Method and system for treating industrial wastewater in mortar production

Abstract

The present application relates to the technical field of industrial wastewater treatment, and discloses a method and system for treating industrial wastewater in mortar production, which comprises the following steps: obtaining the source process and characteristic property real-time parameters of the industrial wastewater in mortar production, classifying the treatment difficulty of the industrial wastewater, obtaining the treatment difficulty level, intelligently distributing the treatment process of the industrial wastewater, performing time classification and gravity sedimentation analysis on the industrial wastewater, calculating the addition amount of flocculants in each classification of industrial wastewater based on the data set after gravity sedimentation, obtaining the flocculant addition amount sequence, performing time gradient stirring process analysis on the industrial wastewater, obtaining the flocculating material quality index, performing good and bad classification treatment process analysis on the industrial wastewater, obtaining the separation liquid index, analyzing the water quality of the industrial wastewater after multi-stage filtration treatment based on the residual pollutant concentration, and obtaining the final water quality result; the present application can improve the efficiency of treating industrial wastewater in mortar production.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, and in particular to a method and system for treating industrial wastewater in mortar production. Background Technology

[0002] In the field of industrial wastewater treatment in mortar production, existing technologies generally lack accurate correlation analysis between the characteristics of the wastewater source process and real-time parameters. Most adopt fixed treatment processes and have not established a treatment difficulty classification mechanism based on process identification and multi-dimensional water quality parameters. Traditional methods rely solely on the concentration of a single pollutant to judge the treatment difficulty, ignoring the differences in the characteristics of wastewater discharged from different production processes. This leads to a mismatch between the treatment process and the actual difficulty of the wastewater, resulting in overtreatment of low-difficulty wastewater and waste of resources, while high-difficulty wastewater fails to meet standards due to insufficient treatment intensity. At the same time, the lack of dynamic process allocation logic makes it impossible to adjust the treatment plan according to real-time water quality fluctuations, which seriously restricts treatment efficiency and economy.

[0003] Furthermore, existing flocculation-sedimentation treatment processes often employ constant time and single stirring parameters, failing to achieve refined control and synergistic optimization across time dimensions. Gravity sedimentation stages are typically performed for fixed durations, making it impossible to capture the dynamic sedimentation process through multi-time-point data acquisition and determine the optimal sedimentation cycle. The calculation of flocculant dosage during flocculation does not incorporate post-sedimentation water quality datasets for hierarchical optimization, and parameters such as stirring speed and duration lack gradient adjustment mechanisms. This results in uneven mixing of flocculant and wastewater, unstable floc formation quality, and a tendency for insufficient or excessive flocculation. This affects solid-liquid separation efficiency, increases reagent costs and subsequent treatment burdens, and fails to meet the demand for efficient and precise treatment of mortar production wastewater. Therefore, how to adjust treatment schemes based on real-time water quality fluctuations and achieve refined control and synergistic optimization across time dimensions has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for treating industrial wastewater in mortar production, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for treating industrial wastewater in mortar production, comprising:

[0006] S1. Obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and classify the treatment difficulty of the industrial wastewater to obtain the treatment difficulty level.

[0007] S2. Based on the treatment difficulty level, intelligently allocate treatment processes for the industrial wastewater to obtain an optimized treatment flow.

[0008] S3. Based on the optimized processing flow, perform time-sequential gravity sedimentation analysis on the industrial wastewater to obtain a dataset after gravity sedimentation.

[0009] S4. Based on the dataset after gravity sedimentation, calculate the amount of flocculant added to each stage of the industrial wastewater to obtain a flocculant addition sequence.

[0010] S5. Based on the flocculant addition sequence, the industrial wastewater with added flocculant is subjected to time gradient stirring process analysis to obtain the flocculant quality index.

[0011] S6. Based on the flocculant quality index and the treatment difficulty level, analyze the treatment process of the industrial wastewater by classifying its quality to obtain the separation liquid index containing concentration changes.

[0012] S7. Based on the residual pollutant concentration in the separated liquid index, analyze the water quality of the industrial wastewater after multi-stage filtration treatment to obtain the final water quality result.

[0013] In a preferred embodiment, the step of obtaining real-time parameters of the source process and characteristic properties of mortar production industrial wastewater, and classifying the wastewater according to its treatment difficulty to obtain a treatment difficulty level, includes:

[0014] S201, Extract the source process information of the industrial wastewater from the preset mortar production process database to obtain the process identifier;

[0015] S202, Obtain real-time parameters of the characteristic properties of industrial wastewater, and perform data cleaning on the real-time parameters of the characteristic properties to obtain a characteristic parameter vector;

[0016] S203, the feature parameter vector is scaled to map the original parameter values ​​to a uniform range to obtain the normalized feature parameter vector of the industrial wastewater.

[0017] S204, Based on the process identifier, calculate the processing difficulty index of the normalized feature parameter vector to obtain the preliminary difficulty value of the industrial wastewater.

[0018] S205, perform a level classification process on the preliminary difficulty value to obtain the treatment difficulty level of the industrial wastewater.

[0019] In a preferred embodiment, the step of intelligently allocating treatment processes for the industrial wastewater based on the treatment difficulty level to obtain an optimized treatment flow includes:

[0020] S301, Based on the processing difficulty level, extract a set of candidate processes corresponding to the processing difficulty level from a preset industrial wastewater treatment process database to obtain the initial process set for the industrial wastewater.

[0021] S302, based on real-time processing constraint parameters, multiply the standardized values ​​of all parameters in each process by the corresponding weight coefficients, accumulate all the multiplication results one by one to calculate the applicability score, and combine the applicability scores of each process in an orderly manner to obtain the process score vector of the industrial wastewater.

[0022] S303, perform a selection and sorting operation on the process score vector to obtain the optimized treatment process for the industrial wastewater.

[0023] In a preferred embodiment, the step of performing time-sequential gravity sedimentation analysis on the industrial wastewater based on the optimized treatment process to obtain a dataset after gravity sedimentation includes:

[0024] S401, Based on the optimized processing flow, extract the time-grading parameters of the industrial wastewater to obtain the time-grading vector;

[0025] S402, Based on the time-level vector, collect water quality monitoring data of the industrial wastewater after gravity sedimentation at multiple preset time points to obtain the original sedimentation dataset;

[0026] S403, perform data integration processing on the original sedimentation dataset to obtain the dataset after gravity sedimentation of the industrial wastewater.

[0027] In a preferred embodiment, the step of calculating the amount of flocculant added to each stage of the industrial wastewater based on the dataset after gravity sedimentation of the industrial wastewater to obtain a flocculant addition sequence includes:

[0028] S501, extract the characteristic parameters of each grade of the industrial wastewater from the dataset after gravity sedimentation of the industrial wastewater to obtain the grade parameter vector.

[0029] S502, based on the graded parameter vector, calculate the flocculant addition amount for each grade, and integrate the flocculant addition amount for each grade into a unified dataset by multiplying the flocculant efficiency coefficient, the pollutant concentration of the grade, and the wastewater volume to obtain the initial addition amount value.

[0030] S503, the initial addition amount value is serialized to convert the initial addition amount value into an ordered sequence of data to obtain the flocculant addition amount sequence.

[0031] In a preferred embodiment, the step of performing time-gradient stirring process analysis on the industrial wastewater with added flocculant based on the flocculant addition sequence to obtain the flocculant quality index includes:

[0032] S601, combined with the preset stirring process rules, calculate the stirring speed, stirring duration and rotation speed change curve of each time period, so as to dynamically configure the flocculant addition sequence and wastewater characteristics. The parameters set by the dynamic parameter configuration include time point sequence, rotation speed value, acceleration parameter, temperature control threshold and energy consumption limit, so as to obtain the stirring parameter vector of industrial wastewater.

[0033] S602, Multi-parameter real-time monitoring of flocs generated in the industrial wastewater during the time gradient stirring operation with added flocculant is performed to obtain a flocculant monitoring dataset;

[0034] S603, Based on the flocculant monitoring dataset, a comprehensive evaluation of the flocculation effect quality index of the industrial wastewater is performed, and the contribution values ​​of multiple monitoring parameters are integrated into a single quality index to obtain the flocculant quality index.

[0035] In a preferred embodiment, the process analysis of classifying the industrial wastewater into high-quality and low-quality treatments based on the flocculant quality index and the treatment difficulty level to obtain separation liquid indicators containing concentration changes includes:

[0036] S701, Based on the flocculant quality index and the treatment difficulty level, calculate the comprehensive quality score of the industrial wastewater to obtain an initial quality value;

[0037] S702, the initial quality value is classified into grades, the initial quality value is converted into discrete grades, and the initial quality value is compared with three preset thresholds. If the initial quality value is less than the first threshold, it is classified as grade 1; if it is between the first threshold and the second threshold, it is classified as grade 2; if it reaches or exceeds the second threshold, it is classified as grade 3, so as to obtain the quality grade of the industrial wastewater.

[0038] S703, based on the aforementioned quality level, the industrial wastewater is subjected to pollutant concentration change analysis to obtain separation liquid index containing concentration changes.

[0039] In a preferred embodiment, the step of classifying the initial quality values ​​to obtain the quality level of the industrial wastewater includes:

[0040] S801, based on the preset grade determination vector, the initial quality value is matched to the determination interval and numerical comparison is performed. If the initial quality value is less than the first threshold, it is matched as interval 1; if it is between the first and second thresholds, it is matched as interval 2; if it reaches or exceeds the second threshold, it is matched as interval 3, so as to obtain the initial grade identifier of the industrial wastewater.

[0041] S802, perform grade label mapping on the initial grade identifier to obtain the quality grade of the industrial wastewater.

[0042] In a preferred embodiment, the step of analyzing the water quality of the industrial wastewater after multi-stage filtration based on the residual pollutant concentration in the separated liquid index to obtain the final water quality result includes:

[0043] S901, extract the residual pollutant concentration parameter from the separated liquid index to obtain the pollutant concentration vector of the industrial wastewater;

[0044] S902, Based on the pollutant concentration vector, configure multi-level filtration parameters to obtain the filtration parameter set for the industrial wastewater;

[0045] S903, Based on the set of filtration parameters, analyze the multi-stage filtration process of the industrial wastewater to obtain a dataset of filtered water quality.

[0046] S904, The filtered water quality dataset is evaluated and calculated to obtain the final water quality result of the industrial wastewater.

[0047] To address the aforementioned problems, the present invention also provides a system for treating industrial wastewater from mortar production, the system comprising:

[0048] The processing difficulty level module is used to obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and to classify the processing difficulty of the industrial wastewater to obtain the processing difficulty level.

[0049] The intelligent allocation module is used to intelligently allocate the treatment process of the industrial wastewater based on the treatment difficulty level, so as to obtain an optimized treatment process.

[0050] The gravity sedimentation module is used to perform time-series gravity sedimentation analysis on the industrial wastewater based on the optimized treatment process, and obtain a dataset after gravity sedimentation.

[0051] The flocculant addition module is used to calculate the amount of flocculant added to each stage of the industrial wastewater based on the dataset after gravity sedimentation, so as to obtain a flocculant addition sequence.

[0052] The time gradient module is used to perform time gradient stirring process analysis on the industrial wastewater with added flocculant based on the flocculant addition sequence, so as to obtain the flocculant quality index.

[0053] The quality classification module is used to analyze the treatment process of industrial wastewater by classifying its quality based on the flocculant quality index and the treatment difficulty level, so as to obtain the separation liquid index containing concentration changes.

[0054] The final water quality result module is used to analyze the water quality of the industrial wastewater after multi-stage filtration based on the residual pollutant concentration in the separated liquid index, so as to obtain the final water quality result.

[0055] 1. This invention enables precise and efficient wastewater treatment. The system extracts the process identifiers from the wastewater source and combines them with multi-dimensional feature parameter vectors such as pH, turbidity, and chemical oxygen demand after data cleaning. Through normalization and treatment difficulty index calculation, it accurately classifies the wastewater treatment difficulty level, avoiding the one-sidedness of traditional single-indicator judgments. Then, based on real-time processing constraint parameters, it calculates the applicability score of each candidate process and quickly sorts and selects the optimal process combination to form an optimized treatment flow. This solves the problem of "over-treatment" or "under-treatment" of wastewater from different processes using fixed processes, and dynamically matches wastewater characteristics with treatment resources, reducing energy consumption and time costs, and improving overall treatment efficiency and economy.

[0056] 2. This invention significantly improves solid-liquid separation efficiency and reagent utilization efficiency. During the gravity sedimentation stage, monitoring data is collected at multiple preset time points using a time-gradient vector. This data is then weighted and averaged to generate a precise post-sedimentation dataset, allowing for dynamic capture of sedimentation process changes and determination of the optimal sedimentation period, avoiding the blindness of traditional fixed-duration sedimentation. In the flocculation treatment stage, the flocculant dosage is calculated hierarchically based on the post-sedimentation dataset and formed into an ordered sequence. Combined with time-gradient stirring parameter configuration, the stirring speed and duration are dynamically adjusted, promoting thorough mixing of the flocculant and wastewater. This effectively improves the uniformity of floc particle size and sedimentation performance, solving the problems of insufficient flocculation or excessive reagent dosage under traditional constant parameters. It also reduces the burden on subsequent filtration, ensuring stable effluent quality that meets standards, while controlling reagent costs, achieving a dual optimization of treatment effect and economy. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of a method for treating industrial wastewater in mortar production according to an embodiment of the present invention.

[0058] Figure 2 A functional block diagram of an industrial wastewater treatment system for mortar production provided in an embodiment of the present invention;

[0059] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0061] This application provides a method for treating industrial wastewater from mortar production. The execution entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for treating industrial wastewater from mortar production can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0062] Reference Figure 1 The diagram shown is a flowchart illustrating a method for treating industrial wastewater in mortar production according to an embodiment of the present invention. In this embodiment, the method for treating industrial wastewater in mortar production includes:

[0063] S1. Obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and classify the treatment difficulty of the industrial wastewater to obtain the treatment difficulty level.

[0064] In this embodiment of the invention, the step of obtaining real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and classifying the wastewater according to its treatment difficulty to obtain a treatment difficulty level, includes:

[0065] S201, Extract the source process information of the industrial wastewater from the preset mortar production process database to obtain the process identifier;

[0066] S202, Obtain real-time parameters of the characteristic properties of industrial wastewater, and perform data cleaning on the real-time parameters of the characteristic properties to obtain a characteristic parameter vector;

[0067] S203, the feature parameter vector is scaled to map the original parameter values ​​to a uniform range to obtain the normalized feature parameter vector of the industrial wastewater.

[0068] S204, Based on the process identifier, calculate the processing difficulty index of the normalized feature parameter vector to obtain the preliminary difficulty value of the industrial wastewater.

[0069] S205, perform a level classification process on the preliminary difficulty value to obtain the treatment difficulty level of the industrial wastewater.

[0070] It should be noted that the pre-set mortar production process database is established by collecting historical wastewater discharge data from each process in the entire mortar production process. The specific method is as follows: based on the raw material processing, mixing, molding and curing and equipment cleaning processes in mortar production, wastewater source information is collected in real time through a sensor network and stored in a relational database. The database includes process number, process name, wastewater type identifier, typical pollutant concentration range and discharge flow parameters.

[0071] It should be noted that the process identifier is obtained by querying the wastewater source process information from the preset mortar production process database. It is a code used to uniquely identify the mortar production process, including the process ID, process type code and associated wastewater characteristic benchmark value. Essentially, it provides a process characteristic basis for classifying the treatment difficulty. By distinguishing the wastewater characteristics of different processes, it assists in the intelligent allocation of subsequent treatment processes.

[0072] It should be noted that data cleaning refers to the method of preprocessing real-time parameters of the characteristic properties of industrial wastewater to remove outliers and noise. Specifically, for each data point in the parameter vector, its standard score is calculated, which is the difference between the data point and the overall mean divided by the standard deviation. If the absolute value of the standard score is greater than 3, the data point is identified as an outlier and replaced with the average of its neighboring data points to obtain the characteristic parameter vector. The purpose of data cleaning is to improve data quality and avoid erroneous data interfering with the calculation of subsequent processing difficulties.

[0073] It should be noted that the feature parameter vector contains the physicochemical parameters of industrial wastewater, specifically including pH value, turbidity, chemical oxygen demand, biological oxygen demand, total suspended solids and heavy metal ion concentration; the role of the feature parameter vector is to quantify the characteristics of wastewater and provide multi-dimensional input data for normalization treatment and difficulty index calculation.

[0074] It should be noted that the mapping of the original parameter values ​​to a unified range is achieved through normalization, which converts the original parameter values ​​into relative proportional values ​​within the range of 0 to 1. Specifically, the original value of the parameter is subtracted from the preset minimum threshold, and then divided by the difference between the preset maximum threshold and the minimum threshold to obtain the standardized value. The method unifies all parameters to the same dimension, which facilitates subsequent comparative analysis and calculation.

[0075] Furthermore, normalization serves to make different parameters comparable, ensuring the fairness and accuracy of the processing difficulty index calculation.

[0076] It should be noted that the processing difficulty index is a quantitative indicator calculated based on process identifiers and normalized feature parameter vectors, and its mathematical expression is as follows:

[0077] ;

[0078] In the formula, To handle the difficulty index, Numerical coding for process identification. For the first A normalized parameter value, and These are preset weighting coefficients.

[0079] Furthermore, and The two each account for half of the weight.

[0080] It should be noted that the grade classification process divides the continuous processing difficulty index into several clear grade intervals by setting preset thresholds. Specifically, when the difficulty index is below the first threshold, it is classified as grade 1; when it is between the first and second thresholds, it is classified as grade 2; and when it reaches or exceeds the second threshold, it is classified as grade 3. This method converts continuous values ​​into clear grade classifications, providing a clear basis for judgment in subsequent process selection and resource allocation.

[0081] It should be noted that the treatment difficulty level is a classification result of the difficulty of wastewater treatment. It is a level identifier that indicates the complexity of wastewater treatment and includes a level number, level description and recommended treatment intensity parameters. The purpose of the treatment difficulty level is to guide the selection and configuration of subsequent treatment processes to ensure that the treatment process matches the characteristics of the wastewater.

[0082] S2. Based on the treatment difficulty level, intelligently allocate treatment processes for the industrial wastewater to obtain an optimized treatment flow.

[0083] The intelligent allocation of treatment processes for the industrial wastewater based on the treatment difficulty level to obtain an optimized treatment flow includes:

[0084] S301, Based on the processing difficulty level, extract a set of candidate processes corresponding to the processing difficulty level from a preset industrial wastewater treatment process database to obtain the initial process set for the industrial wastewater.

[0085] S302, based on real-time processing constraint parameters, multiply the standardized values ​​of all parameters in each process by the corresponding weight coefficients, accumulate all the multiplication results one by one to calculate the applicability score, and combine the applicability scores of each process in an orderly manner to obtain the process score vector of the industrial wastewater.

[0086] S303, perform a selection and sorting operation on the process score vector to obtain the optimized treatment process for the industrial wastewater.

[0087] It should be noted that the pre-set industrial wastewater treatment process database was established by integrating historical wastewater treatment cases, process specifications, and expert knowledge. The specific establishment method is as follows: based on the characteristics of mortar production wastewater, the operation parameters of processes such as sedimentation, flocculation, filtration, adsorption, and biochemical treatment are collected, process characteristics are extracted through data mining technology, and stored in a structured database. The database includes process number, process type, applicable treatment difficulty level range, treatment efficiency index, energy consumption parameters, time cost, and equipment requirements data.

[0088] It should be noted that the initial process set is a set of candidate processes that are extracted from a pre-set industrial wastewater treatment process database and matched with the treatment difficulty level. It includes process identification sequence, process type code, basic treatment parameters and expected effect indicators. It is used to provide multiple feasible treatment solutions, provide basic data for subsequent applicability assessment, and ensure that the process selection is adapted to the characteristics of the wastewater.

[0089] It should be noted that the applicability score calculation is a quantitative assessment based on real-time processing of constraint parameters and process characteristics. The mathematical expression for the applicability score calculation is as follows:

[0090] ;

[0091] In the formula, For the first The applicability score of each process, For the first The weighting coefficients of each constraint parameter. For the first The first process Parameter values, This is a function for standardizing parameters.

[0092] Furthermore, The weighting coefficients are pre-assigned based on expert experience and historical data. It is used to objectively assess the priority of each process under current constraints and to provide a basis for ranking.

[0093] Furthermore, the parameter standardization function is a mathematical transformation method that converts parameter values ​​with different dimensions into a unified standard scale. Specifically, it involves subtracting the preset lower threshold of the parameter from its original value and then dividing by the difference between the upper and lower thresholds to obtain the standardized value. This standardized value is used to eliminate the dimensional differences between different parameters, ensuring that all parameters are comparable in subsequent calculations, thereby improving the fairness and accuracy of the evaluation.

[0094] It should be noted that the process score vector is a set of applicability scores for all candidate processes. It organizes the applicability of each process into an ordered list in the form of scores, which is used to integrate the scattered process evaluation results into unified structured data, providing a clear data foundation for subsequent sorting, screening and process optimization.

[0095] It should be noted that selection sort refers to sorting the scores in the process score vector in descending order and selecting the process with the highest score based on the sorting result. It is used to achieve efficient sorting through the quicksort algorithm. Essentially, it selects the optimal combination of processes from the candidate processes to ensure the efficiency and economy of the processing flow.

[0096] Furthermore, quicksort is a divide-and-conquer sorting algorithm, specifically as follows: First, a pivot value is selected, and the vector is divided into two subsequences, where all scores in the left subsequence are less than or equal to the pivot value, and all scores in the right subsequence are greater than or equal to the pivot value. Then, the same operation is recursively performed on the left and right subsequences until the entire sequence is ordered. This is used to efficiently sort the process score vectors in descending order, ensuring the rapid selection of the optimal process and improving the decision-making efficiency of the processing flow.

[0097] It should be noted that the optimized treatment process is a sequence of procedures determined after selection and sorting. It includes the execution order of procedures, parameter configuration scheme, resource allocation plan and expected treatment time. It is used to guide the actual operation of the wastewater treatment system, realize the intelligentization of the treatment process and the optimization of resources, and improve the overall treatment efficiency.

[0098] S3. Based on the optimized processing flow, perform time-sequential gravity sedimentation analysis on the industrial wastewater to obtain a dataset after gravity sedimentation.

[0099] Based on the optimized processing flow, time-sequential gravity sedimentation analysis is performed on the industrial wastewater to obtain a dataset after gravity sedimentation, including:

[0100] S401, Based on the optimized processing flow, extract the time-grading parameters of the industrial wastewater to obtain the time-grading vector;

[0101] S402, Based on the time-level vector, collect water quality monitoring data of the industrial wastewater after gravity sedimentation at multiple preset time points to obtain the original sedimentation dataset;

[0102] S403, perform data integration processing on the original sedimentation dataset to obtain the dataset after gravity sedimentation of the industrial wastewater.

[0103] It should be noted that the time grading parameters are configuration parameters related to time control extracted from the optimization process. The specific extraction method is as follows: by parsing the time sequence control module in the optimization process, the parameters of sedimentation stage division, time interval setting and sampling frequency are read. These parameters are used to define the time segmentation strategy of the gravity sedimentation process, ensuring that sedimentation analysis is carried out systematically in different time dimensions, and providing a basis for multi-time point data collection.

[0104] It should be noted that the time-level vector is a numerical representation of the time-level parameters, which includes the time point sequence, time interval value, level identifier and sampling priority data. It is used to organize the time control parameters into a structured sequence to guide the timing and frequency of monitoring data acquisition during gravity sedimentation.

[0105] It should be noted that multiple preset time point collection refers to data collection at multiple specific time points set according to the time-level vector during the gravity sedimentation process. This is used to capture the dynamic changes in the sedimentation process, analyze the sedimentation efficiency trend by comparing data from multiple time points, identify the optimal sedimentation time, and avoid the limitations of single-point sampling.

[0106] It should be noted that gravity sedimentation is a physical treatment method that uses gravity to achieve solid-liquid separation. The specific process is as follows: when industrial wastewater is in a static or slow-flowing state, suspended solid particles gradually settle to the bottom due to density differences. The supernatant and precipitate separate into layers, which is used to remove settleable solids from the wastewater and reduce turbidity and suspended solids concentration.

[0107] It should be noted that the monitoring data are water quality parameters collected in real time during the gravity sedimentation process, including supernatant turbidity, sediment interface height, solids concentration, pH value, and temperature data. These data are used to quantify the sedimentation effect and provide real-time feedback and original evidence for evaluating the sedimentation process.

[0108] It should be noted that the original sedimentation dataset is a collection of monitoring data collected at multiple preset time points. It is in the form of a time-parameter matrix, containing timestamp sequences, corresponding monitoring parameter values, and data quality identifiers. It is used to store the original observation records of the sedimentation process and provide a basic data source for subsequent data integration.

[0109] It should be noted that data integration processing is an operation to standardize and aggregate the original sedimented dataset. Specifically, the weighted average algorithm is used to calculate the weighted average of the data at each time point to eliminate data fluctuations. At the same time, normalization processing is used to unify the units of different parameters to improve the consistency and comparability of the data, generating a clean and well-organized dataset for easy use in subsequent analysis.

[0110] Furthermore, the weighted average algorithm calculates the weighted average by multiplying the parameter values ​​at each time point by a preset weight coefficient and then summing them up, in order to eliminate random fluctuations in the data and generate a smooth and consistent comprehensive dataset for subsequent analysis.

[0111] The preset weights are based on the weight allocation of sampling priority.

[0112] Furthermore, the sampling priority weight allocation process is as follows:

[0113] 1. Define priorities: Identify key time points based on process characteristics and classify them into high, medium, and low priorities;

[0114] 2. Assign original weights: assign a value of 3 to high-priority time points, a value of 2 to medium-priority time points, and a value of 1 to low-priority time points;

[0115] 3. Normalization: Divide the original weights of all time points by the sum of the weights to make the final weight coefficients sum to 1;

[0116] Through this process, key data receive a higher weight in subsequent calculations, ensuring that the analysis results more accurately reflect the core characteristics of the process.

[0117] It should be noted that the dataset after gravity sedimentation is structured data obtained through data integration and processing. It includes integrated turbidity values, sedimentation efficiency indicators, time-averaged concentrations, and sedimentation stability parameters to characterize the overall state of wastewater after gravity sedimentation treatment and to provide input data for calculating the amount of flocculant to be added.

[0118] S4. Based on the dataset after gravity sedimentation, calculate the amount of flocculant added to each stage of the industrial wastewater to obtain a flocculant addition sequence.

[0119] Based on the dataset of the industrial wastewater after gravity sedimentation, the amount of flocculant added to each stage of the industrial wastewater is calculated to obtain a flocculant addition sequence, including:

[0120] S501, extract the characteristic parameters of each grade of the industrial wastewater from the dataset after gravity sedimentation of the industrial wastewater to obtain the grade parameter vector.

[0121] S502, based on the graded parameter vector, calculate the flocculant addition amount for each grade, and integrate the flocculant addition amount for each grade into a unified dataset by multiplying the flocculant efficiency coefficient, the pollutant concentration of the grade, and the wastewater volume to obtain the initial addition amount value.

[0122] S503, the initial addition amount value is serialized to convert the initial addition amount value into an ordered sequence of data to obtain the flocculant addition amount sequence.

[0123] It should be noted that the characteristic parameters of industrial wastewater are key parameters related to the flocculation process extracted from the dataset after gravity sedimentation. Specifically, they include turbidity after sedimentation, suspended solids concentration, pH value, temperature, and residual heavy metal ion concentration. These parameters are used to quantify the physicochemical state of wastewater after sedimentation treatment, providing accurate input for calculating the amount of flocculant to be added, and ensuring that the amount added is optimized based on the actual characteristics of the wastewater.

[0124] It should be noted that the flocculant dosage for each stage is a precise dosage calculated using a mathematical model, the mathematical expression of which is:

[0125] ;

[0126] in For the first The amount of flocculant added in each grade is determined. This is the flocculant efficiency coefficient. For the first The concentration of pollutants at each level, For the first Each stage of wastewater volume;

[0127] It should be noted that the flocculant dosage for each grade refers to a personalized flocculant dosage calculated for different wastewater grades. This is to ensure efficient flocculation, avoid insufficient or excessive addition, optimize treatment effects, and control costs.

[0128] It should be noted that the initial addition value is a preliminary calculation result set of the addition amount of each staged flocculant. It represents the raw addition amount data that has not been serialized and includes the stage number, addition amount value, unit identifier and calculation timestamp. The purpose of the initial addition value is to temporarily store the intermediate calculation results and provide structured input data for subsequent serialization operations.

[0129] It should be noted that serialization is a data processing procedure that converts the initial added quantity values ​​into an ordered sequence. The method involves arranging the added quantity values ​​in ascending or descending order according to the hierarchical numbering and generating a continuous sequence through data encapsulation technology. The purpose of serialization is to organize the scattered added quantity data into a logically coherent sequence, which is convenient for subsequent time gradient stirring processes to call in sequence.

[0130] It should be noted that the flocculant addition sequence is an ordered list of addition amounts, where each element corresponds to the flocculant dosage and related parameters for different treatment stages. This is used to guide dosage control during the time gradient stirring process, ensuring that the stirring operation and the addition amount are coordinated, thereby improving the accuracy and efficiency of the treatment process.

[0131] S5. Based on the flocculant addition sequence, the industrial wastewater with added flocculant is subjected to time gradient stirring process analysis to obtain the flocculant quality index.

[0132] The analysis of the time gradient stirring process of the industrial wastewater with added flocculant, based on the flocculant addition sequence, to obtain the flocculant quality index includes:

[0133] S601, combined with the preset stirring process rules, calculate the stirring speed, stirring duration and rotation speed change curve of each time period, so as to dynamically configure the flocculant addition sequence and wastewater characteristics. The parameters set by the dynamic parameter configuration include time point sequence, rotation speed value, acceleration parameter, temperature control threshold and energy consumption limit, so as to obtain the stirring parameter vector of industrial wastewater.

[0134] S602, Multi-parameter real-time monitoring of flocs generated in the industrial wastewater during the time gradient stirring operation with added flocculant is performed to obtain a flocculant monitoring dataset;

[0135] S603, Based on the flocculant monitoring dataset, a comprehensive evaluation of the flocculation effect quality index of the industrial wastewater is performed, and the contribution values ​​of multiple monitoring parameters are integrated into a single quality index to obtain the flocculant quality index.

[0136] It should be noted that dynamic parameter configuration is achieved by analyzing the dosage change pattern in the flocculant addition sequence and combining it with preset stirring process rules to calculate the stirring speed, stirring duration, and rotation speed change curves for each time period. The parameters set include time point sequence, rotation speed value, acceleration parameter, temperature control threshold, and energy consumption limit. This is used to optimize the synergy between the stirring process and flocculant addition, ensure the optimization of floc formation conditions, improve flocculation efficiency, and reduce energy consumption.

[0137] It should be noted that the stirring parameter vector integrates the control parameters in the time gradient stirring process into an ordered set of values. This is used to organize the dispersed stirring conditions into structured instructions, which facilitates the precise execution of staged operations by the stirring equipment.

[0138] It should be noted that the time gradient stirring operation is a dynamic stirring process performed according to the stirring parameter vector. The method is as follows: based on the parameter sequence in the vector, the stirring speed and direction are adjusted at preset time points to achieve a smooth transition from high-speed mixing to low-speed flocculation. This is used to promote full contact between flocculant and pollutants through gradient changes in speed and time, optimize the floc growth environment, and improve the size uniformity and settling performance of flocculants.

[0139] Furthermore, settling performance refers to a comprehensive indicator of the efficiency and speed at which flocs separate from wastewater under the action of gravity. It reflects factors such as the settling rate of flocs, interface clarity, and the degree of solid-liquid separation. It is used to ensure that flocs settle quickly and stably, optimize the solid-liquid separation process, and improve the efficiency of subsequent treatment steps and the quality of effluent.

[0140] It should be noted that the flocculant monitoring dataset is flocculant characteristic data collected in real time during the time gradient stirring process. It includes flocculant particle size distribution, settling rate, interface clarity, turbidity change curves and image morphological features. It is used to comprehensively record the dynamic changes of the flocculation process, provide multi-dimensional observation data for effect evaluation, and ensure the objectivity and accuracy of the evaluation results.

[0141] It should be noted that the comprehensive evaluation of flocculation effect quality index is a quantitative analysis method based on floc monitoring datasets. It integrates the contribution values ​​of multiple monitoring parameters into a single quality index through a weighted comprehensive algorithm. The specific calculation method is as follows: multiply the scores of each parameter by their corresponding weight coefficients, sum them, and then divide by the total weight coefficient to generate a unified quality index. This is used to simplify the multi-dimensional judgment of flocculation effect, integrate complex data into an intuitive index, and provide a reliable basis for subsequent processing decisions.

[0142] Furthermore, the quantitative analysis method is implemented as follows: based on the flocculant monitoring dataset, the standardized scores of each parameter are calculated through a weighted comprehensive algorithm, and the scores are multiplied by preset weight coefficients and then accumulated to generate a single quality index. This index is used to objectively integrate multi-dimensional monitoring data into a unified quantitative indicator, eliminate subjective judgment bias, provide a scientific basis for flocculation effect evaluation, and support the optimization decision-making of treatment strategies.

[0143] It should be noted that the floc quality index is the output result of the comprehensive evaluation of the flocculation effect quality index. It is used to characterize the overall quality of flocs and includes the index value, quality grade label and contribution of key parameters. The role of the floc quality index is to quantify the effect of the flocculation process, provide core input data for the classification of good and bad treatment, and guide the generation and optimization of the separation liquid index.

[0144] S6. Based on the flocculant quality index and the treatment difficulty level, analyze the treatment process of the industrial wastewater by classifying its quality to obtain the separation liquid index containing concentration changes.

[0145] In this embodiment of the invention, the process analysis of classifying the industrial wastewater into high-quality and low-quality treatments based on the flocculant quality index and the treatment difficulty level to obtain separation liquid indicators containing concentration changes includes:

[0146] S701, Based on the flocculant quality index and the treatment difficulty level, calculate the comprehensive quality score of the industrial wastewater to obtain an initial quality value;

[0147] S702, the initial quality value is classified into grades, the initial quality value is converted into discrete grades, and the initial quality value is compared with three preset thresholds. If the initial quality value is less than the first threshold, it is classified as grade 1; if it is between the first threshold and the second threshold, it is classified as grade 2; if it reaches or exceeds the second threshold, it is classified as grade 3, so as to obtain the quality grade of the industrial wastewater.

[0148] S703, based on the aforementioned quality level, the industrial wastewater is subjected to pollutant concentration change analysis to obtain separation liquid index containing concentration changes.

[0149] It should be noted that the overall performance score is a quantitative indicator calculated based on the flocculant quality index and the treatment difficulty level. Its mathematical expression is as follows:

[0150] ;

[0151] In the formula, To determine the overall score based on merits and demerits, The flocculant quality index. To handle difficulty levels, and These are preset weighting coefficients.

[0152] Furthermore, and The two scores each account for half of the weight, and the combined score of superiority and inferiority is used to integrate the flocculation effect and the processing difficulty to generate a unified evaluation value, providing a numerical basis for the classification of superiority and inferiority.

[0153] It should be noted that the comprehensive score is calculated by performing a weighted summation operation. The numerical representations of the flocculant quality index and the treatment difficulty level are multiplied by their corresponding weight coefficients and then summed to generate a comprehensive score. This score is used to integrate multi-dimensional parameters into a single indicator, simplifying the input complexity of subsequent level classification and improving the efficiency and objectivity of processing decisions.

[0154] It should be noted that the initial merit value is the result of the comprehensive merit score calculation. It is the raw value without grade division and includes the score value, calculation timestamp and related parameter identifier. It is used to temporarily store the intermediate results of the comprehensive evaluation, provide structured input data for the grade division operation, and ensure the continuity of the process.

[0155] It should be noted that the grading is a process of converting initial quality values ​​into discrete grades through preset thresholds. Specifically, the initial quality value is compared with three preset thresholds. If the initial quality value is less than the first threshold, it is classified as Grade 1; if it is between the first and second thresholds, it is classified as Grade 2; and if it reaches or exceeds the second threshold, it is classified as Grade 3. The purpose of grading is to convert continuous values ​​into clear classification grades, so that the system can execute different processing strategies according to the grades, thereby optimizing resource allocation.

[0156] It should be noted that the pollutant concentration change analysis is an operation that calculates the degree of pollutant reduction based on the good and bad grades. Its expression is: the pollutant concentration change value equals the initial pollutant concentration multiplied by 1 minus the grade-related removal rate. The grade-related removal rate is obtained by querying a preset removal rate table and is used to quantify the pollutant reduction effect during the treatment process, providing concentration change data for the generation of separation liquid indicators.

[0157] It should be noted that the separation liquid index is a set of results from the analysis of pollutant concentration changes. It is a set of parameters representing the state of the treated wastewater, including residual pollutant concentration, concentration change rate, separation efficiency index, and water quality stability parameters. It is used to comprehensively describe the separation effect of wastewater after quality classification treatment, providing key input data for subsequent multi-stage filtration treatment and guiding the final water quality assessment.

[0158] In this embodiment of the invention, the step of classifying the initial quality values ​​to obtain the quality level of the industrial wastewater includes:

[0159] S801, based on the preset grade determination vector, the initial quality value is matched to the determination interval and numerical comparison is performed. If the initial quality value is less than the first threshold, it is matched as interval 1; if it is between the first and second thresholds, it is matched as interval 2; if it reaches or exceeds the second threshold, it is matched as interval 3, so as to obtain the initial grade identifier of the industrial wastewater.

[0160] S802, perform grade label mapping on the initial grade identifier to obtain the quality grade of the industrial wastewater.

[0161] It should be noted that the preset grade determination vector is a threshold sequence preset by analyzing historical processing data and expert experience. The specific preset method is as follows: based on the statistical distribution of wastewater treatment effect, the threshold boundary is determined by clustering algorithm and stored in the configuration file. The grade determination vector contains ordered threshold values, the number of thresholds and the grade interval definition, which is used to provide a standardized division basis for interval matching and ensure the objectivity and consistency of grade division.

[0162] It should be noted that interval matching is an operation that compares the initial merit value with the threshold in the grade determination vector to determine the interval to which it belongs. Specifically, the numerical comparison is performed. If the initial merit value is less than the first threshold, it is matched as interval 1. If it is between the first and second thresholds, it is matched as interval 2. If it reaches or exceeds the second threshold, it is matched as interval 3. The purpose of interval matching is to quickly map continuous values ​​to discrete interval identifiers, providing input for grade label mapping.

[0163] It should be noted that the initial level identifier is the output result of interval matching, representing the intermediate code of the interval position. It includes the interval number, matching timestamp, and confidence parameters to temporarily store intermediate data of interval matching, ensuring that the input structure of the level label mapping is standardized.

[0164] It should be noted that the level label mapping is the process of converting the initial level identifier into user-friendly labels, as follows: Based on a preset label database, the interval number is mapped to a descriptive label through a table lookup operation, which enhances the readability and usability of the level and facilitates system decision-making and manual intervention.

[0165] It should be noted that the quality rating is the final output of the rating label mapping, representing a qualitative evaluation of the industrial wastewater treatment effect. It includes the rating label, rating description, recommended treatment intensity, and related parameters, which are used to guide subsequent pollutant concentration change analysis and separation liquid index generation, thereby achieving differentiated treatment strategies and resource optimization.

[0166] S7. Based on the residual pollutant concentration in the separated liquid index, analyze the water quality of the industrial wastewater after multi-stage filtration treatment to obtain the final water quality result.

[0167] In this embodiment of the invention, the step of analyzing the water quality of the industrial wastewater after multi-stage filtration based on the residual pollutant concentration in the separated liquid index to obtain the final water quality result includes:

[0168] S901, extract the residual pollutant concentration parameter from the separated liquid index to obtain the pollutant concentration vector of the industrial wastewater;

[0169] S902, Based on the pollutant concentration vector, configure multi-level filtration parameters to obtain the filtration parameter set for the industrial wastewater;

[0170] S903, Based on the set of filtration parameters, analyze the multi-stage filtration process of the industrial wastewater to obtain a dataset of filtered water quality.

[0171] S904, The filtered water quality dataset is evaluated and calculated to obtain the final water quality result of the industrial wastewater.

[0172] It should be noted that the pollutant concentration vector is a numerical set of residual pollutant parameters extracted from the separated liquid indicators. Specifically, it includes heavy metal ion concentration, organic pollutant content, suspended solids concentration, and microbial indicators. It is used to quantify the pollution status of wastewater after high-quality and low-quality treatment, providing accurate input data for the configuration of multi-stage filtration parameters and ensuring that the filtration process is highly targeted.

[0173] It should be noted that configuring multi-level filtration parameters is a dynamic parameter setting based on pollutant concentration vectors and preset filtration rules. The configuration method is as follows: by parsing the concentration distribution pattern in the pollutant concentration vector and combining it with the filter media characteristic library, the media type, filtration accuracy, flow rate control value and backwashing cycle of each filtration stage are calculated to optimize the matching degree between the filtration sequence and pollutant removal, improve filtration efficiency and extend equipment life.

[0174] It should be noted that the filter parameter set is an integrated output of configuring multi-level filter parameters. It represents a structured set that defines the operating conditions of multi-level filtration, including the filter stage sequence, media combination scheme, pressure control threshold, and energy consumption limit parameters. It is used to organize the dispersed filter control conditions into executable instructions to guide the precise operation of multi-level filtration equipment.

[0175] It should be noted that multi-stage filtration is a sequential processing procedure based on a set of filtration parameters. The method involves using a series of processes including sand filtration, activated carbon adsorption, and membrane filtration. Based on the sequence of stages and media combinations in the parameter set, pollutants of different particle sizes and types are removed stage by stage. This is used to achieve deep removal of pollutants through graded treatment, ensuring that the effluent quality meets the standards for reuse or discharge.

[0176] It should be noted that the filtered water quality dataset is a record of water quality status collected during the multi-stage filtration process. It is a data set representing the characteristics of the filtered wastewater, including turbidity of the filtered water, concentration of residual pollutants, pH value, conductivity and biotoxicity indicators. It is used to comprehensively record the filtration effect and provide multi-dimensional observation basis for the final water quality assessment.

[0177] It should be noted that the assessment calculation adopts the weighted summation method, which multiplies the standardized values ​​of each water quality parameter in the filtered water quality dataset by the corresponding weight coefficient and then sums them to generate the final water quality score. This score is used to integrate multi-dimensional water quality data into a unified quantitative indicator, objectively judge the water quality compliance status, and provide a reliable basis for treatment decisions.

[0178] It should be noted that the final water quality result is the output of the assessment calculation, representing the ultimate evaluation of the industrial wastewater treatment effect. It includes water quality score values, compliance level indicators, compliance status of key parameters, and treatment recommendations, providing authoritative basis for wastewater discharge or reuse and realizing closed-loop management of the treatment process.

[0179] like Figure 2 The diagram shown is a functional block diagram of an industrial wastewater treatment system for mortar production according to an embodiment of the present invention.

[0180] The industrial wastewater treatment system 100 for mortar production described in this invention can be installed in an electronic device. Depending on the functions implemented, the industrial wastewater treatment system 100 for mortar production may include a treatment difficulty level module 101, an intelligent allocation module 102, a gravity sedimentation module 103, a flocculant addition module 104, a time gradient module 105, a quality classification module 106, and a final water quality result module 107. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0181] In this embodiment, the functions of each module / unit are as follows:

[0182] The processing difficulty level module is used to obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and to classify the processing difficulty of the industrial wastewater to obtain the processing difficulty level.

[0183] The intelligent allocation module is used to intelligently allocate the treatment process of the industrial wastewater based on the treatment difficulty level, so as to obtain an optimized treatment process.

[0184] The gravity sedimentation module is used to perform time-series gravity sedimentation analysis on the industrial wastewater based on the optimized treatment process, and obtain a dataset after gravity sedimentation.

[0185] The flocculant addition module is used to calculate the amount of flocculant added to each stage of the industrial wastewater based on the dataset after gravity sedimentation, so as to obtain a flocculant addition sequence.

[0186] The time gradient module is used to perform time gradient stirring process analysis on the industrial wastewater with added flocculant based on the flocculant addition sequence, so as to obtain the flocculant quality index.

[0187] The quality classification module is used to analyze the treatment process of industrial wastewater by classifying its quality based on the flocculant quality index and the treatment difficulty level, so as to obtain the separation liquid index containing concentration changes.

[0188] The final water quality result module is used to analyze the water quality of the industrial wastewater after multi-stage filtration based on the residual pollutant concentration in the separated liquid index, so as to obtain the final water quality result.

[0189] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0190] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0191] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0192] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0193] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0194] Finally, 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.

Claims

1. A method for treating industrial wastewater in mortar production, characterized in that, The method includes: S1. Obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and classify the treatment difficulty of the industrial wastewater to obtain the treatment difficulty level. S2. Based on the treatment difficulty level, intelligently allocate treatment processes for the industrial wastewater to obtain an optimized treatment flow. S3. Based on the optimized processing flow, perform time-sequential gravity sedimentation analysis on the industrial wastewater to obtain a dataset after gravity sedimentation. S4. Based on the dataset after gravity sedimentation, calculate the amount of flocculant added to each stage of the industrial wastewater to obtain a flocculant addition sequence. The step of performing time-sequential gravity sedimentation analysis on the industrial wastewater based on the optimized processing flow to obtain a dataset after gravity sedimentation includes: S401, Based on the optimized processing flow, extract the time-grading parameters of the industrial wastewater to obtain the time-grading vector; S402, Based on the time-level vector, collect water quality monitoring data of the industrial wastewater after gravity sedimentation at multiple preset time points to obtain the original sedimentation dataset; S403, perform data integration processing on the original sedimentation dataset to obtain the dataset after gravity sedimentation of the industrial wastewater; S5. Based on the flocculant addition sequence, the industrial wastewater with added flocculant is subjected to time gradient stirring process analysis to obtain the flocculant quality index. The flocculant dosage for each grade refers to the personalized flocculant dosage calculated for different wastewater grades. S6. Based on the flocculant quality index and the treatment difficulty level, analyze the treatment process of the industrial wastewater by classifying its quality to obtain the separation liquid index containing concentration changes. S7. Based on the residual pollutant concentration in the separated liquid index, analyze the water quality of the industrial wastewater after multi-stage filtration treatment to obtain the final water quality result.

2. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, The process of obtaining real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production is used to classify the wastewater into different treatment difficulty levels, including: S201, Extract the source process information of the industrial wastewater from the preset mortar production process database to obtain the process identifier; S202, Obtain real-time parameters of the characteristic properties of industrial wastewater, and perform data cleaning on the real-time parameters of the characteristic properties to obtain a characteristic parameter vector; S203, the feature parameter vector is scaled to map the original parameter values ​​to a uniform range to obtain the normalized feature parameter vector of the industrial wastewater. S204, Based on the process identifier, calculate the processing difficulty index of the normalized feature parameter vector to obtain the preliminary difficulty value of the industrial wastewater. S205, perform a level classification process on the preliminary difficulty value to obtain the treatment difficulty level of the industrial wastewater.

3. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, The intelligent allocation of treatment processes for the industrial wastewater based on the treatment difficulty level to obtain an optimized treatment flow includes: S301, Based on the processing difficulty level, extract a set of candidate processes corresponding to the processing difficulty level from a preset industrial wastewater treatment process database to obtain the initial process set for the industrial wastewater. S302, based on real-time processing constraint parameters, multiply the standardized values ​​of all parameters in each process by the corresponding weight coefficients, accumulate all the multiplication results one by one to calculate the applicability score, and combine the applicability scores of each process in an orderly manner to obtain the process score vector of the industrial wastewater. S303, perform a selection and sorting operation on the process score vector to obtain the optimized treatment process for the industrial wastewater.

4. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, Based on the dataset of the industrial wastewater after gravity sedimentation, the amount of flocculant added to each stage of the industrial wastewater is calculated to obtain a flocculant addition sequence, including: S501, extract the characteristic parameters of each grade of the industrial wastewater from the dataset after gravity sedimentation of the industrial wastewater to obtain the grade parameter vector. S502, based on the graded parameter vector, calculate the flocculant addition amount for each grade, and integrate the flocculant addition amount for each grade into a unified dataset by multiplying the flocculant efficiency coefficient, the pollutant concentration of the grade, and the wastewater volume to obtain the initial addition amount value. S503, the initial addition amount value is serialized to convert the initial addition amount value into an ordered sequence of data to obtain the flocculant addition amount sequence.

5. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, The analysis of the time gradient stirring process of the industrial wastewater with added flocculant, based on the flocculant addition sequence, to obtain the flocculant quality index includes: S601, combined with the preset stirring process rules, calculate the stirring speed, stirring duration and rotation speed change curve of each time period, so as to dynamically configure the flocculant addition sequence and wastewater characteristics. The parameters set by the dynamic parameter configuration include time point sequence, rotation speed value, acceleration parameter, temperature control threshold and energy consumption limit, so as to obtain the stirring parameter vector of industrial wastewater. S602, Multi-parameter real-time monitoring of flocs generated in the industrial wastewater during the time gradient stirring operation with added flocculant is performed to obtain a flocculant monitoring dataset; S603, Based on the flocculant monitoring dataset, a comprehensive evaluation of the flocculation effect quality index of the industrial wastewater is performed, and the contribution values ​​of multiple monitoring parameters are integrated into a single quality index to obtain the flocculant quality index.

6. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, The process analysis of classifying the industrial wastewater into high-quality and low-quality treatments based on the flocculant quality index and the treatment difficulty level, to obtain separation liquid indicators containing concentration changes, includes: S701, Based on the flocculant quality index and the treatment difficulty level, calculate the comprehensive quality score of the industrial wastewater to obtain an initial quality value; S702, the initial quality value is classified into grades, the initial quality value is converted into discrete grades, and the initial quality value is compared with three preset thresholds. If the initial quality value is less than the first threshold, it is classified as grade 1; if it is between the first threshold and the second threshold, it is classified as grade 2; if it reaches or exceeds the second threshold, it is classified as grade 3, so as to obtain the quality grade of the industrial wastewater. S703, based on the aforementioned quality level, the industrial wastewater is subjected to pollutant concentration change analysis to obtain separation liquid index containing concentration changes.

7. The method for treating industrial wastewater in mortar production as described in claim 6, characterized in that, The step of classifying the initial quality values ​​to obtain the quality level of the industrial wastewater includes: S801, based on the preset grade determination vector, the initial quality value is matched to the determination interval and numerical comparison is performed. If the initial quality value is less than the first threshold, it is matched as interval 1; if it is between the first and second thresholds, it is matched as interval 2; if it reaches or exceeds the second threshold, it is matched as interval 3, so as to obtain the initial grade identifier of the industrial wastewater. S802, perform grade label mapping on the initial grade identifier to obtain the quality grade of the industrial wastewater.

8. The method for treating industrial wastewater in mortar production as described in claim 1, characterized in that, The analysis of the residual pollutant concentration in the separated liquid index to determine the water quality of the industrial wastewater after multi-stage filtration treatment, in order to obtain the final water quality result, includes: S901, extract the residual pollutant concentration parameter from the separated liquid index to obtain the pollutant concentration vector of the industrial wastewater; S902, Based on the pollutant concentration vector, configure multi-level filtration parameters to obtain the filtration parameter set for the industrial wastewater; S903, Based on the set of filtration parameters, analyze the multi-stage filtration process of the industrial wastewater to obtain a dataset of filtered water quality. S904, The filtered water quality dataset is evaluated and calculated to obtain the final water quality result of the industrial wastewater.

9. A system for treating industrial wastewater from mortar production, characterized in that, The system includes: The processing difficulty level module is used to obtain real-time parameters of the source process and characteristic properties of industrial wastewater from mortar production, and to classify the processing difficulty of the industrial wastewater to obtain the processing difficulty level. The intelligent allocation module is used to intelligently allocate the treatment process of the industrial wastewater based on the treatment difficulty level, so as to obtain an optimized treatment process. The gravity sedimentation module is used to perform time-series gravity sedimentation analysis on the industrial wastewater based on the optimized treatment process, and obtain a dataset after gravity sedimentation. The step of performing time-sequential gravity sedimentation analysis on the industrial wastewater based on the optimized processing flow to obtain a dataset after gravity sedimentation includes: S401, Based on the optimized processing flow, extract the time-grading parameters of the industrial wastewater to obtain the time-grading vector; S402, Based on the time-level vector, collect water quality monitoring data of the industrial wastewater after gravity sedimentation at multiple preset time points to obtain the original sedimentation dataset; S403, perform data integration processing on the original sedimentation dataset to obtain the dataset after gravity sedimentation of the industrial wastewater; The flocculant addition module is used to calculate the amount of flocculant added to each stage of the industrial wastewater based on the dataset after gravity sedimentation, so as to obtain a flocculant addition sequence. The time gradient module is used to perform time gradient stirring process analysis on the industrial wastewater with added flocculant based on the flocculant addition sequence, so as to obtain the flocculant quality index. The flocculant dosage for each grade refers to the personalized flocculant dosage calculated for different wastewater grades. The quality classification module is used to analyze the treatment process of industrial wastewater by classifying its quality based on the flocculant quality index and the treatment difficulty level, so as to obtain the separation liquid index containing concentration changes. The final water quality result module is used to analyze the water quality of the industrial wastewater after multi-stage filtration based on the residual pollutant concentration in the separated liquid index, so as to obtain the final water quality result.

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