Industrial sewage treatment method and system based on big data

Abstract

The invention relates to the technical field of industrial sewage treatment, in particular to an industrial sewage treatment method and system based on big data, and the method comprises the following steps: collecting sewage samples discharged from multiple industrial sources, obtaining chemical component data, carrying out the initialization arrangement of the types and contents of recoverable resources in the samples, and forming a resource feature list. According to the method, through detailed data analysis and implementation of an optimization process, the extraction efficiency and quality of recoverable resources in industrial sewage are remarkably improved, a sewage sample is collected from an industrial source and subjected to chemical component analysis, data support is provided for a subsequent resource recovery strategy, resource classification and recovery are more accurate, and the resource recovery efficiency is improved. The recovery priority is determined by constructing an economic evaluation model, the environmental benefits and the economic benefits are effectively integrated, the intelligent degree of industrial sewage treatment is enhanced, and the resource recovery efficiency and quality are remarkably improved through a real-time adjustment mechanism.

Description

Technical Field

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

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

[0003] Among them, the industrial wastewater treatment method of big data refers to the use of big data technology to analyze and optimize the wastewater treatment process. This method guides the decision-making and operation of wastewater treatment by collecting and analyzing a large amount of data related to wastewater treatment, such as pollutant concentrations, operating status of treatment facilities, and environmental impact indicators. The application of big data can improve the efficiency and effectiveness of industrial wastewater treatment, make the treatment process more intelligent and automated, and also help reduce energy consumption and reduce operation and maintenance costs. The purpose of this technology is mainly to optimize the treatment process of industrial wastewater, ensure the achievement of environmental standards, and enhance the adaptability and responsiveness of the system.

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

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

[0006] In order to achieve the above object, the present invention adopts the following technical scheme, a method for treating industrial wastewater based on big data, comprising the following steps: S1: Collect wastewater samples discharged from multiple industrial sources, obtain chemical composition data, initialize and organize the types and contents of recyclable resources in the samples, and form a resource feature list; S2: Based on the resource feature list, the target screening logic of big data is used to classify metal ions and organic solvents, and resource classification data is generated through classification processing and label recording; S3: Using the resource classification data, screen metal ions and organic solvents with high occurrence frequency and high value, build an economic evaluation model to determine recycling priorities, and generate a priority recycling list; S4: According to the priority recycling list, adjust the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reactants, and obtain optimized process parameters by testing the efficiency and cost of multiple parameters through process simulation; S5: Implementing the optimized process parameters into the real-time industrial wastewater resource recovery process, collecting and analyzing data during the implementation process, making real-time adjustments and verifying the optimization of resource recovery rate, and forming an adjusted recovery strategy; S6: Utilize the adjusted recycling strategy to recycle industrial wastewater resources, monitor the resource recovery efficiency and operating costs in actual operations, and obtain recycling effect evaluation results by comparing the original resource characteristics with the recycled resource characteristics.

[0007] As a further scheme of the present invention, the resource feature list includes metal ions of copper, iron and zinc, organic solvents of toluene, benzene, ethanol and corresponding concentration levels, the resource classification data includes metal ions divided into heavy metals and light metals, volatile and non-volatile organic solvents, the priority recycling list includes high-value metal ions of copper and silver, high-demand organic solvents of toluene and xylene, 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 the adjusted operating steps, the optimized resource recovery efficiency and the expected cost-effectiveness, and the recycling effect evaluation results include the collected resource recovery efficiency, cost analysis and resource quality comparison.

[0008] As a further solution of the present invention, wastewater samples discharged from multiple industrial sources are collected, chemical composition data are obtained, and the types and contents of recyclable resources in the samples are initialized and sorted to form a resource feature list. Specifically, the steps are as follows: S101: Collect wastewater samples from multiple industrial sources, including steel, chemical, and textile industries, verify the representativeness of the samples, record the locations of sampling points using sampling bottles and GPS, and generate a chemical composition dataset; S102: Based on the chemical composition data set, identify recyclable resource samples including metals and organic matter, and generate a resource recycling list by recording the type and initial estimated content of each resource; S103: Through the resource recovery list, each resource is quantitatively analyzed, and the content of each resource is calculated using spectral analysis and mass balance, so as to optimize the consistency and credibility of the data and form a resource feature list.

[0009] As a further solution of the present invention, based on the resource feature list, the target screening logic of big data is used to classify metal ions and organic solvents, and the steps of generating resource classification data through classification processing and label recording are specifically as follows: S201: 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 initialization screening data; S202: Based on the initial screening data, using attribute label matching technology, classify the metal ions and organic solvents, distinguish the chemical properties and uses, support the selection of recycling processes, and generate use processing data; S203: Utilize the processing data for the purpose, mark the recycling value and environmental protection priority of each metal ion and organic solvent, record the classification and label information, check the traceability and applicability of the data, and generate resource classification data.

[0010] As a further solution of the present invention, the resource classification data is used to screen metal ions and organic solvents with high occurrence frequency and high value, and an economic evaluation model is constructed to determine the recycling priority. The steps of generating a priority recycling list are specifically as follows: S301: Based on the resource classification data, metal ions and organic solvents with high occurrence frequencies are screened by statistical frequency, the occurrence times and data quality of multiple resources are evaluated, and high-frequency resource data are formed; S302: 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 cost and benefit, determine the economic value index of the resource, and generate value assessment data; S303: Using the value assessment data, adopting a multi-objective programming method, calculating the priority score of resources according to factors such as economic value, environmental impact, recycling cost and market demand, and generating a priority recycling list.

[0011] As a further solution of the present invention, 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, Representing the market for resources The demand, 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, It is the environmental impact weight.

[0012] As a further solution of the present invention, according to the priority recycling list, the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reactants, are adjusted, and the efficiency and cost of multiple parameters are tested by process simulation to obtain the steps of optimizing the process parameters: S401: Based on the priority recycling list, adjust key parameters of the recycling process, including adjusting temperature settings, pressure levels, and ratios of chemical reactants, optimize recycling efficiency and quality, and form a key parameter adjustment record; S402: 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 change on the overall recovery cost is evaluated, and simulation test results are generated; S403: Based on the simulation test results, balance recovery efficiency and cost-effectiveness, adjust the process to match the characteristics and processing requirements of differentiated types of resources, and generate optimized process parameters.

[0013] As a further solution of the present invention, the optimized process parameters are implemented in the real-time industrial wastewater resource recovery process, and the data in the implementation process are collected and analyzed to make real-time adjustments and verify the optimization of resource recovery rate. The steps of forming the adjusted recovery strategy are specifically as follows: S501: applying the optimized process parameters to the real-time industrial wastewater resource recovery line, monitoring and tracking the changes of key parameters in real time, including temperature, pressure and chemical dosage, checking whether the operating conditions meet the optimization standards, and forming real-time monitoring data; S502: dynamically adjusting process parameters through the real-time monitoring data, evaluating the impact of the adjustment, making adjustments with reference to efficiency and cost factors, and generating a recycling strategy; S503: According to the recycling strategy, evaluate the recycling effect, optimize and adjust the industrial wastewater resource recycling strategy, match differentiated wastewater types and resource characteristics, verify the economic feasibility of the recycling process, and generate an adjusted recycling strategy.

[0014] As a further solution of the present invention, the adjusted recycling strategy is used to recover industrial wastewater resources, and the resource recovery efficiency and operating costs in actual operation are monitored. By comparing the original resource characteristics with the recycled resource characteristics, the steps of obtaining the recycling effect evaluation result are specifically as follows: S601: Implement the adjusted recycling strategy, perform industrial wastewater resource recovery operations, install monitoring equipment to track resource recovery efficiency and operating costs in real time, and form an operation monitoring data set; S602: Collect and analyze data during the recovery process according to the operation monitoring data set, compare changes in resource characteristics before and after the operation, record changes in chemical component concentration and physical state, evaluate the impact of differentiated parameters on the recovery effect, and generate resource characteristic comparison data; S603: Evaluate the recycling efficiency and cost-effectiveness based on the resource characteristic comparison data, optimize the process flow and parameter settings, verify the maximum efficiency and cost control of the recycling process, and obtain the recycling effect evaluation result.

[0015] An industrial sewage treatment system based on big data, the industrial sewage treatment system based on big data is used to execute the above industrial sewage treatment method based on big data, the system comprises: The sample data collection module collects wastewater samples from multiple industrial sources, determines the chemical composition of the samples, and forms a resource feature list; The data classification module classifies the metal ions and organic solvents based on the resource feature list, records the classification labels, and forms resource classification data; The recycling sorting module uses the resource classification data to screen high-value and high-frequency metal ions and organic solvents, build an economic evaluation model, and obtain a priority recycling list; The parameter adjustment module adjusts the temperature, pressure, and amount of chemical reactants of the recycling process according to the priority recycling 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 wastewater treatment, monitors the resource recovery process, and analyzes the resource recovery efficiency and operating costs to obtain recovery effect evaluation results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through detailed data analysis and implementation of optimization process, the extraction efficiency and quality of recyclable resources in industrial wastewater are significantly improved, wastewater samples are collected from industrial sources and chemical composition analysis is performed, providing data support for subsequent resource recovery strategies. The classification and recovery of resources are made more accurate, especially the recovery of metal ions and organic solvents, which not only improves the economic value of resources, but also reduces the energy consumption of the treatment process. By constructing an economic evaluation model to determine the recovery priority, effectively integrating environmental benefits and economic benefits, real-time data monitoring and optimization of process parameters, further ensuring the adaptability and optimization of the treatment process, and enhancing the intelligence of industrial wastewater treatment, this real-time adjustment mechanism significantly improves the efficiency and quality of resource recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 This is a detailed flow chart of S1 of the present invention; Figure 3 This is a detailed flow chart of S2 of the present invention; Figure 4 This is a detailed flow chart of S3 of the present invention; Figure 5 This is a detailed flow chart of S4 of the present invention; Figure 6 This is a detailed flow chart of S5 of the present invention; Figure 7 This is a detailed flow chart of S6 of the present invention; Figure 8 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is 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 intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are 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 cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1The present invention provides a technical solution, a method for treating industrial wastewater based on big data, comprising the following steps: S1: Collect wastewater samples discharged from multiple industrial sources, obtain chemical composition data, and initialize and organize the types and contents of recyclable resources in the samples, including metal ions and organic solvents, to form a resource feature list; S2: Based on the resource feature list, set screening parameters, use the target screening logic of big data to classify metal ions and organic solvents, and generate resource classification data through classification processing and label recording; S3: Use resource classification data to screen metal ions and organic solvents with high frequency and high value, determine recycling priorities in combination with economic evaluation models, and generate a priority recycling list; S4: According to the priority recycling list, adjust the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reactants, test the efficiency and cost of multiple parameters through process simulation, verify the optimal configuration of the process flow, and obtain the optimized process parameters; S5: Implement the optimized process parameters into the real-time industrial wastewater resource recovery process, collect and analyze the data during the implementation process, make real-time adjustments and verify the optimization of resource recovery rate, and form an adjusted recovery strategy; S6: Use the adjusted recycling strategy to recycle industrial wastewater resources, monitor the resource recovery efficiency and operating costs in actual operations, and obtain the recycling effect evaluation results by comparing the original resource characteristics with the recycled resource characteristics.

[0021] The resource characteristic list includes metal ions of copper, iron and zinc, organic solvents of toluene, benzene, ethanol and the corresponding concentration levels; the resource classification data includes metal ions divided into heavy metals and light metals, and classification of volatile and non-volatile organic solvents; the priority recycling list includes high-value metal ions of copper and silver, and high-demand organic solvents of toluene and xylene; the optimized process parameters include the set reaction temperature range, reaction pressure level, reaction time and type of catalyst used; the adjusted recycling strategy includes the adjusted operating steps, optimized resource recovery efficiency and expected cost-effectiveness; the recycling effect evaluation results include the collected resource recovery efficiency, cost analysis and resource quality comparison.

[0022] See also Figure 2 , collect wastewater samples discharged from multiple industrial sources, obtain chemical composition data, initialize and organize the types and contents of recyclable resources in the samples, and form a resource feature list in the following steps: S101: Collect wastewater samples from multiple industrial sources, including steel, chemical and textile industries, check the representativeness of the samples, use sampling bottles and GPS to record the locations of sampling points, and generate a chemical composition dataset. The execution process is as follows; Wastewater samples are collected from multiple industrial sources. When collecting sewage samples, appropriate sampling points are selected according to the emission characteristics of different industrial sources. The selection of sampling points depends on the preliminary investigation of sewage outlets in industrial areas and historical data analysis to ensure that the samples can represent the sewage emission characteristics of each industrial area. Standardized sampling bottles are used to sample sewage and the GPS location of each sample is marked for subsequent traceability and analysis. The specific time and environmental conditions of each sample collection, such as temperature and weather conditions, need to be recorded during the sampling process. These data are of great reference value for subsequent chemical composition analysis. This information is entered into the database to form a chemical composition data set.

[0023] S102: Based on the chemical composition data set, identify the recyclable resource samples including metals and organic matter, and generate the resource recycling list by recording the type and initial estimated content of each resource. The execution process is as follows; By recording the type and initial estimated content of each resource, according to the formula , calculate the estimated total content of each resource. Representative resources The total content of Representative samples Chinese Resources The concentration of Representative samples The volume of Represents the total volume of all samples. Formula explanation and formula calculation derivation process: Set in a wastewater sample of a chemical plant, resources For copper, estimate the concentration in different samples They are 2mg / L, 3mg / L, and 1.5mg / L, corresponding to the volume of the sample The total volume is 1L, 2L, 1.5L 4.5L. Computing resources The process of total content is as follows The results showed that the average concentration of copper in the tested samples was 2.28 mg / L, providing important data support for the generation of resource recycling lists.

[0024] S103: Through the resource recovery list, quantitative analysis is performed on each resource, and the content of each resource is calculated using spectral analysis and mass balance to optimize the consistency and credibility of the data. The execution process of forming a resource feature list is as follows; Through the resource recovery list, each resource is quantitatively analyzed, and the metals and organic matter in the sample are qualitatively and quantitatively analyzed through spectral analysis. During the spectral analysis, the known spectral data is compared with the absorbance of each component in the sample to obtain the content of each component. For unknown components, advanced spectral techniques such as mass spectrometry are used for identification. Quantitative analysis relies on the standard curve method to estimate the content in the sample by comparing the sample absorbance with the absorbance of a standard solution of known concentration. The calculation of mass balance involves multiplying the content obtained by spectral analysis by the total volume to obtain the total mass of each resource. This process needs to ensure the accuracy of the calculation and the consistency of the data to form a resource characteristic list.

[0025] See also Figure 3 Based on the resource feature list, the target screening logic of big data is used to classify metal ions and organic solvents. The specific steps to generate resource classification data through classification processing and label recording are as follows: S201: 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 the execution process of initializing screening data is as follows; Data records of metal ions and organic solvents are extracted from the resource feature list. During the extraction process, the data in the resource feature list are verified to ensure that the recorded data of metal ions and organic solvents are not only complete but also meet 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 subsequently verified or supplemented. After the completion stage, appropriate classification labels are assigned 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 labels makes subsequent data screening more systematic, including complete chemical composition information and applicable classification labels, which provide a basis for further data processing and resource recovery decisions, and form initial screening data.

[0026] S202: Based on the initial screening data, the attribute label matching technology is used to classify the metal ions and organic solvents, distinguish the chemical properties and uses, support the selection of the recycling process, and generate the use processing data. The execution process is as follows; Based on the initial screening data, attribute label matching technology is used. Attribute label matching technology plays a key role. Through advanced classification algorithms, such as support vector machines or decision trees, the attribute data of each chemical component is analyzed, and metal ions and organic solvents are effectively grouped according to chemical properties and actual application needs. Each chemical component is given a clear label, which reflects the role and priority in various recycling processes. The classification process ensures the logical consistency and applicability of the data, including not only classification information, but also refines the industrial application of each substance, providing strong data support for the formulation of more accurate resource recovery strategies and generating usage processing data.

[0027] S203: Using the usage processing data, marking the recycling value and environmental protection priority of each metal ion and organic solvent, recording the classification and label information, checking the traceability and applicability of the data, and generating the resource classification data execution process is as follows; Mark the recovery value and environmental priority of each metal ion and organic solvent according to the formula , calculate the comprehensive evaluation score of each substance. Represents the comprehensive evaluation score, Represents the recycling value, Represents environmental protection priority, and is the weight coefficient. Detailed explanation of the formula and the process of formula calculation: Setting the recycling value of a certain metal ion 80, environmental protection priority is 95, the weight coefficient and According to the actual situation, they are set to 0.6 and 0.4 respectively. Calculate the comprehensive evaluation score of metal ions The process is as follows: The results show that the recovery of metal ions not only has high economic value, but also has a high priority in terms of environmental protection, and therefore should be the focus of recovery work.

[0028] See also Figure 4 , using resource classification data, screen metal ions and organic solvents with high occurrence frequency and high value, build an economic evaluation model to determine recycling priorities, and generate a priority recycling list in the following steps: S301: Based on the resource classification data, metal ions and organic solvents with high occurrence frequencies are screened by statistical frequency, and the occurrence frequency and data quality of multiple resources are evaluated to form high-frequency resource data. The execution process is as follows; Evaluate the number of occurrences and data quality of multiple resources according to the formula , the frequency of occurrence of computing resources. Represents the total number of occurrences, Represents the frequency of occurrence of a single resource in the dataset, is the number of resource types. Formula explanation and formula calculation derivation process: There are three types of resources, and the frequency of occurrence is 10, 15, and 5 times respectively. Calculate the total number of occurrences of these resources The process is as follows: The result shows that the cumulative number of occurrences of these resources in the investigated data set is 30, which can determine the high frequency of occurrence of these resources and conduct subsequent resource recycling priority division.

[0029] S302: Analyze the market value and recovery cost of each resource based on high-frequency resource data, evaluate the economic feasibility of recovery by analyzing costs and benefits, determine the economic value index of the resource, and generate the execution process of value assessment data as follows; Based on high-frequency resource data, the market value and recycling cost of each resource are analyzed. The economic value is evaluated based on market demand and supply, and an expected market value is estimated for each resource. The analysis of recycling costs takes into account the costs of logistics, processing, and reuse in the recycling process. Through detailed calculations, the recycling cost and market value of each resource are compared to determine the net economic value of each resource. The net value reflects the direct economic benefits after recycling. Combined with the frequency of occurrence and net economic value of the resource, the economic feasibility of the resource is comprehensively evaluated. These data not only guide the priority sorting of recycling work, but also help policy makers optimize resource allocation, ensure the maximization of the economic benefits of resource recycling work, and form value assessment data.

[0030] S303: Using the value assessment data and adopting the multi-objective programming method, the priority score of the resources is calculated according to the factors of economic value, environmental impact, recycling cost and market demand, and the execution process of generating the priority recycling list is as follows; The formula of 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, Representing the market for resources The demand, 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, It is the environmental impact weight.

[0031] Detailed explanation of the formula and the process of formula calculation and derivation: This formula is a calculation method used to determine the priority of resource recovery in a multi-objective optimization algorithm. Various parameters must be quantified and specific values ​​must be obtained through data monitoring, collection or calculation methods to ensure accuracy and a realistic basis.

[0032] parameter :resource The recycling value of the resource is determined through market analysis and historical transaction data analysis. For aluminum, according to market data, the average value of recycled aluminum is $1,700 per ton.

[0033] parameter :resource Recycling costs. For example, the recycling cost of aluminum is $500 per ton, as reported by recycling facilities and industry standards.

[0034] parameter :Market vs. Resources According to industry demand reports and market research results, for example, the current market demand index for aluminum is 0.8.

[0035] parameter :resource According to environmental assessment reports and environmental impact studies, the environmental impact score of aluminum is -0.3, which means it has a slight negative impact on the environment.

[0036] Weight , , and :These parameters are weight coefficients, which are set according to the policy, economic and environmental strategies of resource recovery. , , , These weights reflect the influence of different indicators on the total score, among which , Higher than , The importance of showing market value and demand is high.

[0037] Using these parameters and weights, the calculation is performed as follows: , , ,

[0038] , , ,

[0039] Calculate recovery value and cost impact:

[0040]

[0041] Market demand and environmental impact:

[0042]

[0043] Combine the above results into the total score formula:

[0044] The results show 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 that should be recycled first. This score reflects the comprehensive consideration of aluminum's economic value, cost-effectiveness, market demand and environmental impact, and guides the priority arrangement of resource recycling.

[0045] See also Figure 5 According to the priority recycling list, the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reactants, 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: 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 reactants, optimize the recycling efficiency and quality, and form the execution process of the key parameter adjustment record as follows; 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 reactants. During the process, the temperature, pressure and the ratio of chemical reactants are adjusted according to the chemical characteristics of the resources and the recycling technical requirements. The specific adjustment steps include preliminary setting of parameters, evaluating the initial effect of the adjustment through small-scale tests, making minor adjustments to the parameters based on the test results, and evaluating the effect again until the optimization is achieved. It includes not only the specific values ​​of the parameters, but also the specific impact on the recycling efficiency and quality. It is an important basis for subsequent process optimization and cost control. Through systematic parameter adjustment, the recovery rate of metal ions and organic solvents can be significantly improved, while ensuring that the quality of the recycling process meets the predetermined standards, forming a record of key parameter adjustments.

[0046] S402: Through 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, and the effect of the change on the overall recovery cost is evaluated. The execution process of generating the simulation test results is as follows; Through the key parameter adjustment records, process simulation testing is a key technical step to simulate the specific impact of adjusted parameters on recovery efficiency. By changing the temperature, pressure and amount of chemical reactants, the impact of each variable adjustment on recovery efficiency is observed. 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 costs to ensure that the optimized recovery process is economically feasible. The simulated data provides precise guidance to help develop the most economical recovery plan, record the economic and efficiency evaluation of various parameter adjustments, provide a scientific basis for process optimization, and generate simulation test results.

[0047] S403: Based on the simulation test results, the recycling efficiency and cost-effectiveness are balanced, the process is adjusted to match the characteristics and processing requirements of the differentiated type resources, and the execution process of generating the optimized process parameters is as follows; Balancing recycling efficiency and cost-effectiveness, according to the formula , calculate the process benefit ratio after optimization.

[0048] In the formula, Represents the process benefit ratio, Represents the recycling efficiency, Represents the amount of recycling cost savings, Represents the total cost. Detailed explanation of the formula and the process of formula calculation: Set the recycling efficiency after optimization Increased to 90%, the original recycling efficiency was 75%, and the recycling cost savings For 2000 units, the total cost 10,000 units. Calculate the process benefit ratio after optimization The process is as follows: The results show 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 enterprises achieve environmental protection while maintaining economic benefits.

[0049] See also Figure 6 , the optimized process parameters are implemented in the real-time industrial wastewater resource recovery process, and the data collected and analyzed during the implementation process are adjusted in real time and the resource recovery rate is verified to be optimized. The specific steps for forming the adjusted recovery strategy are as follows: S501: Apply the optimized process parameters to the real-time industrial wastewater resource recovery line, 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, and form the execution process of real-time monitoring data as follows; Apply the optimized process parameters to the real-time industrial wastewater resource recovery line to ensure that all monitoring equipment operates according to the latest process parameter settings. The monitoring system needs to capture every change in key parameters in real time and compare it with the set optimization standards. Any deviation will trigger the alarm system to prompt the operator to check or adjust. Real-time data collection and analysis are essential to verifying the effectiveness of process parameters. The system will automatically record and analyze data to ensure that operating conditions continue to meet the requirements of process optimization. It not only provides instant feedback on operations, but also forms basic data for subsequent review and further optimization, forming real-time monitoring data.

[0050] S502: By 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 the execution process of the recycling strategy as follows; Dynamically adjust the process parameters according to the formula , calculate the adjustment factor. In the formula, represents the adjustment factor, Represents the pressure change, represents the original pressure, represents the temperature change, represents the original temperature, and To adjust the weight. Detailed explanation of the formula and the derivation process of the formula calculation: Set the original pressure is 100 kPa, the current pressure increases to 110 kPa, the original temperature is 300K, the current temperature drops to 290K, the weight and 0.5 and 0.5 respectively. Calculate the adjustment factor The process is as follows: .

[0051] The results indicate that, based on real-time monitoring data, slight process adjustments may be required to maintain operational efficiency and cost control.

[0052] S503: According to the recycling strategy, evaluate the recycling effect, optimize and adjust the industrial wastewater resource recycling strategy, match the differentiated wastewater types and resource characteristics, verify the economic feasibility of the recycling process, and generate the execution process of the adjusted recycling strategy as follows; According to the recycling strategy, in the process of evaluating the recycling effect, attention is paid to three main indicators: recovery rate, resource quality and economic benefit. According to the recycling effect, the adjustment strategy includes changing the chemical dosage, adjusting the treatment temperature or modifying the pressure setting to adapt to the specific needs of different types of wastewater. The process of optimizing the process is iterative and needs to be continuously adjusted based on real-time data and periodic evaluation. The cost-effectiveness ratio of the recycling strategy is confirmed through economic analysis to ensure that the strategy is not only technically feasible but also commercially sustainable. The adjusted strategy records the matching of each wastewater type and resource characteristics. These strategies will be reapplied to the production line after updating to ensure the continuous optimization of the recycling process and maximize economic benefits, and generate adjusted recycling strategies.

[0053] See also Figure 7 , using the adjusted recycling strategy, to recycle industrial wastewater resources, monitor the resource recovery efficiency and operating costs in actual operation, and obtain the recycling effect evaluation results by comparing the original resource characteristics with the recycled resource characteristics. The specific steps are as follows: S601: Implement the adjusted recycling strategy, conduct industrial wastewater resource recycling operations, install monitoring equipment to track resource recycling efficiency and operating costs in real time, and form an operation monitoring data set. The execution process is as follows; The implementation of the adjusted recycling strategy includes the comprehensive deployment of sensors and monitoring systems to monitor the key operating parameters of the recycling line such as temperature, pressure and chemical dosage in real time. 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 to adjust the operating parameters immediately to adapt to process requirements and environmental changes, but also predicts potential equipment failures or efficiency declines, performs maintenance in advance, ensures the continuity and stability of the recycling process, and provides a scientific basis for subsequent strategy adjustments and economic evaluations, thereby maximizing cost-effectiveness and continuously optimizing resource recovery efficiency, and obtaining an operation monitoring data set.

[0054] S602: According to the operation monitoring data set, the data in the recycling process is collected and analyzed, and by comparing the changes in resource characteristics before and after the operation, the change information of chemical composition concentration and physical state is recorded, and the influence of the differentiation parameters on the recycling effect is evaluated. The execution process of generating resource characteristic comparison data is as follows; By comparing the changes in resource characteristics before and after the operation, according to the formula , calculate the change of chemical component concentration. Represents the change in chemical component concentration, and Represents the concentration of chemical components before and after operation. Formula explanation and formula calculation derivation process: Set the concentration of a certain metal ion before operation The concentration after operation is 50mg / L Reduced to 30mg / L. Calculate the change in chemical composition concentration The process is as follows: The results showed that the recovery operation effectively reduced the concentration of the metal ion, providing important data for evaluating the specific effects of the differentiation parameters on the recovery effect.

[0055] S603: According to resource characteristic comparison data, evaluate recycling efficiency and cost-effectiveness, optimize process flow and parameter settings, verify the maximum efficiency and cost control of the recycling process, and obtain the execution process of the recycling effect evaluation result as follows; In the process of evaluating the recovery efficiency and cost-effectiveness based on the resource characteristic comparison data, the efficiency of the recovery operation is analyzed based on the resource characteristic comparison data. Taking into account the changes in the concentration of chemical components and the changes in the physical state, statistical methods such as regression analysis are used to evaluate the impact of various parameter adjustments on the recovery efficiency. At the same time, the cost-effectiveness analysis focuses on comparing the economic indicators before and after the operation changes, such as operating costs, resource recovery value and related environmental protection tax incentives. This analysis helps determine which process parameter adjustments can bring the best economic returns and guide further process optimization. By comprehensively evaluating the recovery efficiency and cost-effectiveness, the economic and technical feasibility of the recovery process is verified, which provides improvement directions for future operations and forms a recovery effect evaluation result.

[0056] See also 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 industrial sewage treatment method based on big data, the system includes: The sample data collection module collects wastewater samples from multiple industrial sources, determines the chemical composition of the samples, and forms a resource feature list; The data classification module classifies metal ions and organic solvents based on the resource feature list, records the classification labels, and forms resource classification data; The recycling ranking module uses resource classification data to screen high-value and high-frequency metal ions and organic solvents, build an economic evaluation model, and obtain a priority recycling list; The parameter adjustment module adjusts the temperature, pressure, and amount of chemical reagents used in the recycling process according to the priority recycling list, tests the cost efficiency of multiple parameters, and generates optimized process parameters; The real-time monitoring module will optimize the process parameters and apply them to real-time industrial wastewater treatment, monitor the resource recovery process, and analyze the resource recovery efficiency and operating costs to obtain the recovery effect evaluation results.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls 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: The following steps are involved: Collect wastewater samples discharged from multiple industrial sources, obtain chemical composition data, initialize and organize the types and contents of recyclable resources in the samples, and form a resource feature list; Based on the resource feature list, the metal ions and organic solvents are classified using the target screening logic of big data, and resource classification data is generated through classification processing and label recording; Using the resource classification data, screen metal ions and organic solvents with high occurrence frequency and high value, build an economic evaluation model to determine recycling priorities, and generate a priority recycling list; According to the priority recycling list, adjust the parameters of the industrial wastewater recycling process, including temperature, pressure and the amount of chemical reactants, and obtain the optimized process parameters through process simulation testing of the efficiency and cost of multiple parameters; Implementing the optimized process parameters into the real-time industrial wastewater resource recovery process, collecting and analyzing data from the implementation process, making real-time adjustments and verifying the optimization of resource recovery rate, and forming an adjusted recovery strategy; The adjusted recycling strategy is used to recycle industrial wastewater resources, monitor the resource recovery efficiency and operating costs in actual operations, and obtain recycling effect evaluation results by comparing the original resource characteristics with the recycled resource characteristics.

2. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: The resource feature list includes metal ions of copper, iron and zinc, organic solvents of toluene, benzene and ethanol and corresponding concentration levels, the resource classification data includes metal ions divided into heavy metals and light metals, and classification of volatile and non-volatile organic solvents, the priority recycling list includes high-value metal ions of copper and silver, and high-demand organic solvents of toluene and xylene, the optimized process parameters include the set reaction temperature range, reaction pressure level, reaction time and type of catalyst used, the adjusted recycling strategy includes adjusted operating steps, optimized resource recovery efficiency and expected cost-effectiveness, and the recycling effect evaluation results include 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: Collect wastewater samples discharged from multiple industrial sources, obtain chemical composition data, initialize and organize the types and contents of recyclable resources in the samples, and form a resource feature list. The specific steps are as follows: Collect wastewater samples from multiple industrial sources, including the steel, chemical and textile industries, verify the representativeness of the samples, record the locations of the sampling points using sampling bottles and GPS, and generate a chemical composition dataset; Based on the chemical composition data set, identify samples of recyclable resources including metals and organics, and generate a resource recycling inventory by recording the type and initial estimated content of each resource; Through the resource recovery list, each resource is quantitatively analyzed, and the content of each resource is calculated using spectral analysis and mass balance to optimize the consistency and credibility of the data and form a resource characteristic list.

4. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: Based on the resource feature list, the metal ions and organic solvents are classified using the target screening logic of big data, and the steps of generating resource classification data through classification processing and label recording are 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, the metal ions and organic solvents are classified by using attribute label matching technology, the chemical properties and uses are distinguished, the selection of recycling processes is supported, and use processing data is generated; The data is processed using the purposes described, the recycling value and environmental priority of each metal ion and organic solvent are marked, the classification and label information are recorded, the traceability and applicability of the data are checked, and resource classification data is generated.

5. 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 the recycling priority. 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 cost and benefit, determine the economic value index of the resource, and generate value assessment data; Utilizing the value assessment data and adopting a multi-objective programming method, the priority scores of resources are calculated based on factors such as economic value, environmental impact, recycling cost and market demand, and a priority recycling list is generated.

6. The industrial wastewater treatment method based on big data according to claim 5 is 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, Representing the market for resources The demand, 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, It is the environmental impact weight.

7. The industrial wastewater 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 reactants, are adjusted, and the efficiency and cost of multiple parameters are tested through process simulation to obtain the steps of optimizing the process parameters: based on the priority recycling list, the key parameters of the recycling process are adjusted, including adjusting the temperature setting, pressure level and the ratio of chemical reactants, optimizing the recycling efficiency and quality, and forming a key parameter adjustment record; Through 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 change 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.

8. The method for treating industrial wastewater based on big data according to claim 1, characterized in that: 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: Apply the optimized process parameters to the real-time industrial wastewater 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; Through the real-time monitoring data, dynamically adjust the process parameters, evaluate the impact of the adjustment, make adjustments with reference to efficiency and cost factors, and generate a recycling strategy; According to the recycling strategy, the recycling effect is evaluated, the industrial wastewater resource recovery strategy is optimized and adjusted, the differentiated wastewater types and resource characteristics are matched, the economic feasibility of the recycling process is verified, and the adjusted recycling strategy is generated.

9. The industrial wastewater treatment method based on big data according to claim 1, characterized in that: The steps of using the adjusted recycling strategy to recycle industrial wastewater resources, monitoring the resource recovery efficiency and operating costs in actual operation, and obtaining the recycling effect evaluation results by comparing the original resource characteristics with the recycled resource characteristics are as follows: Implement the adjusted recycling strategy, perform industrial wastewater resource recovery operations, install monitoring equipment to track resource recovery efficiency and operating costs in real time, and form an operation monitoring data set; According to the operation monitoring data set, data during the recovery process is collected and analyzed, and by comparing the changes in resource characteristics before and after the operation, information on changes in chemical composition concentration and physical state is recorded, the impact of differentiated parameters on the recovery effect is evaluated, and resource characteristic comparison data is generated; Based on the resource characteristics comparison data, the recovery efficiency and cost-effectiveness are evaluated, the process flow and parameter settings are optimized, the maximum efficiency and cost control of the recovery process are verified, and the recovery effect evaluation results are obtained.

10. An industrial wastewater treatment system based on big data, characterized in that: According to any one of claims 1 to 9, the industrial wastewater treatment method based on big data comprises: The sample data collection module collects wastewater samples from multiple industrial sources, determines the chemical composition of the samples, and forms a resource feature list; The data classification module classifies the metal ions and organic solvents based on the resource feature list, records the classification labels, and forms resource classification data; The recycling sorting module uses the resource classification data to screen high-value and high-frequency metal ions and organic solvents, build an economic evaluation model, and obtain a priority recycling list; The parameter adjustment module adjusts the temperature, pressure, and amount of chemical reactants of the recycling process according to the priority recycling 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 wastewater treatment, monitors the resource recovery process, and analyzes the resource recovery efficiency and operating costs to obtain recovery effect evaluation results.

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