An industrial sewage treatment method and system based on big data

Abstract

The present invention relates to the technical field of industrial sewage treatment, and specifically provides an industrial sewage treatment method and system based on big data, including the following steps: collecting sewage samples discharged from multiple industrial sources, obtaining chemical composition data, and initially sorting out the types and contents of recyclable resources in the samples to form a resource feature list. In the present invention, through detailed data analysis and the implementation of optimized processes, the extraction efficiency and quality of recyclable resources in industrial sewage are significantly improved. Collecting sewage samples from industrial sources and conducting chemical composition analysis provides data support for subsequent resource recovery strategies, making the classification and recovery of resources more accurate. By constructing an economic evaluation model to determine the recovery priority, the environmental benefits and economic benefits are effectively integrated, enhancing the intelligent level of industrial sewage treatment. This real-time adjustment mechanism significantly improves the efficiency and quality of resource recovery.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial sewage treatment, and particularly to an industrial sewage treatment method and system based on big data. Background Art

[0002] The technical field of industrial sewage treatment involves various methods and technologies aimed at reducing or eliminating the environmental impact of sewage generated during industrial production. This field includes physical, chemical, and biological treatment methods to effectively remove harmful substances in sewage, such as heavy metals, organic pollutants, and suspended particles. The core goal of industrial sewage treatment is to meet environmental protection standards and recycle water resources, which is crucial for protecting water bodies and ecosystems. With the progress of technology, modern industrial sewage treatment technologies are also continuously introducing advanced monitoring and control systems to improve treatment efficiency and reduce operating costs.

[0003] Among them, the industrial sewage treatment method based on big data refers to using big data technology to analyze and optimize the sewage treatment process. This method guides the decision-making and operation of sewage treatment by collecting and analyzing a large amount of data related to sewage treatment, such as pollutant concentration, operation status of treatment facilities, and environmental impact indicators. Applying big data can improve the efficiency and effect of industrial sewage treatment, make the treatment process more intelligent and automated, and at the same time help reduce energy consumption and operation and maintenance costs. The main use of this technology is to optimize the industrial sewage treatment process, ensure the achievement of environmental standards, and enhance the adaptability and response ability of the system.

[0004] Existing industrial sewage treatment technologies focus on the removal of pollutants rather than the recovery and reuse of resources, which to a certain extent restricts the exertion of environmental and economic potential. These methods mostly rely on traditional physical and chemical treatment means and do not fully consider the value of resource recovery during the treatment process, resulting in a large waste of recoverable resources. Existing technologies lack a real-time monitoring and adjustment mechanism during operation, making it difficult to optimize the treatment effect according to actual situations, which not only increases operating costs but also reduces the flexibility and response speed of the treatment system. For example, in the case of failing to adjust treatment parameters in real time, once the composition of the input sewage changes, the existing system cannot effectively respond, affecting the overall treatment efficiency and the effect of resource recovery. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art and propose an industrial sewage treatment method and system based on big data.

[0006] To achieve the above purpose, the present invention adopts the following technical solution. An industrial sewage treatment method based on big data includes the following steps:

[0007] S1: Collect sewage samples discharged from multiple industrial sources, obtain chemical composition data, initially sort out the types and contents of recyclable resources in the samples, and form a resource feature list.

[0008] S2: Based on the resource feature list, use the target screening logic of big data to classify metal ions and organic solvents. Through classification processing and label recording, generate resource classification data.

[0009] S3: Use the resource classification data to screen out metal ions and organic solvents with high frequencies and high values, construct an economic evaluation model to determine the recycling priority, and generate a priority recycling list.

[0010] S4: According to the priority recycling list, adjust the parameters of the industrial sewage recycling process, including temperature, pressure, and the dosage of chemical reaction agents. Through process simulation, test the efficiency and cost of multiple parameters to obtain optimized process parameters.

[0011] S5: Implement the optimized process parameters into the real-time industrial sewage resource recycling process. By collecting and analyzing the data during the implementation process, make real-time adjustments and verify the optimization of the resource recovery rate to form an adjusted recovery strategy.

[0012] S6: Use the adjusted recovery strategy to carry out industrial sewage resource recycling, monitor the resource recovery efficiency and operating cost in actual operation, and obtain the recovery effect evaluation result by comparing the original resource characteristics and the resource characteristics after recovery.

[0013] As a further solution of the present invention, the resource feature list includes copper, iron, zinc of metal ions, toluene, benzene, ethanol of organic solvents and their corresponding concentration levels. The resource classification data includes the classification of metal ions into heavy metal types and light metal types, and the classification of organic solvents into volatile and non-volatile types. The priority recycling list includes copper and silver of metal ions with high values, and toluene and xylene of organic solvents with high demand. The optimized process parameters include the set reaction temperature range, reaction pressure level, reaction time, and the type of catalyst used. The adjusted recovery strategy includes adjusted operation steps, optimized resource recovery efficiency, and expected cost-benefit. The recovery effect evaluation result includes the collected resource recovery efficiency, cost analysis, and resource quality comparison.

[0014] As a further solution of the present invention, the steps of collecting sewage samples discharged from multiple industrial sources, obtaining chemical composition data, and initially sorting out the types and contents of recyclable resources in the samples to form a resource feature list are specifically as follows:

[0015] S101: Collect sewage samples from multiple industrial sources, including the steel, chemical, and textile industries. Check the representativeness of the samples, use sampling bottles and GPS to record the sampling point locations, and generate a chemical composition data set.

[0016] S102: Based on the chemical composition data set, identify recyclable resource samples including metals and organic substances. By recording the type of each resource and initializing the estimated content, generate a resource recovery list.

[0017] S103: Through the resource recovery list, conduct quantitative analysis on each resource, apply spectral analysis and mass balance to calculate the content of each resource, optimize the consistency and credibility of the data, and form a resource characteristic list.

[0018] As a further solution of the present invention, based on the resource characteristic list, using the target screening logic of big data, classify metal ions and organic solvents. The steps of generating resource classification data by classification processing and label recording are specifically as follows:

[0019] S201: Extract data records of metal ions and organic solvents from the resource characteristic list, check the data integrity and consistency, and assign classification labels to form initial screening data.

[0020] S202: Based on the initial screening data, use the attribute label matching technology to classify metal ions and organic solvents, distinguish chemical properties and uses, support the selection of recycling processes, and generate use processing data.

[0021] S203: Use the use processing data to mark the recycling value and environmental protection priority of each metal ion and organic solvent, record the classification situation and label information, check the traceability and applicability of the data, and generate resource classification data.

[0022] As a further solution of the present invention, using the resource classification data, screen out metal ions and organic solvents with high frequencies and large values, construct an economic evaluation model to determine the recycling priority, and the steps of generating a priority recycling list are specifically as follows:

[0023] S301: Based on the resource classification data, screen out metal ions and organic solvents with high frequencies through statistical frequency, evaluate the occurrence times and data quality of multiple resources, and form high-frequency resource data.

[0024] S302: According to the high-frequency resource data, analyze the market value and recycling cost of each resource, evaluate the economic feasibility of recycling by analyzing costs and benefits, determine the economic value indicators of the resources, and generate value evaluation data.

[0025] S303: Using the value evaluation data, adopting the multi-objective programming method, and calculating the priority scores of resources and generating a priority recycling list according to the factors of economic value, environmental impact, recycling cost, and market demand.

[0026] As a further solution of the present invention, the formula of the multi-objective programming method is as follows: Wherein, is the priority score of the resource, is the recycling value of the resource , is the recycling cost, represents the reciprocal of the recycling cost of the resource , represents the market demand for the resource , is the environmental impact, represents the absolute value of the environmental impact of the resource , is the economic value weight, is the recycling cost weight, is the market demand weight, is the environmental impact weight.

[0027] As a further solution of the present invention, according to the priority recycling list, adjusting the parameters of the industrial sewage recycling process, including temperature, pressure, and the dosage of chemical reaction agents, and testing the efficiency and cost of multiple parameters through process simulation, the specific steps for obtaining the optimized process parameters are as follows:

[0028] S401: Based on the priority recycling list, adjusting the key parameters of the recycling process, including adjusting the temperature setting, pressure level, and the ratio of chemical reaction agents, optimizing the recycling efficiency and quality, and forming a record of key parameter adjustment;

[0029] S402: Through the record of key parameter adjustment, testing the influence of the adjusted temperature, pressure, and the dosage of chemical reaction agents on the recycling efficiency of metal ions and organic solvents through process simulation, evaluating the effect of the change on the overall recycling cost, and generating a simulation test result;

[0030] S403: According to the simulation test result, balancing the recycling efficiency and cost-effectiveness, adjusting the characteristics and processing requirements of the process to match different types of resources, and generating optimized process parameters.

[0031] As a further solution of the present invention, implementing the optimized process parameters into the real-time industrial sewage resource recycling process, collecting and analyzing the data during the implementation process, making real-time adjustments and verifying the optimization of the resource recovery rate, the specific steps for forming an adjusted recycling strategy are as follows:

[0032] S501: Apply the optimized process parameters to the real-time industrial sewage resource recovery line, monitor and track the changes of key parameters in real time, including temperature, pressure and chemical dosage, check whether the operating conditions meet the optimization standards, and form real-time monitoring data;

[0033] S502: Through the real-time monitoring data, dynamically adjust the parameters in the process, evaluate the impact of the adjustment, make adjustments with reference to efficiency and cost factors, and generate a recovery strategy;

[0034] S503: According to the recovery strategy, evaluate the recovery effect, optimize and adjust the industrial sewage resource recovery strategy, match the different sewage types and resource characteristics, verify the economy of the recovery process, and generate an adjusted recovery strategy.

[0035] As a further solution of the present invention, using the adjusted recovery strategy to carry out industrial sewage resource recovery, monitoring the resource recovery efficiency and operating cost in actual operation, and obtaining the steps of the recovery effect evaluation result by comparing the original resource characteristics and the resource characteristics after recovery are specifically as follows:

[0036] S601: Implement the adjusted recovery strategy to carry out industrial sewage resource recovery operations, install monitoring equipment to track the resource recovery efficiency and operating cost in real time, and form an operation monitoring data set;

[0037] S602: According to the operation monitoring data set, collect and analyze the data in the recovery process, record the change information of the chemical component concentration and physical state by comparing the changes of the resource characteristics before and after the operation, evaluate the impact of the differential parameters on the recovery effect, and generate resource characteristic comparison data;

[0038] S603: According to the resource characteristic comparison data, evaluate the recovery efficiency and cost-benefit, optimize the process flow and parameter settings, verify the maximum efficiency and cost control of the recovery process, and obtain the recovery effect evaluation result.

[0039] An industrial sewage treatment system based on big data, the industrial sewage treatment system based on big data is used to execute the above-mentioned industrial sewage treatment method based on big data, and the system includes:

[0040] The sample data collection module collects sewage samples from multiple industrial sources, measures the chemical components in the samples, and forms a resource characteristic list;

[0041] The data classification module classifies metal ions and organic solvents based on the resource characteristic list, records the classification labels, and forms resource classification data;

[0042] The recycling sorting module uses the resource classification data to screen out metal ions and organic solvents with high value and high frequency, constructs an economic evaluation model, and obtains a priority recycling list;

[0043] The parameter adjustment module adjusts the temperature, pressure, and dosage of chemical reaction agents in the recycling process according to the priority recycling list, tests the cost efficiency of multiple parameters, and generates optimized process parameters;

[0044] The real-time monitoring module applies the optimized process parameters to real-time industrial sewage treatment, monitors the resource recycling process, analyzes the resource recycling efficiency and operating costs, and obtains the recycling effect evaluation result.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0046] In the present invention, through detailed data analysis and the implementation of optimized processes, the extraction efficiency and quality of recyclable resources in industrial sewage are significantly improved. Sewage samples are collected from the industrial source and chemically analyzed, providing data support for subsequent resource recycling strategies. This makes the classification and recycling of resources more accurate, especially for the recycling of metal ions and organic solvents, which not only increases the economic value of resources but also reduces the energy consumption in the treatment process. By constructing an economic evaluation model to determine the recycling priority, the environmental benefits and economic benefits are effectively integrated. Real-time data monitoring and adjustment of optimized process parameters further ensure the adaptability and optimization of the treatment process, enhancing the intelligence level of industrial sewage treatment. This real-time adjustment mechanism significantly improves the efficiency and quality of resource recycling. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the working process of the present invention;

[0048] Figure 2 It is a detailed flowchart of S1 of the present invention;

[0049] Figure 3 It is a detailed flowchart of S2 of the present invention;

[0050] Figure 4 It is a detailed flowchart of S3 of the present invention;

[0051] Figure 5 It is a detailed flowchart of S4 of the present invention;

[0052] Figure 6 It is a detailed flowchart of S5 of the present invention;

[0053] Figure 7 It is a detailed flowchart of S6 of the present invention;

[0054] Figure 8 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0057] Please refer to Figure 1 , the present invention provides a technical solution, an industrial sewage treatment method based on big data, including the following steps:

[0058] S1: Collect sewage samples discharged from multiple industrial sources, obtain chemical composition data, and initially sort out the types and contents of recyclable resources in the samples, including metal ions and organic solvents, to form a resource feature list;

[0059] S2: Based on the resource feature list, set screening parameters, and use the target screening logic of big data to classify metal ions and organic solvents. Through classification processing and label recording, generate resource classification data;

[0060] S3: Use the resource classification data to screen out metal ions and organic solvents with high frequencies and high values, and combine an economic evaluation model to determine the recycling priority to generate a priority recycling list;

[0061] S4: According to the priority recycling list, adjust the parameters of the industrial sewage recycling process, including temperature, pressure, and the dosage of chemical reaction agents. Through process simulation, test the efficiency and cost of multiple parameters, verify the optimal configuration of the process flow, and obtain optimized process parameters;

[0062] S5: Implement the optimized process parameters into the real-time industrial sewage resource recycling process. Through collecting and analyzing the data during the implementation process, make real-time adjustments and verify the optimization of the resource recovery rate to form an adjusted recovery strategy;

[0063] S6: Using the adjusted recovery strategy, conduct the recovery of industrial sewage resources, monitor the resource recovery efficiency and operation costs in actual operations, and obtain the recovery effect evaluation results by comparing the original resource characteristics and the resource characteristics after recovery.

[0064] The resource characteristics list includes copper, iron, and zinc of metal ions, toluene, benzene, and ethanol of organic solvents and their corresponding concentration levels. The resource classification data includes the classification of metal ions into heavy metals and light metals, and the classification of organic solvents into volatile and non-volatile types. The priority recovery list includes copper and silver of metal ions with high value, and toluene and xylene of organic solvents with high demand. The optimized process parameters include the set reaction temperature range, reaction pressure level, reaction time, and the type of catalyst used. The adjusted recovery strategy includes the adjusted operation steps, optimized resource recovery efficiency, and the expected cost-benefit to be achieved. The recovery effect evaluation results include the resource recovery efficiency of the collected resources, cost analysis, and resource quality comparison.

[0065] Please refer to Figure 2 , collect sewage samples discharged from multiple industrial sources, obtain chemical composition data, and initialize and organize the types and contents of recoverable resources in the samples to form the resource characteristics list. The specific steps are as follows:

[0066] S101: Collect sewage samples at multiple industrial sources, including the steel, chemical, and textile industries. Check the representativeness of the samples, use sampling bottles and GPS to record the sampling point locations, and the execution process of generating the chemical composition data set is as follows;

[0067] Collect sewage samples at multiple industrial sources. When collecting sewage samples, select appropriate sampling points according to the discharge characteristics of different industrial sources. The selection of sampling points depends on the preliminary investigation of sewage discharge outlets in the industrial area and historical data analysis to ensure that the samples can represent the sewage discharge characteristics of each industrial area. Use standardized sampling bottles to sample the sewage and mark the GPS positions of each sample for subsequent traceability and analysis. During the sampling process, record the specific time and environmental conditions of each sample collection, such as temperature and weather conditions. These data are of great reference value for subsequent chemical composition analysis. Input this information into the database to form the chemical composition data set.

[0068] S102: Based on the chemical composition data set, identify the recoverable resource samples including metals and organic substances, and generate the resource recovery list by recording the type of each resource and the initial estimated content. The execution process is as follows;

[0069] By recording the type of each resource and the initial estimated content, according to the formula , calculate the estimated total content of each resource. In the formula, represents the total content of resource ​ Represents the sample The resources in Concentration of Represents the sample Volume of Represents the total volume of all samples. Explanation of the formula and the derivation process of formula calculation: Set in the sewage samples of a certain chemical plant, the resource Is copper, estimating the concentrations In different samples are 2mg / L, 3mg / L, 1.5mg / L respectively, and the volumes Of the corresponding samples are 1L, 2L, 1.5L, and the total volume Is 4.5L. The process of calculating the total content of the resource Is as follows . The results show that in the detected samples, the average concentration of copper is 2.28mg / L, providing important data support for the generation of the resource recovery list.

[0070] S103: Through the resource recovery list, conduct quantitative analysis on each resource, apply spectroscopic analysis and mass balance to calculate the content of each resource, optimize the consistency and credibility of the data, and the execution process of forming the resource characteristic list is as follows;

[0071] Through the resource recovery list, conduct quantitative analysis on each resource, conduct qualitative and quantitative analysis on metals and organic substances in the sample through spectroscopic analysis. During the spectroscopic analysis process, compare the known spectroscopic data with the absorbance of each component in the sample to obtain the content of each component. For unknown components, use advanced spectroscopic techniques such as mass spectrometry coupling technology for identification. Quantitative analysis relies on the standard curve method, estimating the content in the sample by comparing the absorbance of the sample with the absorbance of the standard solution with known concentration. The calculation of mass balance involves multiplying the content obtained by spectroscopic analysis by the total volume to obtain the total mass of each resource. This process needs to ensure the accuracy of the calculation and data consistency to form the resource characteristic list.

[0072] Please refer to Figure 3 , based on the resource characteristic list, using the target screening logic of big data, classify metal ions and organic solvents, and the steps of generating resource classification data through classification processing and label recording are specifically as follows:

[0073] S201: Extract the data records of metal ions and organic solvents from the resource characteristic list, check the data integrity and consistency, and assign classification labels. The execution process of forming the initial screening data is as follows;

[0074] Extract data records of metal ions and organic solvents from the resource characteristics list. During the extraction process, verify the data in the resource characteristics list to ensure that the recorded data of metal ions and organic solvents are not only complete but also comply with the experimental data recording standards. The steps involve comparing the original collected data with the records in the existing database. Any inconsistent or missing data will be marked and subject to subsequent verification or supplementation. After completion, assign appropriate classification labels to each metal ion and organic solvent based on chemical properties and potential uses. These labels are not only based on chemical properties but also cover the application potential in different industrial processes. The setting of the labels enables more systematic subsequent data screening, including complete chemical composition information and applicable classification labels, providing a basis for further data processing and resource recovery decision-making, and forming initial screening data.

[0075] S202: Based on the initial screening data, use the attribute label matching technology to classify metal ions and organic solvents, distinguish chemical properties and uses, support the selection of recovery processes. The execution process for generating usage processing data is as follows;

[0076] Based on the initial screening data, use the attribute label matching technology. The attribute label matching technology plays a key role. Through advanced classification algorithms such as support vector machines or decision trees, analyze the attribute data of each chemical component, and effectively group metal ions and organic solvents according to chemical properties and actual application requirements. Each chemical component is assigned a clear label, and these labels reflect the roles and priorities in various recovery processes. The classification process ensures the logical consistency and applicability of the data, including not only classification information but also refining the industrial applications of each substance, providing strong data support for formulating more precise resource recovery strategies, and generating usage processing data.

[0077] S203: Use the usage processing data to mark the recovery value and environmental protection priority of each metal ion and organic solvent, record the classification situation and label information, and check the traceability and applicability of the data. The execution process for generating resource classification data is as follows;

[0078] Mark the recovery value and environmental protection priority of each metal ion and organic solvent, and calculate the comprehensive evaluation score of each substance according to the formula . In the formula, represents the comprehensive evaluation score, represents the recovery value, represents the environmental protection priority, and are the weight coefficients. Explanation of the formula and the derivation process of the formula calculation: Set the recovery value of a certain metal ion to 80, the environmental protection priority to 95, and the weight coefficients and Set them to 0.6 and 0.4 respectively according to the actual situation. Calculate the comprehensive evaluation score of metal ions The process is as follows: This result indicates that the recycling of metal ions not only has high economic value but also has a high priority in environmental protection. Therefore, it should be the focus of the recycling work.

[0079] Please refer to Figure 4 , using the resource classification data, screening out metal ions and organic solvents with high occurrence frequencies and high values, and the steps to construct an economic evaluation model to determine the recycling priority and generate a priority recycling list are as follows:

[0080] S301: Based on the resource classification data, screen out metal ions and organic solvents with high occurrence frequencies by statistical frequency, evaluate the occurrence times and data quality of multiple resources, and the execution process of forming high-frequency resource data is as follows;

[0081] Evaluate the occurrence times and data quality of multiple resources. According to the formula , calculate the occurrence frequency of resources. In the formula, represents the total occurrence times, represents the occurrence frequency of a single resource in the dataset, is the number of resource types. Explanation of the formula and the derivation process of formula calculation: Suppose there are three resources with occurrence frequencies being 10 times, 15 times, and 5 times respectively. The process of calculating the total occurrence times of these resources is as follows: This result indicates that in the dataset under investigation, the cumulative occurrence times of these resources are 30, and the high-frequency occurrence of these resources can be determined, and subsequent resource recycling priority division can be carried out.

[0082] S302: According to the high-frequency resource data, analyze the market value and recycling cost of each resource, evaluate the economic feasibility of recycling by analyzing costs and benefits, determine the economic value index of resources, and the execution process of generating value evaluation data is as follows;

[0083] Based on the high-frequency resource data, analyze the market value and recovery cost of each resource. The assessment of economic value is carried out according to market demand and supply conditions, and an expected market value is estimated for each resource. The analysis of recovery cost takes into account the expenses in logistics, processing, and reuse during the recovery process. Through detailed calculations, compare the recovery cost and market value of each resource to determine the net economic value of each resource. The net value reflects the direct economic benefit after recovery. Combining the occurrence frequency and net economic value of the resources, comprehensively evaluate the economic feasibility of the resources. These data not only guide the prioritization of the recovery work but also help policymakers optimize resource allocation to ensure the maximization of the economic benefits of the resource recovery work, forming value assessment data.

[0084] S303: Using the value assessment data, adopt the multi-objective programming method. According to the factors of economic value, environmental impact, recovery cost, and market demand, calculate the priority score of the resources, and the execution process of generating the priority recovery list is as follows;

[0085] The formula of the multi-objective programming method is as follows: Among them, is the priority score of the resource, is the recovery value of the resource , is the recovery cost, represents the reciprocal of the recovery cost of the resource , represents the market demand for the resource , is the environmental impact, represents the absolute value of the environmental impact of the resource , is the economic value weight, is the recovery cost weight, is the market demand weight, is the environmental impact weight.

[0086] Detailed explanation of the formula and the derivation process of the formula calculation:

[0087] This formula is the calculation method used to determine the resource recovery priority in the multi-objective optimization algorithm. It is necessary to quantify each parameter and obtain specific values through data monitoring, collection, or calculation methods to ensure accuracy and a realistic basis.

[0088] Parameter : The recovery value of the resource . It is determined by market analysis and historical transaction data analysis. For example, if the resource is aluminum, according to market data, the average value of recycled aluminum is $1,700 per ton.

[0089] Parameter : The resource Recycling cost. Obtained from recycling facility reports and industry standards. For example, the recycling cost of aluminum is $500 per ton.

[0090] Parameter : Market demand for resources : Based on industry demand reports and market research results. For example, the current market demand index for aluminum is 0.8.

[0091] Parameter : Environmental impact of resources : Based on environmental assessment reports and environmental impact studies. For example, the environmental impact score of aluminum is -0.3, indicating a slight negative impact on the environment.

[0092] Weight , , and : These parameters are weight coefficients, set according to the policies, economy, and environmental protection strategies of resource recycling. Let , , , . These weights reflect the influence of different indicators on the total score, among which , is higher than , , indicating that market value and demand are more important.

[0093] Use these parameters and weights to perform specific calculations according to the following steps:

[0094] , , ,

[0095] , , ,

[0096] Calculate the recycling value and cost impact:

[0097]

[0098]

[0099] Market demand and environmental impact:

[0100]

[0101]

[0102] Merge the above results into the total score formula:

[0103]

[0104] The result shows that the priority score of aluminum is 22674.68, which is relatively high in the multi-objective optimization framework, indicating that aluminum is a resource with priority for recycling. This score reflects a comprehensive consideration of the economic value, cost-effectiveness, market demand, and environmental impact of aluminum, guiding the arrangement of the priority order for resource recycling.

[0105] Please refer to Figure 5 , and according to the priority recycling list, adjust the parameters of the industrial sewage recycling process, including temperature, pressure, and the dosage of chemical reaction agents. Through process simulation, test the efficiency and cost of multiple parameters. The specific steps to obtain the optimized process parameters are as follows:

[0106] S401: Based on the priority recycling list, adjust the key parameters of the recycling process, including adjusting the temperature setting, pressure level, and the ratio of chemical reaction agents, to optimize the recycling efficiency and quality. The execution process of forming the key parameter adjustment record is as follows;

[0107] Based on the priority recycling list, adjust the key parameters of the recycling process, including adjusting the temperature setting, pressure level, and the ratio of chemical reaction agents. During the process, the adjustment of temperature, pressure, and the ratio of chemical reaction agents is carried out according to the chemical characteristics of the resources and the requirements of the recycling technology. The specific adjustment steps include initially setting the parameters, evaluating the preliminary effect of the adjustment through small-scale tests, making fine adjustments to the parameters according to the test results, and evaluating the effect again until optimization is achieved. This not only includes the specific values of the parameters but also the specific impact on the recycling efficiency and quality, which is an important basis for subsequent process optimization and cost control. Through systematic parameter adjustment, the recovery rates of metal ions and organic solvents can be significantly improved, and at the same time, ensure that the quality of the recycling process meets the predetermined standards, forming a key parameter adjustment record.

[0108] S402: Through the key parameter adjustment record, test the impact of the adjusted temperature, pressure, and dosage of chemical reaction agents on the recovery efficiency of metal ions and organic solvents through process simulation, and evaluate the effect of the change on the overall recycling cost. The execution process of generating the simulation test results is as follows;

[0109] Through the adjustment record of key parameters, the process simulation test is a crucial technical step. It simulates the specific impact of adjusted parameters on the recovery efficiency. By changing the temperature, pressure, and the dosage of chemical reactants, it observes the impact of each variable adjustment on the recovery efficiency. These test results help confirm that the parameter adjustment is the most effective. At the same time, the simulation test also evaluates the specific impact of these adjustments on the cost to ensure that the optimized recovery process is economically feasible. The simulated data provides precise guidance to help formulate the most cost-effective recovery plan. Record the economic and efficiency evaluations of each parameter adjustment to provide a scientific basis for process optimization and generate simulation test results.

[0110] S403: According to the simulation test results, balance the recovery efficiency and cost-benefit, and adjust the process to match the characteristics and processing requirements of different types of resources. The execution process for generating optimized process parameters is as follows;

[0111] Balance the recovery efficiency and cost-benefit, and calculate the optimized process benefit ratio according to the formula .

[0112] In the formula, represents the process benefit ratio, represents the recovery efficiency, represents the recovery cost savings, represents the total cost. Explanation of the formula and the derivation process of the formula calculation: Set the optimized recovery efficiency to 90%, the original recovery efficiency is 75%, and the recovery cost savings is 2000 units, and the total cost is 10000 units. The process of calculating the optimized process benefit ratio is as follows: . This result shows that by adjusting the process to match the characteristics and processing requirements of different types of resources, the optimized process not only improves the recovery efficiency but also achieves a significant improvement in cost efficiency, which helps the enterprise to achieve environmental protection while maintaining economic benefits.

[0113] Please refer to Figure 6 , and implement the optimized process parameters into the real-time industrial sewage resource recovery process. By collecting and analyzing the data during the implementation process, make real-time adjustments and verify the optimization of the resource recovery rate. The specific steps for forming the adjusted recovery strategy are as follows:

[0114] S501: Apply the optimized process parameters to the real-time industrial sewage resource recovery line, and monitor and track the changes of key parameters in real time, including temperature, pressure, and chemical dosage. Check that the operating conditions meet the optimization standards. The execution process for forming real-time monitoring data is as follows;

[0115] Apply the optimized process parameters to the real-time industrial sewage resource recovery line to ensure that all monitoring devices operate according to the latest process parameter settings. The monitoring system needs to capture every change in key parameters in real time and compare them with the set optimization criteria. Any deviation will trigger the alarm system to prompt the operator to check or adjust. The collection and analysis of real-time data are crucial for verifying the effectiveness of process parameters. The system will automatically record and analyze the data to ensure that the operating conditions continuously meet the requirements of process optimization, not only providing immediate feedback on operations but also forming the basic data for subsequent reviews and further optimizations, thus forming real-time monitoring data.

[0116] S502: Dynamically adjust the parameters in the process according to the real-time monitoring data, evaluate the impact of the adjustment, and make adjustments with reference to efficiency and cost factors. The execution process of generating the recovery strategy is as follows;

[0117] Dynamically adjust the parameters in the process, and calculate the adjustment factor according to the formula . In the formula, represents the adjustment factor, represents the pressure change, represents the original pressure, represents the temperature change, represents the original temperature, and are the adjustment weights. Explanation of the formula and the derivation process of the formula calculation: Set the original pressure to 100 kPa, the current pressure increases to 110 kPa, the original temperature is 300 K, and the current temperature drops to 290 K. The weights and are 0.5 and 0.5 respectively. The process of calculating the adjustment factor is as follows: .

[0118] This result indicates that according to the real-time monitoring data, the process needs to be slightly adjusted to maintain operation efficiency and cost control.

[0119] S503: Evaluate the recovery effect according to the recovery strategy, optimize and adjust the industrial sewage resource recovery strategy, match different sewage types and resource characteristics, verify the economy of the recovery process. The execution process of generating the adjusted recovery strategy is as follows;

[0120] During the process of evaluating the recycling effect according to the recycling strategy, three main indicators are concerned: the recycling rate, the resource quality, and the economic benefits. According to the recycling effect, the strategy is adjusted, including changing the chemical dosage, adjusting the treatment temperature, or modifying the pressure setting, to meet the specific requirements of different types of sewage. The process of optimizing the process is iterative and requires continuous adjustment based on real-time data and periodic evaluation. Through economic analysis, the cost-benefit ratio of the recycling strategy is confirmed to ensure that the strategy is not only technically feasible but also commercially sustainable. The adjusted strategy records the matching situation of each sewage type and resource characteristics. After these strategies are updated, they will be reapplied to the production line to ensure the continuous optimization of the recycling process and the maximization of economic benefits, generating the adjusted recycling strategy.

[0121] Please refer to Figure 7 , and using the adjusted recycling strategy, industrial sewage resource recycling is carried out. The steps to monitor the resource recycling efficiency and operating costs in actual operation and obtain the evaluation results of the recycling effect by comparing the original resource characteristics and the resource characteristics after recycling are as follows:

[0122] S601: Implement the adjusted recycling strategy to carry out industrial sewage resource recycling operations. Install monitoring equipment to track the resource recycling efficiency and operating costs in real time. The execution process of forming the operation monitoring data set is as follows;

[0123] Implement the adjusted recycling strategy, which includes comprehensively deploying sensors and monitoring systems to monitor the key operating parameters of the recycling line in real time, such as temperature, pressure, and chemical dosage. The monitoring system can record data in real time and process and analyze the collected data through an advanced data analysis platform. Real-time monitoring not only helps the operation team adjust the operation parameters immediately to adapt to the process requirements and environmental changes but also can predict potential equipment failures or efficiency declines and perform maintenance in advance to ensure the continuity and stability of the recycling process, providing a scientific basis for subsequent strategy adjustment and economic evaluation, achieving the maximization of cost-benefit and the continuous optimization of resource recycling efficiency, and obtaining the operation monitoring data set.

[0124] S602: According to the operation monitoring data set, collect and analyze the data during the recycling process. By comparing the changes in resource characteristics before and after the operation, record the change information of the chemical component concentration and physical state, and evaluate the impact of the differential parameters on the recycling effect. The execution process of generating the resource characteristic comparison data is as follows;

[0125] By comparing the changes in resource characteristics before and after the operation, according to the formula , calculate the change in the chemical component concentration. In the formula, represents the change amount of the chemical component concentration, and represent the chemical component concentrations after and before the operation respectively. Formula details and formula calculation derivation process: Set the concentration of a certain metal ion before the operation is 50 mg / L, and the concentration after the operation is reduced to 30 mg / L. The process of calculating the change in the chemical component concentration is as follows: . The results show that the recycling operation effectively reduces the concentration of this metal ion, providing important data for evaluating the specific impact of differential parameters on the recycling effect.

[0126] S603: According to the resource characteristic comparison data, evaluate the recycling efficiency and cost-benefit, optimize the process flow and parameter settings, verify the maximum efficiency and cost control of the recycling process, and the execution process for obtaining the recycling effect evaluation result is as follows;

[0127] During the process of evaluating the recycling efficiency and cost-benefit according to the resource characteristic comparison data, analyze the efficiency of the recycling operation based on the resource characteristic comparison data. Considering the change in the chemical component concentration and the change in the physical state, use statistical methods such as regression analysis to evaluate the impact of each parameter adjustment on the recycling efficiency. At the same time, the cost-benefit analysis focuses on comparing the economic indicators before and after the operation change, such as the operation cost, the resource recycling value, and the relevant environmental protection tax incentives. This analysis helps to determine which process parameter adjustments can bring the best economic return, guiding further process optimization. By comprehensively evaluating the recycling efficiency and cost-benefit, verify the economic and technical feasibility of the recycling process, provide an improvement direction for future operations, and form the recycling effect evaluation result.

[0128] Please refer to Figure 8 , an industrial sewage treatment system based on big data. The industrial sewage treatment system based on big data is used to execute the above-mentioned industrial sewage treatment method based on big data. The system includes:

[0129] The sample data collection module collects sewage samples from multiple industrial sources, measures the chemical components in the samples, and forms a resource characteristic list;

[0130] The data classification module classifies metal ions and organic solvents based on the resource characteristic list, records the classification labels, and forms resource classification data;

[0131] The recycling ranking module uses the resource classification data to screen metal ions and organic solvents with high value and high frequency, constructs an economic evaluation model, and obtains a priority recycling list;

[0132] The parameter adjustment module adjusts the temperature, pressure, and the dosage of chemical reaction agents of the recycling process according to the priority recycling list, tests the cost efficiency of multiple parameters, and generates optimized process parameters;

[0133] The real-time monitoring module applies the optimized process parameters to real-time industrial sewage treatment, monitors the resource recycling process, and analyzes the resource recycling efficiency and operation cost to obtain the recycling effect evaluation result.

[0134] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for treating industrial wastewater based on big data, characterized in that: It includes the following steps: Collect sewage samples discharged from multiple industrial sources, obtain chemical composition data, initially sort out the types and contents of recyclable resources in the samples, and form a resource feature list; Based on the resource feature list, using the target screening logic of big data, classify metal ions and organic solvents, and generate resource classification data through classification processing and label recording; Using the resource classification data, screen out metal ions and organic solvents with high frequencies and high values, construct an economic evaluation model to determine the recycling priority, and generate a priority recycling list; According to the priority recycling list, adjust the parameters of the industrial sewage recycling process, including temperature, pressure, and the dosage of chemical reaction agents, and test the efficiency and cost of multiple parameters through process simulation to obtain optimized process parameters; Implement the optimized process parameters into the real-time industrial sewage resource recycling process, collect and analyze the data during the implementation process, make real-time adjustments and verify the optimization of the resource recovery rate, and form an adjusted recycling strategy; Using the adjusted recycling strategy, conduct industrial sewage resource recycling, monitor the resource recovery efficiency and operating costs in actual operations, and obtain the recycling effect evaluation results by comparing the original resource feature list and the resource feature list after recycling; The resource feature list includes copper, iron, zinc of metal ions, toluene, benzene, ethanol of organic solvents and their corresponding concentration levels; The step of collecting sewage samples discharged from multiple industrial sources, obtaining chemical composition data, and initially sorting out the types and contents of recyclable resources in the samples to form a resource feature list is specifically as follows: Collect sewage samples at multiple industrial sources, including the steel, chemical, and textile industries, check the representativeness of the samples, use sampling bottles and GPS to record the sampling point locations, and generate a chemical composition data set; Based on the chemical composition data set, identify recyclable resource samples including metals and organic substances, and generate a resource recycling list by recording the type of each resource and the initial estimated content; Through the resource recycling list, conduct quantitative analysis on each resource, apply spectral analysis and mass balance to calculate the content of each resource, and optimize the consistency and credibility of the data to form a resource feature list.

2. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: The resource classification data includes the classification of metal ions into heavy metal types and light metal types, and the classification of organic solvents into volatile and non-volatile types. The priority recycling list includes copper and silver of metal ions with high values, and toluene and xylene of organic solvents with high demand. The optimized process parameters include the set reaction temperature range, reaction pressure level, reaction time, and the type of catalyst used. The adjusted recycling strategy includes adjusted operation steps, optimized resource recovery efficiency, and expected cost-benefit. The recycling effect evaluation results include the collected resource recovery efficiency, cost analysis, and resource quality comparison.

3. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: The step of classifying metal ions and organic solvents based on the resource feature list, using the target screening logic of big data, and generating resource classification data through classification processing and label recording is specifically as follows: Extracting data records of metal ions and organic solvents from the resource feature list, checking data integrity and consistency, and assigning classification labels to form initial screening data; Based on the initial screening data, attribute label matching technology is used to classify metal ions and organic solvents, distinguish chemical properties and uses, support the selection of recycling processes, and generate use processing data; The data is processed using the stated purpose, the recycling value and environmental priority of each metal ion and organic solvent are marked, the classification status and label information are recorded, the traceability and applicability of the data are checked, and resource classification data is generated.

4. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: Using the resource classification data, metal ions and organic solvents with high occurrence frequency and high value are screened, and an economic evaluation model is constructed to determine recycling priorities. The specific steps for generating a priority recycling list are as follows: Based on the resource classification data, metal ions and organic solvents with high occurrence frequencies are screened by statistical frequency, the occurrence frequency and data quality of multiple resources are evaluated, and high-frequency resource data are formed; Analyze the market value and recovery cost of each resource based on the high-frequency resource data, evaluate the economic feasibility of recovery by analyzing the costs and benefits, determine the economic value indicators of the resources, and generate value assessment data; By using the value assessment data and adopting a multi-objective programming method, the priority scores of resources are calculated according to factors such as economic value, environmental impact, recycling cost and market demand, and a priority recycling list is generated.

5. The industrial sewage treatment method based on big data according to claim 4, characterized in that, The formula of the multi-objective programming method is as follows: ； in, is the priority score of the resource, For resources The recycling value, To recover costs, Representative Resources The reciprocal of the recycling cost, represents the market demand for resource i, For environmental impact, Representative Resources The absolute value of the environmental impact, is the economic value weight, is the recovery cost weight, is the market demand weight, is the environmental impact weight.

6. The industrial sewage treatment method based on big data according to claim 1, characterized in that, According to the priority recycling list, the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reagents, are adjusted. The efficiency and cost of multiple parameters are tested through process simulation. The specific steps for optimizing the process parameters are as follows: Based on the priority recycling list, adjust key parameters of the recycling process, including adjusting temperature settings, pressure levels, and chemical reagent ratios, to optimize recycling efficiency and quality, and create a record of key parameter adjustments; Based on the key parameter adjustment records, the effects of the adjusted temperature, pressure, and chemical reagent dosage on the metal ion and organic solvent recovery efficiency are tested through process simulation, the effect of the changes on the overall recovery cost is evaluated, and simulation test results are generated; Based on the simulation test results, the recovery efficiency and cost-effectiveness are balanced, the process is adjusted to match the characteristics and processing requirements of differentiated types of resources, and optimized process parameters are generated.

7. The industrial sewage treatment method based on big data according to claim 1, wherein The optimized process parameters are implemented in the real-time industrial wastewater resource recovery process. By collecting and analyzing the data during the implementation process, real-time adjustments are made and the resource recovery rate is verified to be optimized. The specific steps for forming the adjusted recovery strategy are as follows: Applying the optimized process parameters to a real-time industrial wastewater resource recovery line to monitor and track changes in key parameters, including temperature, pressure, and chemical dosage, to verify that operating conditions meet the optimized standards and generate real-time monitoring data; Dynamically adjust process parameters through the real-time monitoring data, evaluate the impact of the adjustments, make adjustments based on efficiency and cost factors, and generate a recovery strategy; According to the recovery strategy, evaluate the recovery effect, optimize and adjust the industrial sewage resource recovery strategy, match the differentiated sewage types and resource characteristics, verify the economy of the recovery process, and generate the adjusted recovery strategy.

8. The industrial sewage treatment method based on big data according to claim 1, characterized in that Using the adjusted recovery strategy, conduct industrial sewage resource recovery, monitor the resource recovery efficiency and operating costs in actual operations. The steps to obtain the recovery effect evaluation result by comparing the original resource characteristics and the resource characteristics after recovery are as follows: Implement the adjusted recovery strategy to conduct industrial sewage resource recovery operations, install monitoring equipment to track the resource recovery efficiency and operating costs in real time, and form an operation monitoring data set; According to the operation monitoring data set, collect and analyze the data in the recovery process, record the change information of chemical component concentrations and physical states by comparing the changes in resource characteristics before and after the operation, evaluate the impact of differentiated parameters on the recovery effect, and generate resource characteristic comparison data; According to the resource characteristic comparison data, evaluate the recovery efficiency and cost-effectiveness, optimize the process flow and parameter settings, verify the maximum efficiency and cost control of the recovery process, and obtain the recovery effect evaluation result.

9. An industrial wastewater treatment system based on big data, characterized in that: According to the big data-based industrial sewage treatment method described in any one of claims 1-8, the system includes: The sample data collection module collects sewage samples from multiple industrial sources, measures the chemical components in the samples, and forms a resource characteristic list; The data classification module classifies metal ions and organic solvents based on the resource characteristic list, records the classification labels, and forms resource classification data; The recovery ranking module uses the resource classification data to screen metal ions and organic solvents with high value and high frequency, constructs an economic evaluation model, and obtains a priority recovery list; The parameter adjustment module adjusts the temperature, pressure, and dosage of chemical reaction agents in the recovery process according to the priority recovery list, tests the cost efficiency of multiple parameters, and generates optimized process parameters; The real-time monitoring module applies the optimized process parameters to real-time industrial sewage treatment, monitors the resource recovery process, and analyzes the resource recovery efficiency and operating costs to obtain the recovery effect evaluation result.

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