, the number of time scales in which pollutants show the same direction of change, the longest continuous period of maintaining the same direction of change, and the average response ratio between concentration change amplitudes are counted. When the number of synchronous changes is not less than 7 and the number of time scales in which the same direction of change is maintained is not less than 5, and the response ratio of concentration change amplitude falls within a preset range (e.g., 0.8 to 1.2), the corresponding range combination is judged as a strong coupling strength gradient. When the number of synchronous changes is between 4 and 6 and the number of time scales in which the same direction of change is maintained is between 2 and 4, and the response ratio of concentration change amplitude falls within the middle range (e.g., 0.5 to 0.8), the corresponding range combination is judged as a medium coupling strength gradient. When the number of synchronous changes is not more than 3 or the number of time scales in which the same direction of change is maintained is less than 2, and the response ratio of concentration change amplitude is lower than a preset lower limit (e.g., lower than 0.5), the corresponding range combination is judged as a weak coupling strength gradient.

[0091] S4: Based on the coupling strength gradient configuration set, read the pollution load range, match the concentration range of key pollutants and screen the collaborative strategy type, set the control intensity range, and generate a multi-stage collaborative control rule chain.

[0092] S4.1: Read the pollution load range from the coupling strength gradient configuration set, and perform key pollutant concentration range matching to generate the initial collaborative control range;

[0093] Furthermore, the corresponding configuration records are read one by one from the coupling strength gradient configuration set according to the co-grouping identifier of the dyeing and printing wastewater pollutants. The pollution load interval information associated with the co-group is extracted from each configuration record. The upper and lower boundaries of the pollution load intervals are organized into a clear pollution load range to limit the overall pollution level corresponding to the current control stage. After completing the reading of the pollution load range, the key pollutant concentration interval information corresponding to the pollution load interval is retrieved from the same configuration record. The key pollutant concentration interval is matched with the key pollutant concentration value obtained by real-time monitoring. When the key pollutant concentration value falls within the corresponding interval range, it is considered that the match is successful (for example, the key pollutant concentration is in the range of 25 to 60). The co-grouping identifier of the dyeing and printing wastewater pollutants, the pollution load range, and the matched key pollutant concentration interval are combined and packaged so that each combination result clearly defines the pollution load constraint and the key pollutant concentration constraint, generating the initial co-control range.

[0094] It should be noted that an example of a pollution load constraint is that the pollution load value at the current time scale must fall within the interval corresponding to the pollution load range (for example, the pollution load constraint is satisfied only if the pollution load value falls within the interval of 80 to 140).

[0095] Example of a constraint on the concentration of critical pollutants: The critical pollutant concentration at the current time scale must fall within the critical pollutant concentration range that completes the matching (for example, the critical pollutant concentration must fall within the range of 25 to 60 to meet the critical pollutant concentration constraint).

[0096] S4.2: Based on the initial synergistic control range, select synergistic strategy types that meet the pollution load range and key pollutant concentration range from the empirical rule base for synergistic treatment of dyeing and printing wastewater;

[0097] Furthermore, based on the initial collaborative control range, rule entries corresponding to the collaborative grouping identifiers of pollutants in dyeing and printing wastewater are retrieved from the experience rule base for collaborative treatment of dyeing and printing wastewater. The applicable pollution load range and applicable key pollutant concentration range marked by the rule are searched one by one within each rule entry. The applicable pollution load range recorded in the rule entry is compared with the pollution load range in the initial collaborative control range to determine overlap. Simultaneously, the applicable key pollutant concentration range recorded in the rule entry is matched with the key pollutant concentration range in the initial collaborative control range. When the applicable pollution load range of a rule entry effectively overlaps with the initial collaborative control range and the applicable key pollutant concentration range covers the current key pollutant concentration range, the rule entry is deemed to meet the screening criteria (e.g., the applicable pollution load range of the rule is 80–150 and the current pollution load range is 90–140). After completing the matching of each rule entry, the collaborative strategy types corresponding to all rule entries that meet both the pollution load range and key pollutant concentration range conditions are collected and organized to generate a collaborative strategy type set.

[0098] It should be noted that the set of synergistic strategy types includes synergistic removal and control action combinations matched with the pollution load range, key pollutant concentration range and coupling intensity gradient level of the synergistic grouping of dyeing and printing wastewater pollutants (e.g., synergistic removal and control action combinations dominated by color, synergistic removal and control action combinations dominated by degradable organic matter, synergistic removal and control action combinations dominated by recalcitrant organic matter, and synergistic removal and control action combinations dominated by auxiliary agent residues).

[0099] The experience rule base for co-treatment of dyeing and printing wastewater is a structured set of rules used to support decision-making on the co-removal and control of pollutants in dyeing and printing wastewater. It is formed by summarizing and organizing multi-source monitoring records of dyeing and printing wastewater under different operating conditions and production formulas during historical operation, as well as changes in pollution load, changes in the concentration of key pollutants, and corresponding co-control results. In the setting process, the pollution load range and key pollutant concentration range that have repeatedly appeared and shown stability in historical operation are associated and solidified with the co-control response relationship. For each type of association, the applicable co-control strategy type, applicable pollution load range, and applicable key pollutant concentration range are marked. This enables the experience rule base for co-treatment of dyeing and printing wastewater to provide reusable co-control basis under different pollution loads and key pollutant concentrations.

[0100] S4.3: Based on the coupling strength gradient level, set the control strength range for the cooperative strategy type, and attach triggering and termination conditions to generate a phased cooperative control configuration record;

[0101] Furthermore, based on the coupling strength gradient level, the corresponding coupling strength gradient identifier is read for each of the selected collaborative strategy types. According to the correspondence between different coupling strength gradient levels and historical collaborative control response relationships, a matching control intensity range is set for each collaborative strategy type. This ensures that the control intensity range corresponding to a collaborative strategy type with a higher coupling strength gradient level is more concentrated in the high-intensity range, and the control intensity range corresponding to a collaborative strategy type with a lower coupling strength gradient level is more concentrated in the low-intensity range (e.g., setting the control intensity range to 0.7–1.0 under a strong coupling strength gradient, and setting the control intensity range to 0.4–0.7 under a medium coupling strength gradient). After setting the control intensity range, triggering and termination conditions are added to each collaborative strategy type. The triggering condition is used to activate the collaborative strategy type when the pollution load value and key pollutant concentration value enter the corresponding pollution load range and key pollutant concentration range. The termination condition is used to terminate the collaborative strategy type when the pollution load value or key pollutant concentration value leaves the corresponding range or the duration reaches the required value. The collaborative strategy type, coupling strength gradient level, control intensity range, triggering condition, and termination condition are combined and organized to generate a staged collaborative control configuration record.

[0102] S4.4: Connect the stage-based coordinated regulation configuration records in sequence according to the pollution load range and the concentration range of key pollutants, and add stage transition conditions to generate a multi-stage coordinated regulation rule chain.

[0103] Furthermore, the stage-based coordinated control configuration records are collected according to the coordinated grouping identifier of dyeing and printing wastewater pollutants. Within each coordinated group, they are sorted in order of pollution load range from low to high and key pollutant concentration range from low to high, so that the control configurations of each stage form a continuous connection in the interval. Stage transition conditions are set between adjacent stage-based coordinated control configuration records to limit the switching rules when pollution load values ​​and key pollutant concentration values ​​enter the next stage interval from the current interval. For example, when the pollution load values ​​and key pollutant concentration values ​​stably fall into the corresponding interval of the next stage within a continuous period of no less than a preset number of time scales, stage switching is triggered. At the same time, interval boundary preservation conditions are set to avoid frequent switching. The sorted stage-based coordinated control configuration records and the corresponding stage transition conditions are linked together to generate a multi-stage coordinated control rule chain.

[0104] It should be noted that the preset quantity (example range: 2 to 10) is set based on the continuous fluctuation characteristics of pollution load and key pollutant concentration during historical operation.

[0105] S5:

[0106] By combining the multi-stage collaborative regulation rule chain with the multi-source monitoring and recording of dyeing and printing wastewater, real-time scheduling is carried out to obtain and execute collaborative removal and treatment strategies, and generate a collaborative treatment water quality status set.

[0107] S5.1: Real-time collection of multi-source monitoring records of dyeing and printing wastewater, recalculation of pollution load and concentration of key pollutants, mapping to a multi-stage collaborative control rule chain, and generation of real-time status description;

[0108] Furthermore, the real-time collected multi-source monitoring records of dyeing and printing wastewater are aligned and organized according to a unified time scale. At each time scale, color data, degradable organic matter data, recalcitrant organic matter data, auxiliary agent residue data, and treatment-related data of water, wastewater, sewage, or sludge are simultaneously read. The corresponding pollution load values ​​are summarized according to the pollution load calculation rules. At the same time, the concentration values ​​of key pollutants in the dyeing and printing wastewater pollutant synergistic group at that time scale are read according to the key pollutant screening rules as the key pollutant concentrations. The pollution load values ​​and key pollutant concentration values ​​are matched with the pollution load range and key pollutant concentration range recorded in the multi-stage synergistic control rule chain to determine the control stage corresponding to the current time scale. The time scale markers, dyeing and printing wastewater pollutant synergistic group identifiers, pollution load values, key pollutant concentration values, and stage control identifiers are combined and organized to generate a real-time status description.

[0109] S5.2: Compare the real-time status description with the trigger conditions and control intensity range in the stage collaborative control configuration record to generate a collaborative removal real-time scheduling record;

[0110] Furthermore, the time scale markers, pollutant co-grouping identifiers for dyeing and printing wastewater, pollution load values, key pollutant concentration values, and stage control identifiers in the real-time status description are read. Matching trigger conditions and control intensity ranges are retrieved from the corresponding stage-based co-control configuration records. By comparing the pollution load values ​​and key pollutant concentration values ​​in the real-time status description with the trigger conditions, it is determined whether the activation requirements for the current stage-based co-control are met (e.g., the pollution load value remains in the range of 80–140 for three consecutive time scales, and the key pollutant concentration remains simultaneously in the range of 25–60). When the trigger conditions are met, the control intensity range corresponding to the current coupling intensity gradient level is read and associated with the stage control identifier. The time scale markers, pollutant co-grouping identifiers for dyeing and printing wastewater, pollution load values, key pollutant concentration values, control intensity ranges, and stage-based co-control configuration record identifiers are combined and organized to generate a real-time scheduling record for co-removal.

[0111] S5.3: Based on the real-time scheduling records of collaborative removal, select the target collaborative strategy type from the multi-stage collaborative control rule chain, determine the applicability constraints, and generate a collaborative removal processing strategy;

[0112] Furthermore, based on the real-time scheduling record reading time scale markers for collaborative removal, the collaborative grouping identifiers of dyeing and printing wastewater pollutants, the control stage identifiers, and the control intensity range, the corresponding stage collaborative control configuration record set is located in the multi-stage collaborative control rule chain according to the control stage identifier. A candidate set of collaborative strategy types is extracted from the stage collaborative control configuration record set, and collaborative strategy types are screened out based on the control intensity range. Applicability constraints are then applied to each of the remaining collaborative strategy types. The applicability constraint determination includes matching the pollution load values ​​and key pollutant concentration values ​​in the real-time scheduling record for collaborative removal with the pollution load range and key pollutant concentration interval, respectively. The system determines the consistency between the coupling strength gradient level in the real-time scheduling record of collaborative removal and the coupling strength gradient level in the stage collaborative control configuration record. When both the pollution load value and the key pollutant concentration value fall within the corresponding range and the coupling strength gradient level is consistent, the corresponding collaborative strategy type is retained (e.g., the pollution load value falls within 80-140 and the key pollutant concentration value falls within 25-60). The retained collaborative strategy types are sorted according to their matching degree, and the collaborative strategy type with the highest ranking is selected as the target collaborative strategy type. The target collaborative strategy type is combined with the control stage identifier and control intensity range to generate a collaborative removal treatment strategy.

[0113] S5.4: Expand the target collaborative strategy type in the collaborative removal treatment strategy into a collaborative removal scheduling action sequence and execute it, aggregate and organize water quality status elements, and generate a collaborative treatment water quality status set.

[0114] Furthermore, the target collaborative strategy type, control intensity parameter, and control stage identifier in the collaborative removal treatment strategy are read. Based on the target collaborative strategy type in the multi-stage collaborative control rule chain, the target collaborative strategy type is expanded into a collaborative removal scheduling action sequence arranged according to a unified time scale, so that each action in the collaborative removal scheduling action sequence is bound to the corresponding control intensity parameter. During the execution of the collaborative removal scheduling action sequence, the multi-source monitoring records of dyeing and printing wastewater are continuously read, and the pollution load value and key pollutant concentration value are recalculated at each time scale. The pollution load value and key pollutant concentration value are respectively judged against the pollution load range and key pollutant concentration interval. When the pollution load value or key pollutant concentration value triggers the stage transition condition threshold, the next collaborative removal scheduling action is switched. At each time scale, the time scale mark, the summary of the multi-source monitoring records of dyeing and printing wastewater, the pollution load value, the key pollutant concentration value, the coupling intensity gradient level, the target collaborative strategy type identifier, the execution status of the collaborative removal scheduling action sequence, and the threshold judgment result are aggregated and organized to form a water quality state element set. The water quality state element sets corresponding to each time scale are collected in chronological order to generate a collaborative treatment water quality state set.

[0115] It should be noted that the threshold values ​​for the stage transition conditions (example range: 5-20 for pollution load values ​​crossing the boundary of adjacent pollution load ranges and 2-10 for key pollutant concentration values ​​crossing the boundary of adjacent key pollutant concentration ranges) are set with the lower limit based on the upper limit of the short-term fluctuation amplitude recorded by multi-source monitoring of dyeing and printing wastewater under stable operating conditions to avoid jitter-induced false switching, and the upper limit based on the distribution of typical transition amplitudes before stage switching in the operating condition behavior benchmark template to ensure timely triggering of stage switching.

[0116] In summary, this invention achieves coordinated grouping of pollutants by constructing a multi-dimensional behavioral similarity matrix and progressively aggregating it, thereby accurately identifying linked pollutants and improving the targeted treatment of water, wastewater, sewage, or sludge; and by dividing the coupling strength gradient to generate a multi-stage control rule chain, it achieves dynamic matching between treatment strategies and water quality status, thereby improving the treatment efficiency and stability of water, wastewater, sewage, or sludge.

[0117] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for the synergistic removal of pollutants from dyeing wastewater, characterized in that: comprising, Collecting dyeing wastewater multi-source monitoring records, extracting behavior characteristics, solidifying as working condition behavior benchmark template, generating pollutant behavior characteristics; Based on the pollutant behavior characteristics, a multi-dimensional behavior similarity matrix is constructed, and progressive aggregation and soft association labeling are performed to generate a dyeing wastewater pollutant cooperative grouping; Track the concentration change linkage of pollutants in the dyeing wastewater pollutant cooperative grouping, and divide the coupling strength gradient according to the pollution load and the concentration of key pollutants to generate the coupling strength gradient configuration set; According to the coupling strength gradient configuration set, the pollution load range is read, the key pollutant concentration interval is matched, and the cooperative strategy type is selected, and the regulation intensity range is set to generate a multi-stage cooperative regulation rule chain; Combine the multi-stage cooperative regulation rule chain with the dyeing wastewater multi-source monitoring record to perform real-time scheduling, obtain the cooperative removal treatment strategy and execute it to generate a cooperative treatment water quality state set.

2. The method for synergistic removal of pollutants from dyeing wastewater as claimed in claim 1, wherein: The dyeing wastewater multi-source monitoring record includes chroma data, degradable organic matter data, non-degradable organic matter data, auxiliary agent residue data, water, wastewater, sewage or sludge treatment related data.

3. The method for synergistic removal of pollutants from dyeing wastewater according to claim 2, characterized in that: The generation of pollutant behavior characteristics is as follows, Extract the concentration change amplitude, change direction, change rhythm characteristics and working condition response characteristics from the dyeing wastewater multi-source monitoring record to generate an initial pollutant behavior characteristic record set; Group the initial pollutant behavior characteristic record set according to the working condition and production formula, and perform behavior trajectory comparison and behavior mode aggregation within the group to generate a working condition behavior mode set; Solidify the working condition behavior mode set as a working condition behavior benchmark template, and integrate the remaining behavior characteristic records to generate pollutant behavior characteristics.

4. The method for synergistic removal of pollutants from dyeing wastewater as claimed in claim 3 wherein: The steps of constructing a multi-dimensional behavior similarity matrix based on pollutant behavior characteristics are as follows, Filter and reorganize the pollutant behavior characteristic data set, and fill in the missing behavior characteristic fields to generate a behavior characteristic comparison candidate set; Multi-dimensional comparison is performed on the behavior characteristic comparison candidate set, and the comparison difference is set as the multi-dimensional behavior similarity index of the pollutant category to generate a multi-dimensional behavior similarity matrix.

5. The method for synergistic removal of pollutants from dyeing wastewater as claimed in claim 4, wherein: The steps of performing progressive aggregation and soft association labeling to generate a dyeing wastewater pollutant cooperative grouping are as follows, Based on the multi-dimensional behavior similarity matrix, select the pollutant category with the highest multi-dimensional behavior similarity index to perform progressive aggregation to generate a pollutant initial aggregation grouping record set; Perform soft association weight distribution on the pollutant initial aggregation grouping record set, and combine the isolated pollutant categories to construct a dyeing wastewater pollutant cooperative grouping.

6. The method for synergistic removal of pollutants from dyeing effluents according to claim 5, characterized in that: The steps of tracking the concentration change linkage of pollutants in the dyeing wastewater pollutant cooperative grouping are as follows, Aggregating the pollutant behavior characteristics belonging to the same dyeing wastewater pollutant cooperative grouping according to a unified time axis, calculating the pollution load and the concentration of key pollutants, and generating a concentration linkage tracking data set; Based on the concentration linkage tracking data set, compare the pollutant behavior characteristics in the dyeing wastewater pollutant cooperative grouping, and store them paired with the concentration of key pollutants to generate concentration change linkage characteristics.

7. The method for the synergistic removal of pollutants from dyeing effluents according to claim 6, characterized in that: The steps of dividing the coupling strength gradient according to the pollution load and the concentration of key pollutants to generate the coupling strength gradient configuration set are as follows, The pollution load in the concentration change linkage feature is dynamically divided and paired with the key pollutant concentration to generate a pollution load interval and a key pollutant concentration interval; Based on the pollution load interval and the key pollutant concentration interval, the change degree of the concentration change linkage feature is compared, the coupling strength gradient level is divided, and a coupling strength gradient configuration set is generated.

8. The method for the synergistic removal of pollutants from dyeing effluents according to claim 7, characterized in that: According to the coupling strength gradient configuration set, the pollution load range reading, the key pollutant concentration interval matching and the collaborative strategy type screening are performed, and the steps are as follows, The pollution load range is read from the coupling strength gradient configuration set, and the key pollutant concentration interval matching is performed to generate an initial collaborative regulation range; Based on the initial collaborative regulation range, the collaborative strategy type that meets the pollution load range and the key pollutant concentration interval is screened from the dyeing wastewater collaborative treatment experience rule library.

9. The method for the synergistic removal of pollutants from dyeing effluents according to claim 8, characterized in that: The regulation strength range is set to generate a multi-stage collaborative regulation rule chain, and the steps are as follows, According to the coupling strength gradient level, the regulation strength range is set for the collaborative strategy type, and the trigger condition and the end condition are added to generate a stage collaborative regulation configuration record; The stage collaborative regulation configuration record is sequentially connected according to the pollution load range and the key pollutant concentration interval, and the stage transition condition is added to generate a multi-stage collaborative regulation rule chain.

10. The method for synergistic removal of pollutants from dyeing effluents according to claim 9, characterized in that: The multi-stage collaborative regulation rule chain is combined with the dyeing wastewater multi-source monitoring record to perform real-time scheduling, obtain a collaborative removal treatment strategy and execute it to generate a collaborative treatment water quality state set, and the steps are as follows, The real-time acquisition of the dyeing wastewater multi-source monitoring record, the pollution load and the key pollutant concentration are calculated again, and are mapped to the multi-stage collaborative regulation rule chain to generate a real-time state description; The real-time state description is compared with the trigger condition and the regulation strength range in the stage collaborative regulation configuration record to generate a collaborative removal real-time scheduling record; Based on the collaborative removal real-time scheduling record, the target collaborative strategy type is selected from the multi-stage collaborative regulation rule chain, and the applicability constraint judgment is performed to generate a collaborative removal treatment strategy; The target collaborative strategy type in the collaborative removal treatment strategy is expanded into a collaborative removal scheduling action sequence and executed to aggregate and arrange the water quality state elements to generate a collaborative treatment water quality state set.

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